Development and Validation of a Survey Instrument for Sdgs Understanding in Southeast US Residents

Development and Validation of a Survey Instrument for Sdgs Understanding in Southeast US Residents | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a Survey Instrument for Sdgs Understanding in Southeast US Residents Dr. Megha Shrestha, Dr. Karen McNeal, Dr. Chandana Mitra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6908656/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study introduces a new survey instrument, the Sustainability Survey (SS), which assesses residents' awareness/familiarity, concern/urgency, perception, behavior, involvement, and intended behavior towards sustainability practices in their neighborhood based on United Nations (UN) Sustainability Development Goals (SDGs). The SS was developed by drawing from previous instruments related to sustainability and SDGs, and tested with 936 responses, leading to the refinement of a 100-item pilot survey to a new instrument with 85 items and 7 subscales. The survey provides a comprehensive measurement tool for researchers and policymakers to better understand the attitudes and behaviors of residents towards sustainability policies and can inform the development and implementation of effective education campaigns and marketing strategies to promote sustainable practices and achieve the UN SDGs. survey sustainability UN SDGs EFA CFA Figures Figure 1 Figure 2 Introduction Sustainability as a concept was first introduced in forestry where its meaning was associated with harvesting (Wiersum, 1995 ). The Brundtland Report in 1987 popularized sustainability as a policy idea, which has since been prominent in policy-oriented research to determine what public policies should accomplish (Kuhlman & Farrington, 2010 ). Today, sustainability encompasses three pillars —social equity, economic viability, and environmental protection—to promote development while ensuring that resources are preserved for future generations (Kuhlman & Farrington, 2010 ). There is a widespread debate over the scope of sustainability. Many people believe that pursuing sustainability entails prioritizing natural resources over human progress, even though sustainability is inextricably linked to technological and human progress, with nature conservation playing a significant role in both (Kuhlman & Farrington, 2010 ). Therefore, achieving sustainability is indissolubly linked with human population and resources used. Population has been growing exponentially from 1 billion in 1800 to 7.7 billion in 2019 (Roser, Ritchie, & Ortiz-Ospina, 2013 ). Globally, urbanization is characterized by a rise in population density and accompanying infrastructural development (United Nations, 2019 ). According to the United Nations (UN), more people began to live in cities than in rural regions in 2007, with cities accounting for 55 percent of the world population in 2018 (North Carolina State University, n.d.). By 2050, it is expected that more than two-thirds of the world's population will be living in cities, with 64.1% and 85.9% of population living in the developing and developed worlds, respectively (United Nations 2019 ). Various studies have revealed that urbanization is having a negative impact on the environment around the world, resulting in difficulties such as increasing land insecurity, deteriorating water quality, excessive air pollution, excessive noise pollution, increasing garbage disposal issues, and so on (Abd Rahim, Tahir, Musta, & Roslee, 2018 ; Basak, 2018 ; Fiorini, Zullo, Marucci, & Romano, 2019 ). On the other hand, urban areas can be thriving, long-term communities. The preservation of a quality environment, the use of renewable and efficient energy resources, the maintenance of a healthy population with access to health services, and the existence of economic vigor, social equity, and engaged citizens are all characteristics of a sustainable urban region (University of Michigan, n.d.) and needed for sustainable development of a city. There exist multiple studies tackling the environment, energy, and economy aspects of such development (Arshad & Routray, 2018 ; Chan, 2020 ; Danielis, Rotaris, & Monte, 2018 ; Kusago, 2011 ; Mudau et al., 2020 ) but not enough studies for promoting engagement among residents of the city. Sustainable development, as a concept has been discussed for decades. The concept of sustainable development received its first major international recognition in 1972 at the UN Conference on the Human Environment held in Stockholm. Since then, several conferences on sustainable development have been held around sustainable development pathways. In 1987, the United Nations Brundtland Commission defined sustainability as “meeting the needs of the present without compromising the ability of future generations to meet their own needs.”(United Nations, 2019 ). Since then, the United Nations has worked in different areas of sustainability, starting from education to poverty to climate action and many more. Sustainable Development Goals (SDGs) were adopted at the United Nations Conference on Sustainable Development in Rio de Janeiro in 2012 to achieve the 2030 Agenda for Sustainable Development. SDGs came into being as the UN saw the urgency to produce a set of universal goals that meet the urgent environmental, political, and economical needs of the world. In 2015, 17 goals were established, each tackling unique yet interconnected areas of society to achieve better and sustainable future by 2030. The 17 SDG goals covered are shown in Fig. 1 . These goals are more global than local in character, but they serve as a foundation for gathering data and comparing residents' perceptions of urban sustainability and sustainable development in this project. This statement from the UN news (2018), “Understanding the key trends in urbanization likely to unfold over the coming years is crucial to the implementation of the 2030 Agenda for Sustainable Development, including efforts to forge a new framework of urban development,” demonstrates the value of understanding urban sustainability to achieve the goals and improve human lives in general. There are multiple initiatives taken all over the world towards creating a sustainable and better future. Hamburg (Germany), Magdeburg (Germany), St. Petersburg (US), and Milwaukee (US) were among the first cities chosen to assess the challenges and opportunities associated with their existing sustainability standards, as well as the possibility of incorporating SDGs into the broader sustainability planning process (Krellenberg, Bergsträßer, Bykova, Kress, & Tyndall, 2019 ). Also, the Comprehensive Assessment System for Built Environment Efficiency (CASBEE) has been effectively implementing and assessing sustainable measures at the local level by evaluating quality and environmental load perspectives (Kawakubo et al. 2018). Koch and Krellenberg ( 2018 ) focus on analyzing the contextualization of global urban goals at a national level. Their findings reveal that only a small number of the original SDG 11: Sustainable Cities and Communities targets and indicators set by the United Nations are implemented in the German cities. Therefore, considerable revisions were made in line with Germany's key sustainability challenges. The results reveal that SDG 11 contextualization and sustainable urban development are still happening in Germany and further amendments and obligations must be made (Koch & Krellenberg, 2018 ). This shows the importance of understanding sustainability in a local context. According to the United Nations Department of Economic and Social Affairs (UN DESA) 2018 Revision of World Urbanization Prospects, North America is the world's most urbanized region, with 82% of its population living in cities in 2018 (DESA, 2018 ). The US has also taken several steps in the direction of long-term solutions at various levels - national, state, county, and city. Living Cities Report in 2009 found that over 75% of the 40 largest U.S. cities surveyed have plans for reducing greenhouse gasses in the coming years (Living Cities, 2009 ). The Environment Protection Agency (EPA) also offers many clean energy programs, information, training opportunities, grants, resources, and tools to assist local governments. In 2009, the U.S. Department of Housing and Urban Development, Department of Transportation, and Environmental Protection Agency created the Partnership for Sustainable Communities to promote sustainable communities through better access to affordable housing, more transportation options, and lower transportation costs. The San Jose-Sunnyvale-Santa Clara metro region in California placed first on the SDG Index of the city ranks based on 49 indicators across 16 of the 17 SDGs (Sustainable Development Solutions Network, 2017 ). As per the report from the Center for Sustainable Systems of the University of Michigan, by August 2019, 1,060 mayors have signed on to the 2005 U.S. Mayors Climate Protection Agreement, committing to reduce carbon emissions below 1990 levels, in line with the Kyoto Protocol in USA (University of Michigan, n.d.). There are national and international associations promoting collaboration and cooperation between local, regional, and national governments. One such international organization which is very active in this field is the International Council for Local Environmental Initiatives (ICLEI), whose focus is developing locally designed initiatives to achieve sustainability goals. In USA, ‘Smart Growth America’ serves as a coalition working to improve the planning and building of towns, cities, and metro areas. The ‘Solar Outreach Partnership’ is a component of the U.S. Department of Energy’s SunShot Initiative to make solar energy cost-competitive with other energy technologies. The Solar Outreach Partnership provides local governments with guidance on community-wide deployment of solar power. Local governments all over the USA have launched several projects aimed at achieving the common goal of sustainable development, and they need citizens to both understand and support sustainability for initiatives to be effective. However, there have not been enough studies to understand citizen’s participation in such project and policy development. This paper outlines the development and validation of a survey instrument aimed at gathering data regarding several aspects of a population’s awareness/familiarity, concern/urgency, perception, involvement, behavior, and intended behavior about sustainability practices-based on UN SDGs, and how they are related to each other and other demographics elements as an initial piece of a larger research project. The purpose of the larger project is to provide a framework that city governments in small and medium-sized cities (SMSC) may use to determine which policies their residents support and why. The urgent concern, however, is to develop a methodology on how to adapt the variety of existing sustainability and SDGs survey instruments, modify them, and/or design and evaluate new instrument scales to satisfy these requirements. This study also aims to use statistical techniques such as factor analysis to understand the latent structure of sustainability responses. Factor analysis is a statistical technique used to identify underlying dimensions, or factors, that explain the variance in a set of observed variables (Bryant & Yarnold, 1995 ) and latent structure models are models used in survey analysis to identify unobserved or latent variables and the relationships between them (Asparouhov & Muthén, 2009 ). Therefore, the purpose of our research is (1) collect Southeast US (SEUS) residents' awareness/familiarity, concern/urgency, perception, involvement, behavior, and intended behavior (latent variables) about sustainability and the SDGs (2) understand which latent variables load with the items provided; and (3) provide a reliable and validated survey to collect such information that can guide present and future development plans and policies. Survey Instruments as a measure of understanding SDGs MacDonald et al. ( 2018 ) explores the importance of involving stakeholders in the solutions of different sustainability challenges. The findings revealed that sustainable community plans are still being developed and implemented in a variety of communities around the world, with local organizations serving as implementation partners, acting as an incentive for local government investments in community sustainability, and leading to a sustainable future (MacDonald, Clarke, Huang, Roseland, & Seitanidi, 2018 ). Therefore, to enhance such partnership it is crucial to understand and involve residents of a city in decision making. A survey instrument is a useful metric to assess such understanding and involvement across numerous fields. Clark and Libarkin ( 2011 ) designed, implemented, and scored a valid and reliable mixed-methods survey instrument to gather conceptions of plate tectonics and use the results to better communicate various information related to it (Clark & Libarkin, 2011 ). Similarly, researchers used a survey to differentiate the possible awareness levels between Alabama and Hawaii college students about sustainability, though there was not a significant difference between awareness between the college students. Hawaiian students took more action and were more likely to take further actions to make their college sustainable (Emanuel & Adams, 2011 ). Walker and McNeal (2012) developed and validated a survey instrument for assessing climate change knowledge and views using factor analysis and classical test theory (Walker & McNeal, 2013 ). Undergraduate business students’ attitudes, beliefs, and perceptions about sustainability were evaluated pre and post curriculum change using a semi-structured questionnaire applied across two campuses of James Cook University, Australia (Eagle, Low, Case, & Vandommele, 2015 ). Awareness and knowledge of the SDGs were examined using a cross-sectional survey in Osun State University, Southwestern Nigeria, chosen via multi-stage sampling. Researchers discovered a low level of awareness of and attitudes toward the SDGs, which has serious negative implications for SDG attainment (Omisore, Babarinde, Bakare, & Asekun-Olarinmoye, 2017 ). Libarkin et al. ( 2018 ) also designed and examined a climate change concept inventory with high validity and reliability (Libarkin, Gold, Harris, McNeal, & Bowles, 2018 ). Abiola, Joseph, and Rachael ( 2018 ) designed a survey instrument to assess the general perception of librarians in Osun State in the attainability of the sustainable development goals. They found optimistic responses about achieving gender equality by empowering all women and girls. Additionally, they observed a widespread belief that SDGs can protect, restore, and promote the sustainable use of terrestrial ecosystems, manage forests sustainably, and combat desertification, and that library and information services are relevant to the attainment of the sustainable development goals in Nigeria (Abiola, Joseph, & Rachael, 2018 ). Melles ( 2019 ) used a survey to investigate the knowledge and attitudes of postgraduate United Kingdom (UK) students enrolled in one-year taught sustainability degrees on the multidimensional issues of sustainable development. The study discovered that this cohort was able to recognize and respond to many problems of strong and weak sustainable development issues, rather than demonstrating previously documented knowledge gaps. The survey's findings and qualitative remarks, however, show that students are opposed to major interventions in social, political, and economic life (Melles, 2019 ). Another survey was used to determine the awareness level of University of Malaya students towards SDGs based on knowledge, attitude, and practice in Indonesia. They found a strong correlation between attitude and practice towards SDGs in university students (Afroz & Ilham, 2020 ). Kazakova et al. ( 2020 ) undertook a sociological study of university students, primarily from southwestern Siberia, to assess their grasp of the Sustainable Development Goals and global concerns confronting humanity. They surveyed respondents to determine which world problems should be addressed first: ecological, social, or economic. Respondents chose differently for ecological, social, or economic problems as the most pressing at global, national, and regional scale as their priority - more concerned about ecological problems at global level and economic and social problems at national and regional levels (Kazakova et al., 2020 ). Smaniotto et al. ( 2020 ) employed a Likert scale-based online questionnaire with 70 items to examine first-year students' awareness, knowledge, and attitudes about SDGs and sustainability at nine Italian universities (Smaniotto et al., 2020 ). Most of the survey instruments created focuses on collecting already established policy perspectives about sustainability and sustainable development goals (Aljerf & Choukaife, 2016 ; Gadema & Oglethorpe, 2011 ; Guan, He, He, Cheng, & Qu, 2020 ; Yamane & Kaneko, 2021 ) in higher education and environmental studies but there is little work that specifically use survey instrument to collect residents’ awareness/familiarity, concern/urgency, perception, involvement, behavior, and intended behavior related to sustainability and sustainable goals to inform future plans and policies. In the development of the survey instrument presented here, named as sustainability survey (SS), the above-mentioned surveys with respect to the project’s goal were considered to develop a customized instrument that combines various aspects of the previously noted instruments and newly created items to understand sustainability practices in the SEUS. These following sections outline the stages of development of the SS, along with the reliability and validity descriptions of this new instrument. Classical Test Theory Classical test theory (CTT) employs a conventional quantitative method to assess the reliability and validity of a scale based on its items (Cappelleri, Jason Lundy, & Hays, 2014). CTT is founded on the notion that each observed score (X) is a combination of an underlying true score (T) and random error (E). Consequently, observed score (X) = true score (T) + error (E). True scores (which cannot be observed) define values for whatever is supposed to be measured, in this example, the relationship between individuals and sustainability. CTT assumes that item responses are coded so that higher response scores reflect a greater understanding of the concept of interest. Another assumption of CTT is that random errors are normally distributed (thus the expected value of random fluctuations is assumed to be 0) and uncorrelated to the true score (Crocker & Algina, 2008 ). Since this study is not testing the participant’s knowledge but rather collecting information, only dimensionality component is measured, and item difficulty and item discrimination are not measured. Dimensionality, or the extent at which the items measure a hypothesized concept distinctly, can be evaluated through factor analysis. Exploratory factor analysis (EFA) is used to generate hypotheses about the structure of the data when there is uncertainty as to the number of factors being measured. It is also useful in determining items to remove because they contribute little to the presumed underlying factor or construct. EFA should be complemented by confirmatory factor analysis (CFA) in later stages of instrument development, by imposing the hypothesized structure from the EFA on new data to confirm that structure (Cappelleri et al., 2014 ). Both EFA and CFA are commonly used in the social sciences, particularly in psychology and sociology. The basic assumption of CFA is that the observed variables are a linear function of a set of latent variables. CFA begins by specifying a model that represents the relationships among the observed variables and the latent variables. This model is then tested against the data using a variety of fit indices and statistical tests to determine how well it explains the observed data. If the model fits the data well, it can be used to make inferences about the latent variables and the relationships among them. However, if the model does not fit the data well, it may need to be modified, or a different model may need to be considered. This study utilizes EFA to explore the structure of the data and CFA to validate the structure. Reliability The concept of reliability refers to the consistency or stability of outcomes, i.e., if the assessment or data collection tool catches the same information in a consistent manner. Although tools or evaluations may be referred to as reliable, the term actually refers to the outcomes, not the tool itself. While results must be reliable, reliability alone is insufficient if they lack validity (Reynolds, Livingston, & Willson, 2005 ). There are several approaches for analyzing an instrument's reliability with a reliability coefficient when designing the instrument. Test-retest reliability, alternate-form reliability, and internal consistency reliability are all types of reliability coefficients (Reynolds et al., 2005 ). They are derived from the administration of the same test or tool on multiple occasions, administration of parallel forms of the instrument or test, and administration of a single test respectively. Internal consistency reliability is frequently used in quantitative research because they may be completed very rapidly and require just one administration of an instrument. Among estimations of reliability based on internal consistency, there are numerous prevalent statistical methods. Split-half reliability entails dividing a test or other instrument into two equal halves and administering each half separately. Using the Pearson product-moment correlation, the results of the first half are then correlated with those of the second half. Coefficient alpha or Cronbach's alpha (Cronbach, 1951 ) and Kuder- Richardson Reliability (KR-20) are utilized more frequently (Kuder & Richardson, 1937 ). Both approaches analyze the consistency of a respondent's responses to all questions or a subset of an instrument. In other words, these estimations are comparable to the mean of all potential split-half coefficients. Consequently, these estimates are susceptible to content heterogeneity, or the degree to which the instrument measures similar constructs (Reynolds et al., 2005 ). In this instance, if the underlying structure of an instrument is known to assess numerous constructs, these estimates are applied to items designed to test a particular construct. Then, a composite estimate of reliability is obtained. Typically, the reliability of composite scores is greater than that of the individual components (Reynolds et al., 2005 ). KR-20 is one of several reliability equations proposed by Kuder and Richardson ( 1937 ), although it is one of the most often employed estimates. It is applicable when objects are scored as correct or incorrect (0 or 1). Cronbach's alpha (Cronbach, 1951 ) is a broader variation of KR-20 (Kuder & Richardson, 1937 ) that deals with things that can produce numerous values (0,1,2, etc.). As a result, coefficient alpha has become the most popular statistic for calculating reliability (Keith & Reynolds, 2003 ). This is especially true for surveys, which typically contain non-binary items. In general, researchers strive for a Cronbach's alpha value of 0.70 or above, however this value may be arbitrary. Cronbach's alpha has been criticized for being unconnected to the internal structure of the test and having minimal utility, despite its widespread use (Sijtsma, 2009 ). This study utilizes internal consistency reliability, specifically Cronbach's alpha as it deals with multiple constructs that produces numerous values to measure the reliability of the instrument. Validity Validity describes the closeness of what we intend to measure and what we measure i.e., accuracy of the interpretation of the score or result (Reynolds et al., 2005 ). One needs to measure both reliability and validity as reliable results do not necessarily lead to valid results (Reynolds et al., 2005 ). For understanding the survey instrument validity, one needs to calculate different types of validity: content validity, criterion-related validity, and construct validity (Reynolds et al., 2005 ). Content validity is defined as “the degree to which items in an instrument reflect the content universe to which the instrument will be generalized” (Boudreau, Gefen, & Straub, 2001 ; Brown, 1947 ). Content validity refers to the extent to which a test adequately samples the content area of a given construct. It is frequently reviewed based on the professional opinions of subject matter experts regarding the relevance of the content. Criterion-related validation is employed when a test user is looking to make inferences from test scores to examinee behavior on a performance criterion that cannot be directly measured by a test. This typically breaks down into two types of criterion-related validation: predictive and concurrent. Predictive validity refers to the degree to which test scores predict criterion measurements that will be made in the future. For example, the SAT scores have some degree of predictive validity with respect to college grade point average (thus the justification for using SAT scores in making admissions decisions). Construct validation evidence is typically assembled through a series of studies. Correlational studies may be conducted to relate scores on a given test or instrument and some other measure of performance. Often multiple regression is used so that contributions of the construct of interest to variance in the criterion can be assessed in relationships to the contribution of other variables. Factor analysis is another approach that may be used to determine whether item responses cluster together in patterns that are reasonable when considering the theoretical structure of the chosen construct to provide evidence for or against validity (Crocker & Algina, 2008 ). For the development of this instrument content validity using expert opinions at the start of the development of the instrument and construct validity through factor analysis was used to measure validity. Methods In total, three surveys were created: a precursor survey, a pilot survey, and a main survey (the SS). The precursor interest survey was designed and distributed among Auburn and Opelika residents in the state of Alabama to select the seven most important SDGs to be evaluated. These selected seven SDGs acted as a basis for the pilot and the main survey construction. In the analysis of the pilot survey results, survey items relating to the awareness, knowledge, behavior, intended behavior, perception, concern about the seven selected SDGs from the precursor survey were analyzed using EFA. It was used to find the latent structure of items relating to these overlying themes or constructs. Based on the item loadings constructs were added and eliminated at this stage and the main survey (SS) was created. Finally, CFA was run to finalize the item loadings in their constructs – awareness/familiarity, concern/urgency, perception, involvement, behavior, and intended behavior about sustainability practices - identified after EFA. For clarity’s sake, these constructs identified through factor analysis will be italicized (E.g., SDG- Awareness and Familiarity is a construct made up of correlated survey items relating to it, as identified through EFA). Instruments The precursor interest survey was based on an in-person interview where 30 participants from diverse backgrounds, age groups, occupations, and income levels were invited and interviewed. Based on the responses and comments from experts, the survey questions were created to rank the SDGs based on priority. For the construction of the main survey instrument, the authors employed a three-stage strategy like (McNeal, Walker, & Rutherford, 2014 ) and multiple steps were taken to ensure validity and reliability of the survey instrument (Table 2 ). Stage 1 required the identification of salient scales to establish awareness, knowledge, concern, intent, intended behavior, and perceptions dimensions as they relate to SDGs. Stage 2 included the development and field testing of items internal to each of the awareness, knowledge, concern, intent, intended behavior, and perceptions scales established in Stage 1 and implementing any changes required. Stage 3 involved field testing each item followed by scale and item analyses and validation. The survey follows a similar structure and has two parts. The first section asks respondents to self-report about their level of awareness, knowledge, concern, intent, intended behavior, and perceptions about sustainability in their neighborhood based on SDGs. It is from this section that the authors conducted exploratory factor analyses to explore items relating to specific factors/construct. The second section consists of demographic questions about education, sexual orientation, age, location, occupation, gender, race, political affiliation, and income level. Each process for each of the three stages mentioned above is as follows: 1. The salient scales were identified and developed using four steps. Literature review associated with using survey instruments to understand the knowledge, attitude, perception, and practice about sustainability, SDGs, and policy development through citizen science (Afroz & Ilham, 2020 ; Emanuel & Adams, 2011 ; Melles, 2019 ; Smaniotto et al., 2020 ). The purpose was to identify available survey instruments and gaps in knowledge. Examine previously developed instruments for their awareness, knowledge, concern, intent, intended behavior, and perceptions dimensions scales that the authors could modify for the survey or that could be useful in informing the development of new scales. Classify awareness, knowledge, concern, intent, intended behavior, and perceptions scales to ensure adequate coverage of all these dimensions. Develop a set of preliminary scales to be reviewed by a panel of experts. The review was done by content experts (four university professors whose research is primarily in sustainability and resilience), survey experts (three researchers who are expert in creating surveys), and four students who are the prospective survey takers. The final scales were agreed upon based on their inputs. 2. Individual items for all the scales were created, adapting, altering, and adding items from previously published surveys and developing new items for the agreed upon dimensions. Demographic items, some of which are distinctive to this survey: education, sex, sexual orientation, income level, religion, political party affiliation, and occupation, among others were also created. Finally, the instrument was typed in Qualtrics for online distribution which was then be distributed using Amazon Mechanical Turk (MTurk) (Buhrmester, Kwang, & Gosling, 2016 ) to SEUS residents. The online instrument was pilot tested with professors and students from department of geosciences in Auburn University to see if there are any errors in the layout, design, or data retrieval. 3. Field testing and analyses was a two-step process: (i) field testing with a sample of 250 to collect data to test the validity and reliability of the survey instrument, to reduce the number of items in the pilot survey, to solicit feedback from a sample of respondents, and to determine how much time was required to complete the survey in order to finalize the instrument into a new instrument from which the authors could utilize for a larger-scale study and (ii) final collection of data after validity testing and removal of items that did not perform well to conduct CFA. Factor analysis was used to identify items that could be removed from the instrument to improve its factor structure, as well as an analysis of internal consistency reliability. Cronbach's alpha coefficient was used to quantify internal consistency in terms of item intercorrelation. To maximize alpha coefficients, items that are not significantly associated within their priori scale was deleted, and data was reanalyzed until all items with low item–scale correlations were removed. Survey Dissemination Human subject research approval (AU IRB #22–138 EX 2204) was collected from university's institutional review board (IRB). The survey sample was a random sample drawn from voluntary participants from residents in the SEUS. The survey was available on the World Wide Web through Amazon MTurk and Qualtrics platform that allows for organized survey posting, data collection, and data download. Participants completed informed consent prior to completing the survey. The estimate survey sample of 1000 respondence was collected for further analysis. Participants The target audience for precursor survey was residents of Auburn-Opelika in Alabama and the pilot and the main survey were SEUS residents. The precursor survey was created with in-person group interview consisting of 30 participants consisting of 70% male and 30% female. The participants were recruited from different sustainability and environment groups in Auburn. The main survey was disseminated as pilot survey and revised survey. The main survey was created based on the precursor survey and pilot survey refined with multiple iterations (discussed above). The pilot and the main survey comprised of English-speaking Southeast US residents. The pilot survey was piloted in May 2021 with a sample of 246 individuals. The pilot survey consisted of 41 questions with a total of 100 items. The participants for the pilot and the main survey were recruited from an online crowdsourcing system, MTURK, based on MTURK documentation of reliable performance completing other MTURK tasks. MTURK samples are representatively similar to traditional research subject pools in terms of race, gender, age, and education (Paolacci, Chandler, & Ipeirotis, 2010 ). Workers were prescreened to ensure only those with good performance records completed the survey. Workers were compensated for completing the study and compensation for task completion was within MTURK standards for similar tasks. The target of this study was to recruit 1,000 individuals. MTURK directed participants to the Qualtrics survey where they were asked to complete the multiple-choice based instrument and then provide basic demographic information (age range, gender, education level, income, etc.). Based on the analysis of pilot survey with 246 responses, the main survey was created removing some questions and changing the order of questions. As a result, the main survey consisted of 28 questions. The survey was conducted with the remaining sample of 739 individuals between July 6 and August 1, 2022. Individuals accessed the survey through MTurk.1048 individuals attempted the survey out of which 358 were considered invalid as they were out of SEUS and submitted incomplete survey resulting in 690 valid responses. The total number of participants analyzed in this study was 936. Basic demographic information from all stages of the MTURK study can be found in Table 1 . Table 1 Demographic information of MTURK participants. Category Response Pilot (n = 246) Main (n = 690) Total (n = 936) Gender Male 135 287 422 Female 111 447 558 Non-binary 0 2 2 Choose not to Identify 0 3 3 Age 18–25 19 56 75 26–35 79 202 281 36–45 63 189 252 46–55 34 181 215 55–65 26 71 97 Over 65 18 40 58 Choose not to respond 7 0 7 Education Highschool 27 77 104 Community College/Trade School 19 117 136 Undergraduate Degree 114 266 380 Graduate Degree 59 143 202 Postgraduate and above 24 129 153 Other 3 3 6 Decline to state 0 4 4 Statistical Analysis The statistical software suite used to analyze the data was Jamovi and R programming language. The data were used to develop, validate, and test reliability of constructs - awareness, knowledge, concern, intent, intended behavior, and perceptions scales. The pilot survey with 249 responses was used for EFA in Jamovi which was later combined with the 690 responses collected using the main survey for CFA in order to establish cross-validation. Cronbach’s alpha, which is an estimate of internal consistency, was utilized to calculate reliability. Typically, most concept inventory researchers set 0.7 as the acceptable value for Cronbach’s alpha (Nunnally, 1978 ; S.Litwin, 1995 ). However, since concept inventories tend to not be homogenous tests, tests of internal consistency can seriously underestimate reliability (Miller, 1995 ). Due to this fact, some researchers have given 0.6 as the minimum acceptable value for the equivalent Kuder-Richardson 20 (Ursachi, Zait, & Horodnic, 2015 ). To test the dimensionality of the concept inventory and understand how many latent factors were being measured, an EFA was completed using minimal residuals with varimax rotation in Jamovi. The goal of factor analysis is to figure out the variables' basic structure and, as a result, how strongly items load on a priori scales. With their own scale, all objects must load at least 0.45 (Walker & McNeal, 2013 ). For CFA, diagonally weighted least squares (WLSMV) model is used in laavan module in R due to ordinal nature of the responses. Criterion pattern loading of .50 or higher was used to determine which items were loading onto which factors for all EFA for this study (Byrne, Shavelson, & Muthén, 1989 ). The model fit for CFA was measured by goodness-of-fit indices - the χ2 test of exact fit, the root mean squared error of approximation (RMSEA), standardized root mean squared residual (SRMR), Comparative Fit Index (CFI), and the Tucker–Lewis Index (TLI). Values of RMSEA and SRMR closer to 0 indicate better fit, values less than .08 considered acceptable fit (Hooper, Coughlan, & Mullen, 2008 ). For CFI and TLI values closer to 1 value indicate better fit, values greater than .90 indicating good fit (Hu & Bentler, 1999 ). Results 244 responses were recorded and analyzed to get the 7 selected SDGs from the precursor survey. These selected seven SDGs acted as a basis for the pilot and the main survey construction (Fig. 2 ). The sustainability survey before EFA consisted of 43 questions with a median completion time of 11.4 minutes. Respondents answered questions about 1) awareness/familiarity about SDGs; 2) knowledge about SDGs; 3) concerns about attaining sustainability; 4) intent about supporting and practicing sustainability policies; 5) intended behavior about supporting and practicing sustainability policies; 6) perceptions about SDGs; and 7) demographics. Survey questions included a variety of types of items with multiple choice questions with Likert scale and yes/no responses (Table 2 ). Table 2 Theoretical constructs that were examined during the exploratory factor analysis. Criteria Name Purpose Question numbers Question Type SDG-Awareness and Familiarity To understand if the participant is aware about sustainable development goal Q1 to Q3 Likert Scale SDG-Knowledge To understand how much the participant know about sustainability and what falls under sustainable development goals Q4 to Q11 Yes/No SDG-Concern To understand if the participant is concerned about the socioeconomic and environmental changes due to sustainability issues Q12 to Q15 Likert Scale and Yes/No SDG-Intent To understand if the participant thinks or feels that the issues related to sustainability needs to be addressed Q16 Likert Scale SDG-Intended Behavior To understand if the participant is ready to act to solve issues related to sustainability needs Q17 to Q25 Likert Scale SDG-Perception To understand how the participant perceive about solving the issues related to sustainable development Q26 to Q28 Likert Scale No scale Questions in general I am interested in knowing - not related to any scale Q29 to Q30 Demographics General demographic question Q31 to Q41 Validity check Q42 These questions were subjected to exploratory factor analysis to develop the revised sustainability survey. It was based on the strategy that only items with a moderate factor loading on their own scale and a low factor loading on other scales be kept. It also uses the intuitive-rational strategy, which says that only things that make sense to each other stay in the final instrument (Hase & Goldberg, 1967 ). After exploratory factor analysis the question structure changed based on the analysis. Exploratory factor analysis Validity Content validity was addressed in Stage 1 with a panel of experts, and in Stage 2 with a pilot test. Construct validity was investigated through minimum residuals with varimax rotation, Kaiser normalization, and Eigenvalues greater than one. The aim of factor analysis is to determine the basic structure of a set of variables to determine how strongly items load on a priori scales. That is, it is a method to determine if an item within a given scale is measuring that scale. Only items with a factor loading of at least 0.5 with their own scale and less than 0.5 with all other scales were kept. 15 “faulty” items were identified and removed. In addition to the loss of those 15 items the entire sub-scale of knowledge was lost due to low factor loading. Likewise, due to factor loadings, the Intended Behavior subscale and Sources in the Issues scale was split into Behavior, Intended Behavior, and Involvement scale. In hindsight this is likely due to the question stems that read: “I currently take specific action to make my community more sustainable with respect to achieving following goals.” (Behavior of present), and “I intent to take specific action to make my community more sustainable with respect to achieving following goals.” (Possibility of behavior in the future) in one set. In the end, the total number of items in the refined scale was 28, decreased from the original 44. The 6 factors chosen in this study based on eigen value greater than 1.3 (Table 3 ) cumulatively explain 62.7% of the variances of the responses as shown in Table 4 . Table 3 Eigenvalues of the 6 factors in EFA. Factor Eigenvalue 1 31.1779 2 9.563 3 4.4035 4 3.4188 5 1.6281 6 1.3769 Table 4 Variance explained by the factors in EFA. Factor SS Loadings % of Variance Cumulative % 1 16.59 19.52 19.5 2 14.22 16.73 36.2 3 10.3 12.12 48.4 4 6.25 7.35 55.7 5 3.72 4.37 60.1 6 2.2 2.59 62.7 Table 5 presents the factor loading for the different items to create the new scales. ‘AQ’ represents original items on awareness/familiarity, ‘CQ’ represents concern, ‘P’ represents perception, ‘I’ represents intent, and ‘IB’ represents intended behavior. Table 5 Factor loadings of each item on 6 factors using 'Minimum residual' extraction method in combination with a 'varimax' rotation. Items Factor loadings 1 2 3 4 5 6 CQ4_1 0.728 CQ4_10 0.585 CQ4_11 0.642 CQ4_12 0.629 CQ4_13 0.606 CQ4_14 0.594 CQ4_2 0.671 CQ4_3 0.64 CQ4_4 0.715 CQ4_6 0.557 CQ4_7 0.553 CQ4_8 0.578 CQ4_9 0.565 IQ1_1 0.673 IQ1_2 0.657 IQ1_3 0.715 IQ1_4 0.703 IQ1_5 0.712 IQ1_6 0.629 IQ1_7 0.695 PQ1_1 0.675 PQ1_2 0.68 PQ1_3 0.71 PQ1_4 0.639 PQ1_5 0.687 PQ1_6 0.636 PQ1_7 0.678 PQ2_1 0.597 PQ2_2 0.666 PQ2_3 0.707 PQ2_4 0.575 PQ2_5 0.643 PQ2_6 0.558 PQ2_7 0.624 AQ3A_3 0.857 AQ3A_12 0.85 AQ3A_8 0.835 AQ3A_9 0.833 AQ3A_4 0.828 AQ3A_7 0.822 AQ3A_13 0.808 AQ3A_6 0.801 AQ3A_5 0.8 AQ3A_17 0.799 AQ3A_14 0.792 AQ3A_2 0.781 AQ3A_10 0.78 AQ3A_1 0.771 AQ3A_11 0.727 AQ3A_15 0.723 AQ3A_16 0.693 AQ1 0.635 AQ2 0.607 IBQ6_2 0.805 IBQ6_1 0.803 IBQ6_7 0.794 IBQ6_4 0.767 IBQ6_6 0.762 IBQ6_3 0.743 IBQ6_5 0.742 IBQ8_1 0.707 IBQ8_2 0.677 IBQ8_3 0.654 IBQ8_6 0.652 IBQ8_7 0.643 IBQ8_4 0.607 IBQ8_5 0.584 IBQ7 0.519 PQ3_3 0.765 PQ3_5 0.735 PQ3_2 0.73 PQ3_6 0.713 PQ3_4 0.695 PQ3_7 0.694 PQ3_1 0.693 IBQ1_1 0.699 IBQ1_3 0.622 IBQ1_5 0.604 IBQ1_7 0.573 IBQ1_2 0.572 IBQ1_4 0.542 IBQ1_6 0.526 IBQ4 0.664 IBQ9 0.599 IBQ5 0.547 Note. 'Minimum residual' extraction method was used in combination with a 'varimax' rotation Reliability During the development of the SS, each scale was analyzed for internal consistency. Table 6 presents the alpha reliability for each refined scale. Of the 6 scales/sub-scales one was removed due to low reliability (alpha < 0.60) and 1 scale was rearranged into 3 other scales. The scale removed was the entire subscale of knowledge (α = 0.6). Other items based on factor loadings were also removed. Thus, 15 additional items were removed. The overall instrument reliability after the removal of poor items was α = 0.938. Table 6 Scale reliability using Cronbach’s alpha coefficient. Criteria Name Final number of items Alpha Reliability Awareness/Familiarity (AW) 19 0.975 Concern/Urgency (CU) 34 0.965 Perception (P) 7 0.940 Behavior (B) 3 0.739 Involvement (I) 15 0.969 Intended Behavior – Engagement (IB_E) 7 0.938 Therefore, Table 7 represents the new constructs and the number of items remaining in each construct after validity and reliability analyses. Table 7 Constructs developed in the revised SS based on exploratory factor analysis. Construct Name Questions Selected from Jamovi (Varimax Rotation) Number of questions Awareness/Familiarity (AW) Q1, Q2, Q3_1 - Q3_17 19 Concern/Urgency (CU) Q15_1 to Q15_14 except Q15_5, Q16_1 – Q16_7, Q26_1 – Q26_7, Q27_1 – Q27_7 34 Perception (P) Q28_1 to Q28_7 7 Behavior (B) Q20, Q21, Q25 3 Involvement (I) Q17_1 – Q17_7 7 Intended Behavior – Engagement (IB_E) Q22_1 – Q22_7, Q23, Q241_Q24_7 15 Confirmatory Factor Analysis After running the reliability and validity test the main survey instrument was subjected to CFA in R to see how they perform with a bigger dataset. 1038 datasets were collected out of which 690 were added to already collected 249 responses to create a sample of 936 responses for CFA. In a CFA analysis, the null hypothesis is that the matrix inferred by the data and model is statistically identical to the input or analysis matrix. Hence, overall "fit" in our study refers to how accurately the given model can replicate the original polychoric correlation analysis matrix i.e., that the two matrices are statistically equivalent. It is important to note that the analysis used in this study employed robust approaches, which are typically needed for ordinal data and produces various scaled statistics. At the p = .05 significance level, the scaled (robust) chi-square for our model was X2(df) = 13610.26 (3470). This resulted in rejecting the null hypothesis which is there are no relationships between the factors created. Due to chi square being sensitive to sample size and given the large sample size in this dataset, even small departures are significant leading to a need of calculating other fit indices providing better analysis pathways. The RMSEA is a popular measure of the difference between the model-based correlation matrix and the observed correlation matrix providing data to understand model fit. It makes modifications based on model complexity (parsimony-adjusted) and has a known sample distribution, allowing confidence intervals to be calculated. The scaled RMSEA values obtained from lavaan output in R were 0.058. For analysis purpose an RMSEA = .10 as a poor fit. On the basis of the obtained RMSEA point estimate = .058 and the 90% CI [.057,.059], the authors determined that the model's fit was satisfactory. The other two popular fit measures utilized in this study to assess model adequacy were — the Comparative Fit Index (CFI) and the standardized root mean square residual (SRMR). The CFI is a member of the incremental fit index family that compares your model to a constrained baseline model. The SRMR is derived from the real differences (discrepancies) between model-based correlations and actual correlations. In addition, various interpretation recommendations for these measures have been presented. For this case, the threshold parameters were CFI > = 0.91 and SRMR < = 0.08. Based on the thresholds, the authors determined that CFI.scaled = .941 and SRMR = .007 provided additional evidence that the model was credible. Based on the values of fit measures, it was concluded that the model was plausible. Finally, item parameter estimates were examined from lavaan output are shown in Table 8 . Table 8 Laavan output of CFA and item loadings Item Standardized ci.lower ci.upper SE Z p.value 1 AW1 0.82 0.798 0.842 0.011 73.375 0 2 AW2 0.809 0.785 0.834 0.012 65.323 0 3 AW3a 0.888 0.874 0.902 0.007 122.299 0 4 AW3b 0.914 0.902 0.927 0.006 145.515 0 5 AW3c 0.927 0.916 0.937 0.005 175.576 0 6 AW3d 0.942 0.933 0.95 0.005 206.838 0 7 AW3e 0.901 0.887 0.915 0.007 126.34 0 8 AW3f 0.905 0.891 0.919 0.007 126.855 0 9 AW3g 0.913 0.901 0.926 0.006 147.555 0 10 AW3h 0.925 0.915 0.935 0.005 181.019 0 11 AW3i 0.936 0.927 0.946 0.005 199.235 0 12 AW3j 0.896 0.882 0.909 0.007 130.573 0 13 AW3k 0.866 0.85 0.882 0.008 107.305 0 14 AW3l 0.922 0.912 0.933 0.005 167.782 0 15 AW3m 0.916 0.905 0.927 0.006 162.294 0 16 AW3n 0.857 0.84 0.874 0.009 100.201 0 17 AW3o 0.867 0.852 0.882 0.008 112.25 0 18 AW3p 0.826 0.805 0.847 0.011 77.947 0 19 AW3q 0.913 0.902 0.924 0.006 161.421 0 20 CU1a 0.631 0.588 0.674 0.022 28.839 0 21 CU1b 0.693 0.656 0.73 0.019 36.608 0 22 CU1c 0.601 0.554 0.649 0.024 24.901 0 23 CU1d 0.668 0.63 0.707 0.02 33.913 0 24 CU1e 0.624 0.58 0.667 0.022 28.137 0 25 CU1f 0.77 0.738 0.802 0.016 47.149 0 26 CU1g 0.737 0.703 0.771 0.018 41.908 0 27 CU1h 0.726 0.691 0.761 0.018 40.919 0 28 CU1i 0.754 0.722 0.787 0.017 45.667 0 29 CU1j 0.733 0.7 0.766 0.017 43.297 0 30 CU1k 0.744 0.712 0.776 0.016 45.216 0 31 CU1l 0.75 0.718 0.781 0.016 46.308 0 32 CU1m 0.735 0.701 0.769 0.017 42.228 0 33 CU2a 0.794 0.767 0.822 0.014 56.349 0 34 CU2b 0.778 0.748 0.807 0.015 51.819 0 35 CU2c 0.802 0.774 0.829 0.014 56.781 0 36 CU2d 0.768 0.737 0.799 0.016 48.251 0 37 CU2e 0.776 0.746 0.805 0.015 51.366 0 38 CU2f 0.782 0.754 0.811 0.015 53.668 0 39 CU2g 0.78 0.75 0.81 0.015 50.647 0 40 CU3a 0.826 0.801 0.851 0.013 65.904 0 41 CU3b 0.809 0.781 0.836 0.014 57.675 0 42 CU3c 0.817 0.791 0.844 0.014 60.423 0 43 CU3d 0.781 0.75 0.811 0.016 50.316 0 44 CU3e 0.797 0.77 0.824 0.014 58.037 0 45 CU3f 0.808 0.781 0.835 0.014 59.18 0 46 CU3g 0.749 0.716 0.782 0.017 44.456 0 47 CU4a 0.775 0.746 0.804 0.015 52.712 0 48 CU4b 0.749 0.717 0.781 0.016 46.168 0 49 CU4c 0.774 0.744 0.804 0.015 50.293 0 50 CU4d 0.701 0.663 0.738 0.019 36.438 0 51 CU4e 0.751 0.72 0.782 0.016 47.149 0 52 CU4f 0.755 0.724 0.786 0.016 47.423 0 53 CU4g 0.73 0.696 0.764 0.017 41.971 0 54 IB_E1a 0.866 0.848 0.883 0.009 96.027 0 55 IB_E1b 0.896 0.881 0.911 0.008 116.633 0 56 IB_E1c 0.899 0.883 0.915 0.008 111.252 0 57 IB_E1d 0.855 0.835 0.876 0.011 80.892 0 58 IB_E1e 0.856 0.837 0.875 0.01 89.676 0 59 IB_E1f 0.876 0.859 0.893 0.009 100.696 0 60 IB_E1g 0.872 0.854 0.89 0.009 97.357 0 61 IB_E2 0.599 0.55 0.648 0.025 23.88 0 62 IB_E3a 0.88 0.863 0.897 0.009 101.165 0 63 IB_E3b 0.905 0.89 0.921 0.008 117.255 0 64 IB_E3c 0.897 0.88 0.914 0.009 103.323 0 65 IB_E3d 0.828 0.803 0.852 0.012 67.251 0 66 IB_E3e 0.872 0.852 0.891 0.01 89.246 0 67 IB_E3f 0.87 0.851 0.89 0.01 87.65 0 68 IB_E3g 0.861 0.841 0.881 0.01 84.429 0 69 I1a 0.869 0.847 0.891 0.011 77.076 0 70 I1b 0.868 0.846 0.889 0.011 79.273 0 71 I1c 0.897 0.879 0.915 0.009 96.017 0 72 I1d 0.843 0.816 0.869 0.014 62.423 0 73 I1e 0.877 0.857 0.896 0.01 89.692 0 74 I1f 0.904 0.884 0.923 0.01 90.51 0 75 I1g 0.899 0.881 0.916 0.009 99.101 0 76 P1a 0.849 0.821 0.877 0.014 58.768 0 77 P1b 0.865 0.839 0.89 0.013 66.703 0 78 P1c 0.911 0.888 0.934 0.012 77.213 0 79 P1d 0.881 0.854 0.908 0.014 64.108 0 80 P1e 0.825 0.796 0.854 0.015 55.596 0 81 P1f 0.852 0.824 0.88 0.014 59.476 0 82 P1g 0.849 0.823 0.875 0.013 63.275 0 83 B1 0.605 0.542 0.669 0.033 18.603 0 84 B2 0.96 0.896 1.023 0.032 29.668 0 85 B3 0.692 0.636 0.748 0.029 24.016 0 *AW – Awareness/Familiarity; CU – Concern/Urgency; IB_E – Intended Behavior; I- Involvement; P – Perception; B - Behavior This output presents the standardized factor loadings and their standard errors for the 85 items on the Awareness/Familiarity (AW), Concern/Urgency (CU), Perception (P), Behavior (B), Involvement (I), and Intended Behavior – Engagement (IB_E) latent variables of the instrument. These results support the conclusion that the instrument retained its structure with the new items of the instrument. The loadings ranged from 0.8 to 0.95 for awareness/familiarity, from 0.6 to 0.83 for concern/urgency, from 0.599 to 0.899 for intended behavior-engagement, from 0.84 to 0.91 for involvement, from 0.8 to 0.92 for perception, and from 0.6 to 0.97 for behavior, indicating that the magnitude of the item-factor relationships was adequate with cutoff for acceptable loadings being 0.5. Loadings offer researchers with vital information; they indicate how much item scores vary with a one-unit change in the construct. Items with greater loadings are more sensitive to changes in levels of the latent construct they measure and contribute more to defining the construct than items with lower loadings. Item R2s, also known as squared multiple correlations or SMCs, are related to factor loadings. R2s are the squared standard loadings of items; they represent the proportion of variance explained by each factor for each item related to the factor. The greater the amount of an item's variance that is explained by the factor, the more accurately the item measures the factor. The R2 values vary between 0.36 and 0.93 for all their items with respect to their scale. There is no specific threshold for acceptable R2s but values greater than 0.50 are desired and higher values are preferable. Only nine items had R2 values less than 0.5. This large sample CFA of the SS offers evidence to the construct measurement validity of the scales in the SS. While the objects' relationships with their respective constructs differ, they appear to perform well together as a whole. Discussion This study has validated a new survey instrument, the SS (see Appendix A), which collects information on residents’ awareness/familiarity, concern/urgency, perception, behavior, involvement, and intended behavior regarding sustainability practices in their neighborhood based on the UN SDGs. Influenced by prior sustainability and SDGs related instruments, the SS was drafted, and field tested with a total of 936 items. Through analysis of data from 246 pilot participants, 15 items were identified and eliminated with either low factor loading or low internal consistency reliability. The original 100-item pilot survey was refined to a new instrument, the SS, that measures 1) awareness/familiarity about SDGs; 2) concern/urgency about SDGs; 3) involvement about supporting and practicing sustainability policies; 4) intended behavior -engagement about supporting and practicing sustainability policies; 5) perceptions about SDGs; 6) behavior supporting sustainability practices; and 7) demographics by Southeast US residents in their city. Researchers will be able to use this survey to determine what their residents know and their values about sustainability policies in their area. Limitations As with any research-grade survey, the reliability and validity of the SS may be limited when considering future populations under investigation. Sub-scales with low numbers of items may need to be combined into larger scales or additional items may need to be added in future studies. Nevertheless, diligent investigators using the SS in future studies should be able to measure sustainability awareness/familiarity, concern/urgency, involvement, perception, intended behavior, and behavior in greater detail than previous research. These findings will enable policy makers to gain a better understanding of which policies will be accepted and where additional efforts are required for education campaigns or marketing strategies. Conclusion In conclusion, the development and validation of the SS represents a significant contribution to the field of sustainability research. The SS provides a detailed and comprehensive measurement tool for researchers to assess residents' awareness/familiarity, concern/urgency, perception, behavior, involvement, and intended behavior about sustainability practices in their neighborhood based on UN SDGs. While the survey has limitations, such as the need for future validation with different populations and potential refinement of sub-scales, the SS provides a valuable tool for researchers and policymakers to better understand the attitudes and behaviors of residents towards sustainability policies. As such, the SS can help inform the development and implementation of effective education campaigns and marketing strategies to promote sustainable practices and achieve the UN SDGs. Declarations The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Abd Rahim I, Tahir S, Musta B, Roslee R (2018) URBANIZATION VS. ENVIRONMENTAL QUALITY: SOME OBSERVATION IN TELIPOK, SABAH, MALAYSIA. 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Retrieved from https://livingcities.s3.amazonaws.com/resource/29/download.pdf MacDonald A, Clarke A, Huang L, Roseland M, Seitanidi MM (2018) Multi-stakeholder Partnerships (SDG #17) as a Means of Achieving Sustainable Communities and Cities (SDG #11). In: Leal Filho W (ed) Handbook of Sustainability Science and Research. Springer International Publishing, Cham, pp 193–209. https://doi.org/10.1007/978-3-319-63007-6_12 McNeal KS, Walker SL, Rutherford D (2014) Assessment of 6- to 20-Grade Educators’ Climate Knowledge and Perceptions: Results From the Climate Stewardship Survey. J Geosci Educ 62(4):645–654. https://doi.org/10.5408/13-098.1 Melles G (2019) Survey Study on Attitudes to Multi-Dimensional Sustainable Development with U.K. MSc Students. Social Sci 8(3):75. https://doi.org/10.3390/socsci8030075 Miller D (1995) Citizenship and Pluralism. Polit Stud 43(3):432–450. https://doi.org/10.1111/j.1467-9248.1995.tb00313.x Mudau N, Mwaniki D, Tsoeleng L, Mashalane M, Beguy D, Ndugwa R (2020) Assessment of SDG Indicator 11.3.1 and Urban Growth Trends of Major and Small Cities in South Africa. Sustainability 12(17):7063. https://doi.org/10.3390/su12177063 North Carolina State University (n.d.). Mayday 23: World Population Becomes More Urban Than Rural. Retrieved May 17, 2021, from ScienceDaily website: https://www.sciencedaily.com/releases/2007/05/070525000642.htm Nunnally JC (1978) Psychometric Theory. McGraw-Hill Omisore AG, Babarinde GM, Bakare DP, Asekun-Olarinmoye EO (2017) Awareness and Knowledge of the Sustainable Development Goals in a University Community in Southwestern Nigeria. Ethiop J Health Sci 27(6):669–676 Paolacci G, Chandler J, Ipeirotis PG (2010) Running Experiments on Amazon Mechanical Turk [SSRN Scholarly Paper]. Social Science Research Network, Rochester, NY. https://papers.ssrn.com/abstract=1626226 Retrieved from Social Science Research Network website Reynolds C, Livingston R, Willson V (2005), May 6 Measurement and Assessment in Education . Retrieved from https://www.semanticscholar.org/paper/Measurement-and-Assessment-in-Education-Reynolds-Livingston/cecad5fd9e52c9f835d1872c491b8a3ed89757fb Roser M, Ritchie H, Ortiz-Ospina E (2013) World Population Growth. Our World in Data . Retrieved from https://ourworldindata.org/world-population-growth Sijtsma K (2009) On the Use, the Misuse, and the Very Limited Usefulness of Cronbach’s Alpha. Psychometrika 74(1):107–120. https://doi.org/10.1007/s11336-008-9101-0 Litwin S, M (1995) How to Measure Survey Reliability and Validity. SAGE Publications, Inc. https://doi.org/10.4135/9781483348957 Smaniotto C, Battistella C, Brunelli L, Ruscio E, Agodi A, Auxilia F, Sisi S (2020) Sustainable Development Goals and 2030 Agenda: Awareness, Knowledge and Attitudes in Nine Italian Universities, 2019. Int J Environ Res Public Health 17(23):8968. https://doi.org/10.3390/ijerph17238968 Sustainable Development Solutions Network (2017) SDG Index and Dashboards 2017. Retrieved October 25, 2021, from https://www.sustainabledevelopment.report United Nations D of E. and, Division SAP (2019) (2019). World Urbanization Prospects: The 2018 Revision (ST/ESA/SER.A/420) . New York: United Nations University of Michigan. (n.d.). U.S. Cities Factsheet | Center for Sustainable Systems. Retrieved May 17 (2021) from http://css.umich.edu/factsheets/us-cities-factsheet Ursachi G, Zait A, Horodnic I (2015) How Reliable are Measurement Scales? External Factors with Indirect Influence on Reliability Estimators. Procedia Econ Finance 20:679–686. https://doi.org/10.1016/S2212-5671(15)00123-9 Walker S, McNeal K (2013) Development and Validation of an Instrument for Assessing Climate Change Knowledge and Perceptions: The Climate Stewardship Survey (CSS) . https://doi.org/10.18497/IEJEE-GREEN.79359 Wiersum KF (1995) 200 years of sustainability in forestry: Lessons from history. Environ Manage 19(3):321–329. https://doi.org/10.1007/BF02471975 Yamane T, Kaneko S (2021) Impact of raising awareness of Sustainable Development Goals: A survey experiment eliciting stakeholder preferences for corporate behavior. J Clean Prod 285:125291. https://doi.org/10.1016/j.jclepro.2020.125291 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6908656","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472204886,"identity":"95df3ed0-e1bb-4ddb-a9d8-8fe411683d3b","order_by":0,"name":"Dr. Megha Shrestha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYHACNiA+AGVU2ABpxsYDRGphBjLOpIG0NJCghbHtMFgIrxbd9vZrD378uZPPz3/+2IMfbOft1rYfBtpSYxONS4vZmTPlhr1tzyxnzkhmN+zhuZ287UwiUMuxtNwGXFpu5KRJ8DYcNjC4wcwmwSNxO9nsAFALY8Nh3Fruv0mT/PPnsIH9+cNskn8MziWbnX9IQMsN9mPSPGxAWxiS2aR5Eg7Ymd0gZMuZHDZp2bbDBhI3ks2kZQ4kJ5jdANqSgM8vx48/k3wDdBh//8Fnkm//2dmbnU9/+OBDjQ1OLQwMPAYo3ESwygScykGA/QEK1x6v4lEwCkbBKBiRAACa1WgoN+oHcwAAAABJRU5ErkJggg==","orcid":"","institution":"Houston Galveston Area Council","correspondingAuthor":true,"prefix":"Dr.","firstName":"Megha","middleName":"","lastName":"Shrestha","suffix":""},{"id":472204887,"identity":"44e85aaa-82d7-4a7d-9f00-d6cbb84067f3","order_by":1,"name":"Dr. Karen McNeal","email":"","orcid":"","institution":"Auburn University","correspondingAuthor":false,"prefix":"Dr.","firstName":"Karen","middleName":"","lastName":"McNeal","suffix":""},{"id":472204888,"identity":"cfb4f70a-2709-41d5-80bb-8ed67d88bea1","order_by":2,"name":"Dr. Chandana Mitra","email":"","orcid":"","institution":"Auburn University","correspondingAuthor":false,"prefix":"Dr.","firstName":"Chandana","middleName":"","lastName":"Mitra","suffix":""}],"badges":[],"createdAt":"2025-06-16 21:51:03","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6908656/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6908656/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84852860,"identity":"18a733d5-fc00-402d-9b77-a3d72b30aac4","added_by":"auto","created_at":"2025-06-18 05:06:23","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":312072,"visible":true,"origin":"","legend":"\u003cp\u003eUnited Nations Sustianable Development Goals (\u003cem\u003eSource:\u003c/em\u003e(United Nation 2015))\u003c/p\u003e","description":"","filename":"1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6908656/v1/1f51a5b0e1d79ea35d14e076.jpeg"},{"id":84852859,"identity":"f799046e-8c41-421a-aa16-987c18efefe3","added_by":"auto","created_at":"2025-06-18 05:06:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45856,"visible":true,"origin":"","legend":"\u003cp\u003ePrecursor survey responses results.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6908656/v1/f401578fed76865f7bd9a12d.png"},{"id":84854502,"identity":"7bbb3afb-1077-4e40-b52f-f184931a2bd9","added_by":"auto","created_at":"2025-06-18 05:38:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2007227,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6908656/v1/58229305-3811-4921-aa75-d9f53aa5eedd.pdf"},{"id":84852862,"identity":"5bf4cdf9-76e8-4723-933c-583b7f1f0b4d","added_by":"auto","created_at":"2025-06-18 05:06:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1797605,"visible":true,"origin":"","legend":"\u003cp\u003ethe sustainability survey\u003c/p\u003e","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6908656/v1/edb1afb5bbcc6d97b481f2f9.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDevelopment and Validation of a Survey Instrument for Sdgs Understanding in Southeast US Residents\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSustainability as a concept was first introduced in forestry where its meaning was associated with harvesting (Wiersum, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). The Brundtland Report in 1987 popularized sustainability as a policy idea, which has since been prominent in policy-oriented research to determine what public policies should accomplish (Kuhlman \u0026amp; Farrington, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Today, sustainability encompasses three pillars \u0026mdash;social equity, economic viability, and environmental protection\u0026mdash;to promote development while ensuring that resources are preserved for future generations (Kuhlman \u0026amp; Farrington, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). There is a widespread debate over the scope of sustainability. Many people believe that pursuing sustainability entails prioritizing natural resources over human progress, even though sustainability is inextricably linked to technological and human progress, with nature conservation playing a significant role in both (Kuhlman \u0026amp; Farrington, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Therefore, achieving sustainability is indissolubly linked with human population and resources used. Population has been growing exponentially from 1\u0026nbsp;billion in 1800 to 7.7\u0026nbsp;billion in 2019 (Roser, Ritchie, \u0026amp; Ortiz-Ospina, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Globally, urbanization is characterized by a rise in population density and accompanying infrastructural development (United Nations, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). According to the United Nations (UN), more people began to live in cities than in rural regions in 2007, with cities accounting for 55 percent of the world population in 2018 (North Carolina State University, n.d.). By 2050, it is expected that more than two-thirds of the world's population will be living in cities, with 64.1% and 85.9% of population living in the developing and developed worlds, respectively (United Nations \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Various studies have revealed that urbanization is having a negative impact on the environment around the world, resulting in difficulties such as increasing land insecurity, deteriorating water quality, excessive air pollution, excessive noise pollution, increasing garbage disposal issues, and so on (Abd Rahim, Tahir, Musta, \u0026amp; Roslee, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Basak, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fiorini, Zullo, Marucci, \u0026amp; Romano, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). On the other hand, urban areas can be thriving, long-term communities. The preservation of a quality environment, the use of renewable and efficient energy resources, the maintenance of a healthy population with access to health services, and the existence of economic vigor, social equity, and engaged citizens are all characteristics of a sustainable urban region (University of Michigan, n.d.) and needed for sustainable development of a city. There exist multiple studies tackling the environment, energy, and economy aspects of such development (Arshad \u0026amp; Routray, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chan, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Danielis, Rotaris, \u0026amp; Monte, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kusago, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mudau et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) but not enough studies for promoting engagement among residents of the city.\u003c/p\u003e \u003cp\u003eSustainable development, as a concept has been discussed for decades. The concept of sustainable development received its first major international recognition in 1972 at the UN Conference on the Human Environment held in Stockholm. Since then, several conferences on sustainable development have been held around sustainable development pathways. In 1987, the United Nations Brundtland Commission defined sustainability as \u0026ldquo;meeting the needs of the present without compromising the ability of future generations to meet their own needs.\u0026rdquo;(United Nations, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Since then, the United Nations has worked in different areas of sustainability, starting from education to poverty to climate action and many more. Sustainable Development Goals (SDGs) were adopted at the United Nations Conference on Sustainable Development in Rio de Janeiro in 2012 to achieve the 2030 Agenda for Sustainable Development. SDGs came into being as the UN saw the urgency to produce a set of universal goals that meet the urgent environmental, political, and economical needs of the world. In 2015, 17 goals were established, each tackling unique yet interconnected areas of society to achieve better and sustainable future by 2030. The 17 SDG goals covered are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThese goals are more global than local in character, but they serve as a foundation for gathering data and comparing residents' perceptions of urban sustainability and sustainable development in this project. This statement from the UN news (2018), \u0026ldquo;Understanding the key trends in urbanization likely to unfold over the coming years is crucial to the implementation of the 2030 Agenda for Sustainable Development, including efforts to forge a new framework of urban development,\u0026rdquo; demonstrates the value of understanding urban sustainability to achieve the goals and improve human lives in general. There are multiple initiatives taken all over the world towards creating a sustainable and better future. Hamburg (Germany), Magdeburg (Germany), St. Petersburg (US), and Milwaukee (US) were among the first cities chosen to assess the challenges and opportunities associated with their existing sustainability standards, as well as the possibility of incorporating SDGs into the broader sustainability planning process (Krellenberg, Bergstr\u0026auml;\u0026szlig;er, Bykova, Kress, \u0026amp; Tyndall, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Also, the Comprehensive Assessment System for Built Environment Efficiency (CASBEE) has been effectively implementing and assessing sustainable measures at the local level by evaluating quality and environmental load perspectives (Kawakubo et al. 2018). Koch and Krellenberg (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) focus on analyzing the contextualization of global urban goals at a national level. Their findings reveal that only a small number of the original SDG 11: Sustainable Cities and Communities targets and indicators set by the United Nations are implemented in the German cities. Therefore, considerable revisions were made in line with Germany's key sustainability challenges. The results reveal that SDG 11 contextualization and sustainable urban development are still happening in Germany and further amendments and obligations must be made (Koch \u0026amp; Krellenberg, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This shows the importance of understanding sustainability in a local context.\u003c/p\u003e \u003cp\u003eAccording to the United Nations Department of Economic and Social Affairs (UN DESA) 2018 Revision of World Urbanization Prospects, North America is the world's most urbanized region, with 82% of its population living in cities in 2018 (DESA, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The US has also taken several steps in the direction of long-term solutions at various levels - national, state, county, and city. Living Cities Report in 2009 found that over 75% of the 40 largest U.S. cities surveyed have plans for reducing greenhouse gasses in the coming years (Living Cities, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The Environment Protection Agency (EPA) also offers many clean energy programs, information, training opportunities, grants, resources, and tools to assist local governments. In 2009, the U.S. Department of Housing and Urban Development, Department of Transportation, and Environmental Protection Agency created the Partnership for Sustainable Communities to promote sustainable communities through better access to affordable housing, more transportation options, and lower transportation costs. The San Jose-Sunnyvale-Santa Clara metro region in California placed first on the SDG Index of the city ranks based on 49 indicators across 16 of the 17 SDGs (Sustainable Development Solutions Network, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As per the report from the Center for Sustainable Systems of the University of Michigan, by August 2019, 1,060 mayors have signed on to the 2005 U.S. Mayors Climate Protection Agreement, committing to reduce carbon emissions below 1990 levels, in line with the Kyoto Protocol in USA (University of Michigan, n.d.). There are national and international associations promoting collaboration and cooperation between local, regional, and national governments.\u003c/p\u003e \u003cp\u003eOne such international organization which is very active in this field is the International Council for Local Environmental Initiatives (ICLEI), whose focus is developing locally designed initiatives to achieve sustainability goals. In USA, \u0026lsquo;Smart Growth America\u0026rsquo; serves as a coalition working to improve the planning and building of towns, cities, and metro areas. The \u0026lsquo;Solar Outreach Partnership\u0026rsquo; is a component of the U.S. Department of Energy\u0026rsquo;s SunShot Initiative to make solar energy cost-competitive with other energy technologies. The Solar Outreach Partnership provides local governments with guidance on community-wide deployment of solar power. Local governments all over the USA have launched several projects aimed at achieving the common goal of sustainable development, and they need citizens to both understand and support sustainability for initiatives to be effective. However, there have not been enough studies to understand citizen\u0026rsquo;s participation in such project and policy development.\u003c/p\u003e \u003cp\u003eThis paper outlines the development and validation of a survey instrument aimed at gathering data regarding several aspects of a population\u0026rsquo;s awareness/familiarity, concern/urgency, perception, involvement, behavior, and intended behavior about sustainability practices-based on UN SDGs, and how they are related to each other and other demographics elements as an initial piece of a larger research project. The purpose of the larger project is to provide a framework that city governments in small and medium-sized cities (SMSC) may use to determine which policies their residents support and why. The urgent concern, however, is to develop a methodology on how to adapt the variety of existing sustainability and SDGs survey instruments, modify them, and/or design and evaluate new instrument scales to satisfy these requirements. This study also aims to use statistical techniques such as factor analysis to understand the latent structure of sustainability responses. Factor analysis is a statistical technique used to identify underlying dimensions, or factors, that explain the variance in a set of observed variables (Bryant \u0026amp; Yarnold, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) and latent structure models are models used in survey analysis to identify unobserved or latent variables and the relationships between them (Asparouhov \u0026amp; Muth\u0026eacute;n, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Therefore, the purpose of our research is (1) collect Southeast US (SEUS) residents' awareness/familiarity, concern/urgency, perception, involvement, behavior, and intended behavior (latent variables) about sustainability and the SDGs (2) understand which latent variables load with the items provided; and (3) provide a reliable and validated survey to collect such information that can guide present and future development plans and policies.\u003c/p\u003e\n\u003ch3\u003eSurvey Instruments as a measure of understanding SDGs\u003c/h3\u003e\n\u003cp\u003eMacDonald et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) explores the importance of involving stakeholders in the solutions of different sustainability challenges. The findings revealed that sustainable community plans are still being developed and implemented in a variety of communities around the world, with local organizations serving as implementation partners, acting as an incentive for local government investments in community sustainability, and leading to a sustainable future (MacDonald, Clarke, Huang, Roseland, \u0026amp; Seitanidi, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, to enhance such partnership it is crucial to understand and involve residents of a city in decision making. A survey instrument is a useful metric to assess such understanding and involvement across numerous fields. Clark and Libarkin (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) designed, implemented, and scored a valid and reliable mixed-methods survey instrument to gather conceptions of plate tectonics and use the results to better communicate various information related to it (Clark \u0026amp; Libarkin, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Similarly, researchers used a survey to differentiate the possible awareness levels between Alabama and Hawaii college students about sustainability, though there was not a significant difference between awareness between the college students. Hawaiian students took more action and were more likely to take further actions to make their college sustainable (Emanuel \u0026amp; Adams, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Walker and McNeal (2012) developed and validated a survey instrument for assessing climate change knowledge and views using factor analysis and classical test theory (Walker \u0026amp; McNeal, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Undergraduate business students\u0026rsquo; attitudes, beliefs, and perceptions about sustainability were evaluated pre and post curriculum change using a semi-structured questionnaire applied across two campuses of James Cook University, Australia (Eagle, Low, Case, \u0026amp; Vandommele, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Awareness and knowledge of the SDGs were examined using a cross-sectional survey in Osun State University, Southwestern Nigeria, chosen via multi-stage sampling. Researchers discovered a low level of awareness of and attitudes toward the SDGs, which has serious negative implications for SDG attainment (Omisore, Babarinde, Bakare, \u0026amp; Asekun-Olarinmoye, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Libarkin et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) also designed and examined a climate change concept inventory with high validity and reliability (Libarkin, Gold, Harris, McNeal, \u0026amp; Bowles, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Abiola, Joseph, and Rachael (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) designed a survey instrument to assess the general perception of librarians in Osun State in the attainability of the sustainable development goals. They found optimistic responses about achieving gender equality by empowering all women and girls. Additionally, they observed a widespread belief that SDGs can protect, restore, and promote the sustainable use of terrestrial ecosystems, manage forests sustainably, and combat desertification, and that library and information services are relevant to the attainment of the sustainable development goals in Nigeria (Abiola, Joseph, \u0026amp; Rachael, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Melles (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) used a survey to investigate the knowledge and attitudes of postgraduate United Kingdom (UK) students enrolled in one-year taught sustainability degrees on the multidimensional issues of sustainable development. The study discovered that this cohort was able to recognize and respond to many problems of strong and weak sustainable development issues, rather than demonstrating previously documented knowledge gaps. The survey's findings and qualitative remarks, however, show that students are opposed to major interventions in social, political, and economic life (Melles, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Another survey was used to determine the awareness level of University of Malaya students towards SDGs based on knowledge, attitude, and practice in Indonesia. They found a strong correlation between attitude and practice towards SDGs in university students (Afroz \u0026amp; Ilham, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Kazakova et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) undertook a sociological study of university students, primarily from southwestern Siberia, to assess their grasp of the Sustainable Development Goals and global concerns confronting humanity. They surveyed respondents to determine which world problems should be addressed first: ecological, social, or economic. Respondents chose differently for ecological, social, or economic problems as the most pressing at global, national, and regional scale as their priority - more concerned about ecological problems at global level and economic and social problems at national and regional levels (Kazakova et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Smaniotto et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) employed a Likert scale-based online questionnaire with 70 items to examine first-year students' awareness, knowledge, and attitudes about SDGs and sustainability at nine Italian universities (Smaniotto et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Most of the survey instruments created focuses on collecting already established policy perspectives about sustainability and sustainable development goals (Aljerf \u0026amp; Choukaife, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gadema \u0026amp; Oglethorpe, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Guan, He, He, Cheng, \u0026amp; Qu, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yamane \u0026amp; Kaneko, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in higher education and environmental studies but there is little work that specifically use survey instrument to collect residents\u0026rsquo; awareness/familiarity, concern/urgency, perception, involvement, behavior, and intended behavior related to sustainability and sustainable goals to inform future plans and policies.\u003c/p\u003e \u003cp\u003eIn the development of the survey instrument presented here, named as sustainability survey (SS), the above-mentioned surveys with respect to the project\u0026rsquo;s goal were considered to develop a customized instrument that combines various aspects of the previously noted instruments and newly created items to understand sustainability practices in the SEUS. These following sections outline the stages of development of the SS, along with the reliability and validity descriptions of this new instrument.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClassical Test Theory\u003c/h2\u003e \u003cp\u003eClassical test theory (CTT) employs a conventional quantitative method to assess the reliability and validity of a scale based on its items (Cappelleri, Jason Lundy, \u0026amp; Hays, 2014). CTT is founded on the notion that each observed score (X) is a combination of an underlying true score (T) and random error (E). Consequently, observed score (X)\u0026thinsp;=\u0026thinsp;true score (T)\u0026thinsp;+\u0026thinsp;error (E). True scores (which cannot be observed) define values for whatever is supposed to be measured, in this example, the relationship between individuals and sustainability. CTT assumes that item responses are coded so that higher response scores reflect a greater understanding of the concept of interest. Another assumption of CTT is that random errors are normally distributed (thus the expected value of random fluctuations is assumed to be 0) and uncorrelated to the true score (Crocker \u0026amp; Algina, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Since this study is not testing the participant\u0026rsquo;s knowledge but rather collecting information, only dimensionality component is measured, and item difficulty and item discrimination are not measured. Dimensionality, or the extent at which the items measure a hypothesized concept distinctly, can be evaluated through factor analysis. Exploratory factor analysis (EFA) is used to generate hypotheses about the structure of the data when there is uncertainty as to the number of factors being measured. It is also useful in determining items to remove because they contribute little to the presumed underlying factor or construct. EFA should be complemented by confirmatory factor analysis (CFA) in later stages of instrument development, by imposing the hypothesized structure from the EFA on new data to confirm that structure (Cappelleri et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Both EFA and CFA are commonly used in the social sciences, particularly in psychology and sociology. The basic assumption of CFA is that the observed variables are a linear function of a set of latent variables. CFA begins by specifying a model that represents the relationships among the observed variables and the latent variables. This model is then tested against the data using a variety of fit indices and statistical tests to determine how well it explains the observed data. If the model fits the data well, it can be used to make inferences about the latent variables and the relationships among them. However, if the model does not fit the data well, it may need to be modified, or a different model may need to be considered. This study utilizes EFA to explore the structure of the data and CFA to validate the structure.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eReliability\u003c/h3\u003e\n\u003cp\u003eThe concept of reliability refers to the consistency or stability of outcomes, i.e., if the assessment or data collection tool catches the same information in a consistent manner. Although tools or evaluations may be referred to as reliable, the term actually refers to the outcomes, not the tool itself. While results must be reliable, reliability alone is insufficient if they lack validity (Reynolds, Livingston, \u0026amp; Willson, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). There are several approaches for analyzing an instrument's reliability with a reliability coefficient when designing the instrument. Test-retest reliability, alternate-form reliability, and internal consistency reliability are all types of reliability coefficients (Reynolds et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). They are derived from the administration of the same test or tool on multiple occasions, administration of parallel forms of the instrument or test, and administration of a single test respectively. Internal consistency reliability is frequently used in quantitative research because they may be completed very rapidly and require just one administration of an instrument. Among estimations of reliability based on internal consistency, there are numerous prevalent statistical methods. Split-half reliability entails dividing a test or other instrument into two equal halves and administering each half separately. Using the Pearson product-moment correlation, the results of the first half are then correlated with those of the second half. Coefficient alpha or Cronbach's alpha (Cronbach, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1951\u003c/span\u003e) and Kuder- Richardson Reliability (KR-20) are utilized more frequently (Kuder \u0026amp; Richardson, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1937\u003c/span\u003e). Both approaches analyze the consistency of a respondent's responses to all questions or a subset of an instrument. In other words, these estimations are comparable to the mean of all potential split-half coefficients. Consequently, these estimates are susceptible to content heterogeneity, or the degree to which the instrument measures similar constructs (Reynolds et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In this instance, if the underlying structure of an instrument is known to assess numerous constructs, these estimates are applied to items designed to test a particular construct. Then, a composite estimate of reliability is obtained. Typically, the reliability of composite scores is greater than that of the individual components (Reynolds et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). KR-20 is one of several reliability equations proposed by Kuder and Richardson (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1937\u003c/span\u003e), although it is one of the most often employed estimates. It is applicable when objects are scored as correct or incorrect (0 or 1). Cronbach's alpha (Cronbach, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1951\u003c/span\u003e) is a broader variation of KR-20 (Kuder \u0026amp; Richardson, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1937\u003c/span\u003e) that deals with things that can produce numerous values (0,1,2, etc.). As a result, coefficient alpha has become the most popular statistic for calculating reliability (Keith \u0026amp; Reynolds, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This is especially true for surveys, which typically contain non-binary items. In general, researchers strive for a Cronbach's alpha value of 0.70 or above, however this value may be arbitrary. Cronbach's alpha has been criticized for being unconnected to the internal structure of the test and having minimal utility, despite its widespread use (Sijtsma, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This study utilizes internal consistency reliability, specifically Cronbach's alpha as it deals with multiple constructs that produces numerous values to measure the reliability of the instrument.\u003c/p\u003e\n\u003ch3\u003eValidity\u003c/h3\u003e\n\u003cp\u003eValidity describes the closeness of what we intend to measure and what we measure i.e., accuracy of the interpretation of the score or result (Reynolds et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). One needs to measure both reliability and validity as reliable results do not necessarily lead to valid results (Reynolds et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). For understanding the survey instrument validity, one needs to calculate different types of validity: content validity, criterion-related validity, and construct validity (Reynolds et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Content validity is defined as \u0026ldquo;the degree to which items in an instrument reflect the content universe to which the instrument will be generalized\u0026rdquo; (Boudreau, Gefen, \u0026amp; Straub, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Brown, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1947\u003c/span\u003e). Content validity refers to the extent to which a test adequately samples the content area of a given construct. It is frequently reviewed based on the professional opinions of subject matter experts regarding the relevance of the content. Criterion-related validation is employed when a test user is looking to make inferences from test scores to examinee behavior on a performance criterion that cannot be directly measured by a test. This typically breaks down into two types of criterion-related validation: predictive and concurrent. Predictive validity refers to the degree to which test scores predict criterion measurements that will be made in the future. For example, the SAT scores have some degree of predictive validity with respect to college grade point average (thus the justification for using SAT scores in making admissions decisions). Construct validation evidence is typically assembled through a series of studies. Correlational studies may be conducted to relate scores on a given test or instrument and some other measure of performance. Often multiple regression is used so that contributions of the construct of interest to variance in the criterion can be assessed in relationships to the contribution of other variables. Factor analysis is another approach that may be used to determine whether item responses cluster together in patterns that are reasonable when considering the theoretical structure of the chosen construct to provide evidence for or against validity (Crocker \u0026amp; Algina, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). For the development of this instrument content validity using expert opinions at the start of the development of the instrument and construct validity through factor analysis was used to measure validity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eIn total, three surveys were created: a precursor survey, a pilot survey, and a main survey (the SS). The precursor interest survey was designed and distributed among Auburn and Opelika residents in the state of Alabama to select the seven most important SDGs to be evaluated. These selected seven SDGs acted as a basis for the pilot and the main survey construction. In the analysis of the pilot survey results, survey items relating to the awareness, knowledge, behavior, intended behavior, perception, concern about the seven selected SDGs from the precursor survey were analyzed using EFA. It was used to find the latent structure of items relating to these overlying themes or constructs. Based on the item loadings constructs were added and eliminated at this stage and the main survey (SS) was created. Finally, CFA was run to finalize the item loadings in their constructs \u0026ndash; awareness/familiarity, concern/urgency, perception, involvement, behavior, and intended behavior about sustainability practices - identified after EFA. For clarity\u0026rsquo;s sake, these constructs identified through factor analysis will be italicized (E.g., SDG- Awareness and Familiarity is a construct made up of correlated survey items relating to it, as identified through EFA).\u003c/p\u003e\n\u003cp\u003eInstruments\u003c/p\u003e\n\u003cp\u003eThe precursor interest survey was based on an in-person interview where 30 participants from diverse backgrounds, age groups, occupations, and income levels were invited and interviewed. Based on the responses and comments from experts, the survey questions were created to rank the SDGs based on priority.\u003c/p\u003e\n\u003cp\u003eFor the construction of the main survey instrument, the authors employed a three-stage strategy like (McNeal, Walker, \u0026amp; Rutherford, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) and multiple steps were taken to ensure validity and reliability of the survey instrument (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Stage 1 required the identification of salient scales to establish awareness, knowledge, concern, intent, intended behavior, and perceptions dimensions as they relate to SDGs. Stage 2 included the development and field testing of items internal to each of the awareness, knowledge, concern, intent, intended behavior, and perceptions scales established in Stage 1 and implementing any changes required. Stage 3 involved field testing each item followed by scale and item analyses and validation. The survey follows a similar structure and has two parts.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\n \u003cp\u003eThe first section asks respondents to self-report about their level of awareness, knowledge, concern, intent, intended behavior, and perceptions about sustainability in their neighborhood based on SDGs. It is from this section that the authors conducted exploratory factor analyses to explore items relating to specific factors/construct.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe second section consists of demographic questions about education, sexual orientation, age, location, occupation, gender, race, political affiliation, and income level.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eEach process for each of the three stages mentioned above is as follows:\u003c/p\u003e\n\u003cp\u003e1. The salient scales were identified and developed using four steps.\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003e\n \u003cp\u003eLiterature review associated with using survey instruments to understand the knowledge, attitude, perception, and practice about sustainability, SDGs, and policy development through citizen science (Afroz \u0026amp; Ilham, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Emanuel \u0026amp; Adams, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Melles, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Smaniotto et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The purpose was to identify available survey instruments and gaps in knowledge.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eExamine previously developed instruments for their awareness, knowledge, concern, intent, intended behavior, and perceptions dimensions scales that the authors could modify for the survey or that could be useful in informing the development of new scales.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eClassify awareness, knowledge, concern, intent, intended behavior, and perceptions scales to ensure adequate coverage of all these dimensions.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eDevelop a set of preliminary scales to be reviewed by a panel of experts. The review was done by content experts (four university professors whose research is primarily in sustainability and resilience), survey experts (three researchers who are expert in creating surveys), and four students who are the prospective survey takers. The final scales were agreed upon based on their inputs.\u0026nbsp;\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e2. Individual items for all the scales were created, adapting, altering, and adding items from previously published surveys and developing new items for the agreed upon dimensions. Demographic items, some of which are distinctive to this survey: education, sex, sexual orientation, income level, religion, political party affiliation, and occupation, among others were also created. Finally, the instrument was typed in Qualtrics for online distribution which was then be distributed using Amazon Mechanical Turk (MTurk) (Buhrmester, Kwang, \u0026amp; Gosling, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) to SEUS residents. The online instrument was pilot tested with professors and students from department of geosciences in Auburn University to see if there are any errors in the layout, design, or data retrieval.\u003c/p\u003e\n\u003cp\u003e3. Field testing and analyses was a two-step process: (i) field testing with a sample of 250 to collect data to test the validity and reliability of the survey instrument, to reduce the number of items in the pilot survey, to solicit feedback from a sample of respondents, and to determine how much time was required to complete the survey in order to finalize the instrument into a new instrument from which the authors could utilize for a larger-scale study and (ii) final collection of data after validity testing and removal of items that did not perform well to conduct CFA. Factor analysis was used to identify items that could be removed from the instrument to improve its factor structure, as well as an analysis of internal consistency reliability. Cronbach\u0026apos;s alpha coefficient was used to quantify internal consistency in terms of item intercorrelation. To maximize alpha coefficients, items that are not significantly associated within their priori scale was deleted, and data was reanalyzed until all items with low item\u0026ndash;scale correlations were removed.\u003c/p\u003e\n\u003ch3\u003eSurvey Dissemination\u003c/h3\u003e\n\u003cp\u003eHuman subject research approval (AU IRB #22\u0026ndash;138 EX 2204) was collected from university\u0026apos;s institutional review board (IRB). The survey sample was a random sample drawn from voluntary participants from residents in the SEUS. The survey was available on the World Wide Web through Amazon MTurk and Qualtrics platform that allows for organized survey posting, data collection, and data download. Participants completed informed consent prior to completing the survey. The estimate survey sample of 1000 respondence was collected for further analysis.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipants\u003c/h2\u003e\n \u003cp\u003eThe target audience for precursor survey was residents of Auburn-Opelika in Alabama and the pilot and the main survey were SEUS residents. The precursor survey was created with in-person group interview consisting of 30 participants consisting of 70% male and 30% female. The participants were recruited from different sustainability and environment groups in Auburn. The main survey was disseminated as pilot survey and revised survey. The main survey was created based on the precursor survey and pilot survey refined with multiple iterations (discussed above). The pilot and the main survey comprised of English-speaking Southeast US residents. The pilot survey was piloted in May 2021 with a sample of 246 individuals. The pilot survey consisted of 41 questions with a total of 100 items. The participants for the pilot and the main survey were recruited from an online crowdsourcing system, MTURK, based on MTURK documentation of reliable performance completing other MTURK tasks. MTURK samples are representatively similar to traditional research subject pools in terms of race, gender, age, and education (Paolacci, Chandler, \u0026amp; Ipeirotis, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). Workers were prescreened to ensure only those with good performance records completed the survey. Workers were compensated for completing the study and compensation for task completion was within MTURK standards for similar tasks. The target of this study was to recruit 1,000 individuals. MTURK directed participants to the Qualtrics survey where they were asked to complete the multiple-choice based instrument and then provide basic demographic information (age range, gender, education level, income, etc.). Based on the analysis of pilot survey with 246 responses, the main survey was created removing some questions and changing the order of questions. As a result, the main survey consisted of 28 questions. The survey was conducted with the remaining sample of 739 individuals between July 6 and August 1, 2022. Individuals accessed the survey through MTurk.1048 individuals attempted the survey out of which 358 were considered invalid as they were out of SEUS and submitted incomplete survey resulting in 690 valid responses. The total number of participants analyzed in this study was 936. Basic demographic information from all stages of the MTURK study can be found in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic information of MTURK participants.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResponse\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePilot (n\u0026thinsp;=\u0026thinsp;246)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMain (n\u0026thinsp;=\u0026thinsp;690)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;936)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e558\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-binary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChoose not to Identify\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u0026ndash;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u0026ndash;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u0026ndash;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026ndash;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOver 65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChoose not to respond\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighschool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity College/Trade School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUndergraduate Degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduate Degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePostgraduate and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecline to state\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eThe statistical software suite used to analyze the data was Jamovi and R programming language. The data were used to develop, validate, and test reliability of constructs - awareness, knowledge, concern, intent, intended behavior, and perceptions scales. The pilot survey with 249 responses was used for EFA in Jamovi which was later combined with the 690 responses collected using the main survey for CFA in order to establish cross-validation. Cronbach\u0026rsquo;s alpha, which is an estimate of internal consistency, was utilized to calculate reliability. Typically, most concept inventory researchers set 0.7 as the acceptable value for Cronbach\u0026rsquo;s alpha (Nunnally, \u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e; S.Litwin, \u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e). However, since concept inventories tend to not be homogenous tests, tests of internal consistency can seriously underestimate reliability (Miller, \u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e). Due to this fact, some researchers have given 0.6 as the minimum acceptable value for the equivalent Kuder-Richardson 20 (Ursachi, Zait, \u0026amp; Horodnic, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). To test the dimensionality of the concept inventory and understand how many latent factors were being measured, an EFA was completed using minimal residuals with varimax rotation in Jamovi. The goal of factor analysis is to figure out the variables\u0026apos; basic structure and, as a result, how strongly items load on a priori scales. With their own scale, all objects must load at least 0.45 (Walker \u0026amp; McNeal, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). For CFA, diagonally weighted least squares (WLSMV) model is used in laavan module in R due to ordinal nature of the responses. Criterion pattern loading of .50 or higher was used to determine which items were loading onto which factors for all EFA for this study (Byrne, Shavelson, \u0026amp; Muth\u0026eacute;n, \u003cspan class=\"CitationRef\"\u003e1989\u003c/span\u003e). The model fit for CFA was measured by goodness-of-fit indices - the \u0026chi;2 test of exact fit, the root mean squared error of approximation (RMSEA), standardized root mean squared residual (SRMR), Comparative Fit Index (CFI), and the Tucker\u0026ndash;Lewis Index (TLI). Values of RMSEA and SRMR closer to 0 indicate better fit, values less than .08 considered acceptable fit (Hooper, Coughlan, \u0026amp; Mullen, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). For CFI and TLI values closer to 1 value indicate better fit, values greater than .90 indicating good fit (Hu \u0026amp; Bentler, \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e244 responses were recorded and analyzed to get the 7 selected SDGs from the precursor survey. These selected seven SDGs acted as a basis for the pilot and the main survey construction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe sustainability survey before EFA consisted of 43 questions with a median completion time of 11.4 minutes. Respondents answered questions about 1) awareness/familiarity about SDGs; 2) knowledge about SDGs; 3) concerns about attaining sustainability; 4) intent about supporting and practicing sustainability policies; 5) intended behavior about supporting and practicing sustainability policies; 6) perceptions about SDGs; and 7) demographics. Survey questions included a variety of types of items with multiple choice questions with Likert scale and yes/no responses (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTheoretical constructs that were examined during the exploratory factor analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePurpose\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuestion numbers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuestion Type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDG-Awareness and Familiarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo understand if the participant is aware about sustainable development goal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1 to Q3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLikert Scale\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDG-Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo understand how much the participant know about sustainability and what falls under sustainable development goals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ4 to Q11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDG-Concern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo understand if the participant is concerned about the socioeconomic and environmental changes due to sustainability issues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ12 to Q15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLikert Scale and Yes/No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDG-Intent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo understand if the participant thinks or feels that the issues related to sustainability needs to be addressed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLikert Scale\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDG-Intended Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo understand if the participant is ready to act to solve issues related to sustainability needs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ17 to Q25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLikert Scale\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDG-Perception\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo understand how the participant perceive about solving the issues related to sustainable development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ26 to Q28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLikert Scale\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuestions in general I am interested in knowing - not related to any scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ29 to Q30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral demographic question\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ31 to Q41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValidity check\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese questions were subjected to exploratory factor analysis to develop the revised sustainability survey. It was based on the strategy that only items with a moderate factor loading on their own scale and a low factor loading on other scales be kept. It also uses the intuitive-rational strategy, which says that only things that make sense to each other stay in the final instrument (Hase \u0026amp; Goldberg, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1967\u003c/span\u003e). After exploratory factor analysis the question structure changed based on the analysis.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eExploratory factor analysis\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eValidity\u003c/h2\u003e \u003cp\u003eContent validity was addressed in Stage 1 with a panel of experts, and in Stage 2 with a pilot test. Construct validity was investigated through minimum residuals with varimax rotation, Kaiser normalization, and Eigenvalues greater than one. The aim of factor analysis is to determine the basic structure of a set of variables to determine how strongly items load on a priori scales. That is, it is a method to determine if an item within a given scale is measuring that scale. Only items with a factor loading of at least 0.5 with their own scale and less than 0.5 with all other scales were kept. 15 \u0026ldquo;faulty\u0026rdquo; items were identified and removed. In addition to the loss of those 15 items the entire sub-scale of knowledge was lost due to low factor loading. Likewise, due to factor loadings, the Intended Behavior subscale and Sources in the Issues scale was split into Behavior, Intended Behavior, and Involvement scale. In hindsight this is likely due to the question stems that read: \u0026ldquo;I currently take specific action to make my community more sustainable with respect to achieving following goals.\u0026rdquo; (Behavior of present), and \u0026ldquo;I intent to take specific action to make my community more sustainable with respect to achieving following goals.\u0026rdquo; (Possibility of behavior in the future) in one set. In the end, the total number of items in the refined scale was 28, decreased from the original 44. The 6 factors chosen in this study based on eigen value greater than 1.3 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) cumulatively explain 62.7% of the variances of the responses as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEigenvalues of the 6 factors in EFA.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.1779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.4035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.4188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.6281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.3769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariance explained by the factors in EFA.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS Loadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% of Variance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCumulative %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the factor loading for the different items to create the new scales. \u0026lsquo;AQ\u0026rsquo; represents original items on awareness/familiarity, \u0026lsquo;CQ\u0026rsquo; represents concern, \u0026lsquo;P\u0026rsquo; represents perception, \u0026lsquo;I\u0026rsquo; represents intent, and \u0026lsquo;IB\u0026rsquo; represents intended behavior.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactor loadings of each item on 6 factors using 'Minimum residual' extraction method in combination with a 'varimax' rotation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eFactor loadings\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCQ4_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCQ4_10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCQ4_11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCQ4_12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCQ4_13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCQ4_14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCQ4_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCQ4_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCQ4_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCQ4_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCQ4_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCQ4_8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCQ4_9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQ1_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQ1_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQ1_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ1_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ1_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e 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colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ1_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ1_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ1_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ2_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ2_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ2_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e 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colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ3A_8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ3A_9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ3A_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ3A_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ3A_13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ3A_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ3A_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ3A_17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ3A_14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ3A_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ3A_10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ3A_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ3A_11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ3A_15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ3A_16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ6_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ6_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ6_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ6_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ6_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ6_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ6_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ8_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ8_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ8_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ8_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ8_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ8_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ8_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ3_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ3_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ3_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ3_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ3_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ3_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ3_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ1_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ1_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e 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colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ1_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ1_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ1_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBQ5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e 'Minimum residual' extraction method was used in combination with a 'varimax' rotation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReliability\u003c/h2\u003e \u003cp\u003eDuring the development of the SS, each scale was analyzed for internal consistency. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the alpha reliability for each refined scale. Of the 6 scales/sub-scales one was removed due to low reliability (alpha\u0026thinsp;\u0026lt;\u0026thinsp;0.60) and 1 scale was rearranged into 3 other scales. The scale removed was the entire subscale of knowledge (α\u0026thinsp;=\u0026thinsp;0.6). Other items based on factor loadings were also removed. Thus, 15 additional items were removed. The overall instrument reliability after the removal of poor items was α\u0026thinsp;=\u0026thinsp;0.938.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eScale reliability using Cronbach\u0026rsquo;s alpha coefficient.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinal number of items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlpha Reliability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwareness/Familiarity (AW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcern/Urgency (CU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerception (P)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavior (B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvolvement (I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntended Behavior \u0026ndash; Engagement (IB_E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTherefore, Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e represents the new constructs and the number of items remaining in each construct after validity and reliability analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConstructs developed in the revised SS based on exploratory factor analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuestions Selected from Jamovi (Varimax Rotation)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of questions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwareness/Familiarity (AW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1, Q2, Q3_1 - Q3_17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcern/Urgency (CU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ15_1 to Q15_14 except Q15_5, Q16_1 \u0026ndash; Q16_7, Q26_1 \u0026ndash; Q26_7, Q27_1 \u0026ndash; Q27_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerception (P)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ28_1 to Q28_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavior (B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ20, Q21, Q25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvolvement (I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ17_1 \u0026ndash; Q17_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntended Behavior \u0026ndash; Engagement (IB_E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ22_1 \u0026ndash; Q22_7, Q23, Q241_Q24_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eConfirmatory Factor Analysis\u003c/h2\u003e \u003cp\u003eAfter running the reliability and validity test the main survey instrument was subjected to CFA in R to see how they perform with a bigger dataset. 1038 datasets were collected out of which 690 were added to already collected 249 responses to create a sample of 936 responses for CFA. In a CFA analysis, the null hypothesis is that the matrix inferred by the data and model is statistically identical to the input or analysis matrix. Hence, overall \"fit\" in our study refers to how accurately the given model can replicate the original polychoric correlation analysis matrix i.e., that the two matrices are statistically equivalent. It is important to note that the analysis used in this study employed robust approaches, which are typically needed for ordinal data and produces various scaled statistics. At the p\u0026thinsp;=\u0026thinsp;.05 significance level, the scaled (robust) chi-square for our model was X2(df)\u0026thinsp;=\u0026thinsp;13610.26 (3470). This resulted in rejecting the null hypothesis which is there are no relationships between the factors created. Due to chi square being sensitive to sample size and given the large sample size in this dataset, even small departures are significant leading to a need of calculating other fit indices providing better analysis pathways.\u003c/p\u003e \u003cp\u003eThe RMSEA is a popular measure of the difference between the model-based correlation matrix and the observed correlation matrix providing data to understand model fit. It makes modifications based on model complexity (parsimony-adjusted) and has a known sample distribution, allowing confidence intervals to be calculated. The scaled RMSEA values obtained from lavaan output in R were 0.058. For analysis purpose an RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;.05 as the threshold for a close fit; RMSEA\u0026thinsp;=\u0026thinsp;.05 \u0026ndash;.08 as a reasonable fit; and RMSEA\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;.10 as a poor fit. On the basis of the obtained RMSEA point estimate\u0026thinsp;=\u0026thinsp;.058 and the 90% CI [.057,.059], the authors determined that the model's fit was satisfactory.\u003c/p\u003e \u003cp\u003eThe other two popular fit measures utilized in this study to assess model adequacy were \u0026mdash; the Comparative Fit Index (CFI) and the standardized root mean square residual (SRMR). The CFI is a member of the incremental fit index family that compares your model to a constrained baseline model. The SRMR is derived from the real differences (discrepancies) between model-based correlations and actual correlations. In addition, various interpretation recommendations for these measures have been presented. For this case, the threshold parameters were CFI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.91 and SRMR\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.08. Based on the thresholds, the authors determined that CFI.scaled\u0026thinsp;=\u0026thinsp;.941 and SRMR\u0026thinsp;=\u0026thinsp;.007 provided additional evidence that the model was credible.\u003c/p\u003e \u003cp\u003eBased on the values of fit measures, it was concluded that the model was plausible. Finally, item parameter estimates were examined from lavaan output are shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLaavan output of CFA and item loadings\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardized\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eci.lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eci.upper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep.value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.888\u003c/p\u003e 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colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW3d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e206.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW3e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e126.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAW3f\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI1d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI1e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI1f\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI1g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP1d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP1e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP1f\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP1g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e*AW \u0026ndash; Awareness/Familiarity; CU \u0026ndash; Concern/Urgency; IB_E \u0026ndash; Intended Behavior; I- Involvement; P \u0026ndash; Perception; B - Behavior\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis output presents the standardized factor loadings and their standard errors for the 85 items on the Awareness/Familiarity (AW), Concern/Urgency (CU), Perception (P), Behavior (B), Involvement (I), and Intended Behavior \u0026ndash; Engagement (IB_E) latent variables of the instrument. These results support the conclusion that the instrument retained its structure with the new items of the instrument. The loadings ranged from 0.8 to 0.95 for awareness/familiarity, from 0.6 to 0.83 for concern/urgency, from 0.599 to 0.899 for intended behavior-engagement, from 0.84 to 0.91 for involvement, from 0.8 to 0.92 for perception, and from 0.6 to 0.97 for behavior, indicating that the magnitude of the item-factor relationships was adequate with cutoff for acceptable loadings being 0.5. Loadings offer researchers with vital information; they indicate how much item scores vary with a one-unit change in the construct. Items with greater loadings are more sensitive to changes in levels of the latent construct they measure and contribute more to defining the construct than items with lower loadings. Item R2s, also known as squared multiple correlations or SMCs, are related to factor loadings. R2s are the squared standard loadings of items; they represent the proportion of variance explained by each factor for each item related to the factor. The greater the amount of an item's variance that is explained by the factor, the more accurately the item measures the factor. The R2 values vary between 0.36 and 0.93 for all their items with respect to their scale. There is no specific threshold for acceptable R2s but values greater than 0.50 are desired and higher values are preferable. Only nine items had R2 values less than 0.5.\u003c/p\u003e \u003cp\u003eThis large sample CFA of the SS offers evidence to the construct measurement validity of the scales in the SS. While the objects' relationships with their respective constructs differ, they appear to perform well together as a whole.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study has validated a new survey instrument, the SS (see Appendix A), which collects information on residents’ awareness/familiarity, concern/urgency, perception, behavior, involvement, and intended behavior regarding sustainability practices in their neighborhood based on the UN SDGs. Influenced by prior sustainability and SDGs related instruments, the SS was drafted, and field tested with a total of 936 items. Through analysis of data from 246 pilot participants, 15 items were identified and eliminated with either low factor loading or low internal consistency reliability. The original 100-item pilot survey was refined to a new instrument, the SS, that measures 1) awareness/familiarity about SDGs; 2) concern/urgency about SDGs; 3) involvement about supporting and practicing sustainability policies; 4) intended behavior -engagement about supporting and practicing sustainability policies; 5) perceptions about SDGs; 6) behavior supporting sustainability practices; and 7) demographics by Southeast US residents in their city. Researchers will be able to use this survey to determine what their residents know and their values about sustainability policies in their area.\u003c/p\u003e "},{"header":"Limitations","content":"\u003cp\u003eAs with any research-grade survey, the reliability and validity of the SS may be limited when considering future populations under investigation. Sub-scales with low numbers of items may need to be combined into larger scales or additional items may need to be added in future studies. Nevertheless, diligent investigators using the SS in future studies should be able to measure sustainability awareness/familiarity, concern/urgency, involvement, perception, intended behavior, and behavior in greater detail than previous research. These findings will enable policy makers to gain a better understanding of which policies will be accepted and where additional efforts are required for education campaigns or marketing strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the development and validation of the SS represents a significant contribution to the field of sustainability research. The SS provides a detailed and comprehensive measurement tool for researchers to assess residents' awareness/familiarity, concern/urgency, perception, behavior, involvement, and intended behavior about sustainability practices in their neighborhood based on UN SDGs. While the survey has limitations, such as the need for future validation with different populations and potential refinement of sub-scales, the SS provides a valuable tool for researchers and policymakers to better understand the attitudes and behaviors of residents towards sustainability policies. As such, the SS can help inform the development and implementation of effective education campaigns and marketing strategies to promote sustainable practices and achieve the UN SDGs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbd Rahim I, Tahir S, Musta B, Roslee R (2018) URBANIZATION VS. ENVIRONMENTAL QUALITY: SOME OBSERVATION IN TELIPOK, SABAH, MALAYSIA. 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The SS was developed by drawing from previous instruments related to sustainability and SDGs, and tested with 936 responses, leading to the refinement of a 100-item pilot survey to a new instrument with 85 items and 7 subscales. The survey provides a comprehensive measurement tool for researchers and policymakers to better understand the attitudes and behaviors of residents towards sustainability policies and can inform the development and implementation of effective education campaigns and marketing strategies to promote sustainable practices and achieve the UN SDGs.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Survey Instrument for Sdgs Understanding in Southeast US Residents","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 05:06:18","doi":"10.21203/rs.3.rs-6908656/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3710ff50-71ec-46d5-b9e9-555334450bf0","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-18T05:06:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-18 05:06:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6908656","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6908656","identity":"rs-6908656","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}