Psychometric validity and reliability of a methods of research attitude scale: an item response theory analysis

Psychometric validity and reliability of a methods of research attitude scale: an item response theory analysis | 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 Article Psychometric validity and reliability of a methods of research attitude scale: an item response theory analysis Antonio Matas Terrón, Pablo Daniel Franco Caballero, Lourdes Aranda, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6593380/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 The acquisition of data literacy skills is crucial for enhancing teaching practices and educational outcomes. Given that many education students exhibit negative attitudes towards research methods, it is essential to develop tools that accurately assess these perceptions to foster positive changes. This study evaluates the psychometric properties of the Attitudes Toward Research Methodology Questionnaire (ATRMQ), a tool specifically designed to measure education students’ attitudes toward research methodologies. The ATRMQ was administered to a sample of 377 undergraduate education students in Andalusia, Spain. Psychometric analysis, based on Item Response Theory (IRT), assessed reliability, validity, item difficulty, and discrimination indices within its three-dimensional structure: anxiety, interest, and perceived usefulness. Results indicate that the ATRMQ effectively distinguishes between levels of attitude towards research methodology, demonstrating high internal consistency across dimensions. Findings provide valuable insights for refining the ATRMQ, and an updated version is proposed. The ATRMQ offers a robust tool for identifying attitudinal barriers in research methodology training, with implications for curriculum design aimed at improving data literacy and methodological competence among future educators. The study underscores the value of IRT in enhancing scale precision over Classical Test Theory (CTT), advocating its application in educational assessments. Social science/Education Social science/Psychology Attitudes Toward Research Methodology Psychometric Analysis Item Response Theory (IRT) Education Students Data Literacy Figures Figure 1 Figure 2 Introduction The interest in data literacy is indeed experiencing a significant rise, as evidenced by a variety of studies and reports across multiple sectors, including education, healthcare, business, and public policy (Nwagwu, 2024 ). This increasing focus on data literacy is being driven by a diverse group of stakeholders, each recognizing the critical importance of data skills in their respective fields (Fotopoulou, 2021 ; Ghodoosi et al., 2024 ). In this sense, a substantial body of research highlights that educators are increasingly aware of the necessity for data literacy in enhancing teaching practices and improving educational outcomes (Kippers et al., 2018 ; Dodman et al., 2023 ). For instance, Kjelvik and Schultheis ( 2019 ) emphasize that data literacy is becoming a vital asset for students preparing for careers in various fields, indicating a growing interest among educational institutions to integrate data literacy into curricula. The interest in data literacy is also gaining traction in public policy and governance. Most data literacy research is conducted within formal learning environments, underscoring the role of educational institutions in this rising interest (Bowler & Shaw, 2024 ). This integration is crucial because it ensures that future educators enter the profession with a foundational understanding of data-driven decision-making. However, there exists a notable variability in how comprehensively data literacy is incorporated, as some programs offer stand-alone courses while others embed data literacy concepts within existing coursework (Dunlap & Piro, 2016 ). On the other hand, the literature reviewed conclude that many education students exhibit negative attitudes towards research methods, often stemming from a perceived marginalization of this curriculum area within their educational programs. Turner et al. ( 2018 ) report that students frequently struggle with research methods education, which they attribute to a lack of attention in college-based settings compared to university-based education. This marginalization results in students demonstrating negative dispositions towards research methodologies, despite recognizing their relevance in real-life contexts. In any case, when investigating student attitudes, the initial challenge is to define the concept of attitude itself (Vila & Rubio, 2016). The Theory of Reasoned Action (Fishbein & Ajzen, 1975 ) highlights the influence of attitudes and social pressure on behavior. Similarly, the Theory of Planned Behavior (Ajzen, 1991 ) emphasizes the roles of attitudes, subjective norms, and perceived behavioral control in shaping intentions. Other models, like Social Learning Theory (Bandura, 1977 ) address how inconsistencies in beliefs and behaviors drive change. Additionally, frameworks such as Dual Process Theories (Chaiken, 1980 ) explain the complexity of attitude formation through systematic and heuristic processing routes. Within this broad spectrum, the three-dimensional theory of attitude proposed by Rosenberg and Hovland ( 1960 ) stands out. This theory has been a foundational framework in psychology and education and has significantly shaped theoretical and empirical research. Their three-dimensional model has been also applied to studies on attitudes toward statistics, particularly among psychology and education students (e.g., Auzmendi, 1992 ; Gil-Flores, 1999 ). This model suggests that attitudes are predispositions to respond to stimuli with cognitive, emotional, and behavioral (conative) reactions, which offers a comprehensive understanding of attitude formation and expression. In general, there is a significant body of research internationally on university students’ attitudes toward scientific research (Fives & Gill, 2014 ; Papanastasiou & Schumacker, 2014 ; Böttcher-Oschmann & Thiel, 2018 ; Howard & Michael al., 2019; Byman, et al., 2020 ; Wishkoski, et al., 2022 ; Hidalgo, et al., 2023 ) and even tradition on statistics attitudes (Roberts & Bilderback, 1980 ; Wise, 1985 ; Darias-Morales, 2000 ; Méndez & Macía, 2007 ) the fact is that there is a considerably fewer studies explore research methodology as an academic discipline. Research methodology in the social sciences, particularly in the field of education, encompasses a broad spectrum of content that spans theoretical, procedural, and practical aspects. This includes an epistemological examination of various paradigms, approaches, techniques, and tools for conducting research (Fraenkel et al., 2012 ; Patton in 2015). Therefore, this gap underscores the importance of focusing research efforts on understanding how students engage with and comprehend methodological content, with the aim of identifying potential shortcomings and enhancing the teaching of these essential skills for their future professional growth (see Böttcher-Oschmann, et al., 2021 ). Surveys and questionnaires are commonly employed to evaluate student attitudes, typically utilizing appreciative scale items (like Likert-scale kind) to gauge participants’ agreement with specific methodological statements. For instance, Manning ( 2013 ) investigated Assessment Literacy among teachers through questionnaires to assess educators’ perspectives on assessment practices, indirectly reflecting their attitudes toward research methodologies in educational assessment. Similarly, Carmi et al. ( 2020 ) studying the data literacy mean and its relationship with digital media, employed a nationally representative survey of UK citizens. Other recent example is the paper by Oguguo et al. ( 2020 ) administering a survey to a Nigerian sample. Other techniques have been used like interviews or systematic reviews. In this vein, Carlson et all. (2011) conducted a semi-structures interview with faculty at two USA universities focused on three areas: the nature and lifecycle of the researchers’ data sets, their data management practices, and their needs for making their data available to others and curating their data for long-term access. Similarly, Sander ( 2023 ) developed a framework for critical datafication literacy conducting mixed methods that included experts’ interviews with creators of educational resources and qualitative survey with educators in teaching data technologies. On the other hand, Henderson and Corry ( 2020 ) carried out a systematic review to identify and analyze articles on data literacy education in ERIC, Education Sources and JSTOR databases. Although the arsenal of approaches and techniques is wider, screening assessment tests are usually crucial at the beginning of the assessment educational process. They play a crucial role in socio-educational interventions by enabling the early identification of at-risk individuals and informing tailored later strategies to enhance outcomes (Conley et al., 2014 ). In this context, the authors et al. ( 2024 ) developed a new scale specifically designed to assess attitudes toward research methodology (called ATRMQ) among education students, thereby expanding the tools available for exploring attitudes in this academic field. The scale was initially validated using reliability coefficients and Factor Analysis, as is commonly done in such studies. Nonetheless, this psychometric validation way based on Classical Test Theory (CTT) approach, it is recommended combine rather than more advanced frameworks such as Item Response Theory (IRT), by prestigious institutions (American Educational Research Association, American Psychological Association, and National Council on Measurement in Education, 2014). Therefore, this study aims to present a psychometric evaluation of the ATRMQ in a sample of education students from Andalusia (Spain). Specifically, the study assesses item difficulty, discrimination, and test information. Additionally, it examines the concurrent validity of the test by analyzing the relationship between test scores and specific criterion items. Based on the findings of this evaluation, an improved version of the scale may be proposed and further tested with a new sample if deemed necessary. Method This study uses a cross-sectional psychometric design to evaluate the validity and reliability of the Attitudes Toward Research Methodology Questionnaire (ATRMQ) in a sample of education students from Andalusia (Spain). A total of 377 undergraduate Education students from a public university participated in this voluntary study after providing informed consent. Participants’ ages ranged from 18 to 28 years, with an average of 20.91 (SD = 2.11) and a kurtosis of 3.34. Among them, 88.8% were female and 11.1% were male. The majority were enrolled in Pedagogy Degree (44.3%), followed by Early Childhood Education Degree (37.4%) and Social Education Degree (18.3%). Based on self-reported employment status, 54% combined work and study, while 45.47% were full-time students. The instrument to gather data was a questionnaire including the following sections was used: Sociodemographic Questions: data on age, gender, and employment status were collected. Academic Questions: the degree pursued was recorded, as well as the overall level of satisfaction with the degree and with the Research Methods (RM) academical content as criteria to analyze the convergence and divergence validity. Attitude to Research Methods questionnaire (ATRMQ). The ATRMQ is a version of the ATSQ questionnaire by Ordoñez, Romero, and Ruiz de Miguel (2016), which consists of 16 items grouped into 3 dimensions: negative emotion, positive emotion and utility attitude. The response options ranged from (1) strongly disagree, (2) disagree, (3) neither agree nor disagree, (4) agree to (5) strongly agree. An additional option, (6) I don’t know, was included to distinguish between respondents’ lack of knowledge and a neutral stance. In a sample of 447 higher education students from Andalusia, the ATRMQ revealed a coherent three-dimensional latent structure through Confirmatory Factor Analysis. Besides, a single secondary-order factor called “attitude” was identified. The questionnaire demonstrated satisfactory internal consistency, with Cronbach’s alpha of 0.897 and McDonald’s Omega of 0.898 (Authors et al. , 2024). The survey was conducted in person with all students. At the beginning of each data collection session, the research objectives and a brief overview of the test were explained to the students. Informed consent was obtained, and the confidentiality of the information collected was assured. After ensured enrollment, the instrument was read, it was administered to the participants. Data collection took place in October and November of the first semester of the 2022–2023 academic year, encompassing various degrees at the Faculty of Educational Sciences (Degree in Pedagogy, Degree in Early Childhood Education, and Degree in Social Education) at the Andalusian university. The research project strictly followed the ethical principles outlined in the Declaration of Helsinki, ensuring the dignity, rights, safety, and well-being of all participants. Comprehensive protocols were implemented to secure informed consent, protect privacy, and maintain confidentiality. Permission to administer the questionnaires was obtained by emailing teachers in the classrooms where data collection took place, providing detailed information about the research objectives and the nature of the data being collected. To prepare the data for analysis, items 5, 8, 10, 13, 14, 15, 17, 18, 19, and 20 from the ATRMQ (refer to annex A), were reverse-coded. Furthermore, responses that included the option “6—I don’t know” were excluded from the data matrix, getting then, the total number of sample (n = 377). Cases with more than 10% of unanswered scale items were then eliminated to ensure the integrity of the analysis. Subsequently, missing values were imputed using the Predictive Mean Matching (Rubin, 2004 ). The researchers tested the assumption that the scale measures a single dominant latent ability using a range of analytical techniques, including inter-item correlations, parallel analysis, Velicer’s minimum average partial test (Zwick & Velicer, 1986 ), and confirmatory factor analysis. To evaluate the CFA models’ overall fit to the data, it was employed the comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR). Achieving a combined cutoff threshold of CFI > = 0.95, TLI > = 0.95, RMSEA = < 0.08, SRMR = < 0.08 (Brown, 2015 ), and standardized factors loading higher than |0.400| (Hair et al., 2019 ) indicates that the items comprising the scale are effective indicators of the latent trait. A GRM was applied to the scale to obtain item discriminant/difficulty indices, eigenvalues, and to generate response option characteristic curves (OCCs) as well as information function curves for scales/items. Due to the polytomous and ordered categorical nature of the scale/items, the GRM was chosen as the preferred IRT model. For each item in the scale, 1 slope (discrimination, α) and 4 threshold (b) parameters were calculated. Higher α parameters denote items that more effectively distinguish between respondents’ levels. α values ranging from 0.65 to 1.34 indicate “moderate”, from 1.35 to 1.75 “high”, and greater than 1.76 “very high” discrimination (Baker, 2001 ). Item b parameters identified the point on the latent trait continuum where a respondent is most likely to select each response option, with higher b values indicating more “difficult” options. The hypothesis that the likelihood of choosing higher response options rises with an increasing latent trait (i.e., monotonicity) was evaluated using Mokken’s H scalability coefficient (Van der Ark, 2007 ) for each item and the overall scale. Monotonicity is confirmed if item scalability (Hi) coefficients are ≥ 0.3. Between 0.3 and 0.4 is considered weakly scalable, from over 0.4 up to 0.5 as moderated and higher 0.5 as strongly scalable (Mokken, 2011 ). To evaluate item goodness-of-fit in the GRM, we utilized the S-X² statistic (Kang & Chen’s, 2008) assessing how well the observed item response patterns align with the model, with non-significant p-values indicating good item fit. Additionally, the PV-Q1 index was employed to further examine item-level fit, particularly for identifying any potential outliers or problematic items (Chalmer & Ng’s, 2017). Local dependence (LD) between items was examined using LD statistics (Chen & Thissen, 1997 ). LD statistics |10| large (Waddimba et al., 2022 ). Finally, it was developed the reliability by McDonald’s Omega, and convergent and divergent validity by correlation between scores. Results Dimensionality Bartlett’s test of sphericity on test items was significant (Chi-square = 2631.995; p < 0.0001), indicating support for a single underlying latent factor. The KMO value for the scale was 0.91, and values for all items (from 0.80 for item I_17 to 0.96 for item I_5) getting the 0.800 threshold of sampling adequacy for factor analysis. In Velicer’s MAP test on the derivation data, the Eigenvalue of the first component (6.50) accounted for approximately 41% of the total variance. The second component, with an Eigenvalue of 1.64, accounted for 10% of the total variance, and the third component, with an Eigenvalue of 1.48, accounted for 9% of the total variance. Therefore, parallel analysis supported summarizing the test items with three components, indicating multidimensionality. So, behalf we have to consider the test a questionnaire composed of three scales indeed. Latent structure has been explored conducting a Exploratory Analysis Factor, using minimum residuals as method, and applying a oblimin rotation (see annex). Scale 1: Anxiety towards Research Methods. This scale groups items that reflect a negative and anxious attitude towards research methods. The items associated with this factor describe feelings of incapacity, nervousness, insecurity, and fear when facing research method problems. Items I_5, I_8, I_10, I_11, I_13, I_15, I_18, and I_20. Scale 2: Interest in Research Methods. Includes items that reflect a positive interest in using and discussing research methods. Participants scoring high on this factor are likely to find satisfaction in studying and applying research methods. Items I_6, I_7, I_9, and I_12. Scale 3: Perception of Uselessness and Difficulty in Research Methods. This scale groups items that reflect the perception that research methods are difficult, useless, or unnecessary in professional training. Participants scoring high on this factor are likely to perceive research methods as a complex subject with little practical relevance. Items I_14, I_16, I_17, and I_19. Confirmatory Factor Analyses (CFAs) conducted on the three scales model using the ULSMV estimator (considering the lack of normality in the variables) demonstrated an excellent fit to the data (SRMR = 0.048; CFI = 0.998; TLI = 0.998). Standardized factor loadings (λstandardized) for every item exceeded 0.593 (p < 0.001) for all variables. Regarding scales, item I_20 shows the highest loadings (λstandardized = 0.827) in the first scale, item I_6 (λstandardized = 0.828) in the second, and I_19 (λstandardized = 0.888) in the third. The CFA conducted independently on each of the three subscales using the ULSMV estimator demonstrated a good fit to the data across all models. In all cases, RMSEA were 0 and CFI were 1. For subscale 1, TLI was 1.006, and SRMR was 0.024. Similarly, subscale 2 yielded a TLI of 1.005, and SRMR of 0.011. For subscale 3, the was 1.002, and SRMR was 0.021. The consistently perfect fit indices across all subscales confirm the appropriateness of the one-factor model for each subscale, indicating that the items within each subscale reliably measure a single underlying construct. Score distribution and reliability Table 1 illustrates all item score distributions and correlations. Mean and standard deviation items scored range from a low of 2.29 (1.08) on item “I enjoy discussing research methods with others” (I_7) to a high of 4.30 (0.95) on item “Statistic is useless” (I_14). Annex lists the percentages with which respondents endorsed the items. Normality tests (conducting Shapiro-Wilk test and Anderson-Darling test) were all significant (p < .001), so the null hypothesis of normal distribution of item scores was rejected. On the other hand, inter-item polychoric correlations range from a low of 0.104 between items I_17 and I_8 to a high of 0.677 between items I_6 and I_7. Finally, among the three tests McDonald’s Omega was 0.881 for the first test, 0.853 for the second, and 0.753 for the third. Among items, deletion of any items does not cause any increase of Omegas for tests. Regarding subscales descriptives, the data reveals distinct patterns across them, with subscale 3 showing the highest levels of agreement and the most pronounced negative skewness (see Table 1 ). All subscales fail to meet the assumption of normality based on the Anderson-Darling test results (p < .001). Calibration In examining the Option Characteristic Curves (OCCs) in Fig. 1 , it becomes evident that certain items, such as I_20, I_10, and I_18, exhibit high discrimination. These items demonstrate well-separated curves that peak distinctly at various levels of the latent trait ( \(\:\theta\:\) ), indicating their effectiveness in differentiating respondents based on their levels of the latent construct. Conversely, items with curves that are less distinct or exhibit more overlap may not be as proficient in distinguishing between varying levels of the latent trait. Specifically, the response categories for I_17 are not as clearly separated, and the curves exhibit overlap clearly. This suggests that I_17 may not be as effective in discriminating between different levels of the latent trait. These interpretations are confirmed with the information shown in Table 2 . Table 2 Graded Response Model of items: item scalability, discrimination and difficulty parameters; and Item Goodness of items: S-X² and PV-Q1 Item H_i(SE) a(SE) b1(SE) b2(SE) b3(SE) b4(SE) S_X2 RMSEA.S_X2 PV_Q1 RMSEA.PV_Q1 Subescale 1 I_5 0.472 (0.027) 2.186 (0.201) -1.490 (0.133) -0.725 (0.094) 0.029 (0.079) 1.082 (0.108) 55.886 0.024 43.318 0.025 I_8 0.382 (0.034) 1.746 (0.165) -1.949 (0.176) -0.863 (0.110) 0.003 (0.087) 1.474 (0.143) 44.051 0.000 68.936* 0.051 I_10 0.479 (0.025) 2.816 (0.260) -1.391 (0.115) -0.467 (0.079) 0.400 (0.075) 1.305 (0.110) 48.367 0.027 76.677** 0.056 I_11 0.361 (0.035) 1.428 (0.146) -1.810 (0.186) -0.703 (0.113) 0.352 (0.101) 1.632 (0.173) 60.375 0.004 80.249** 0.059 I_13 0.382 (0.036) 2.008 (0.185) -1.529 (0.141) -0.621 (0.094) 0.275 (0.082) 1.347 (0.126) 69.765 0.032 551.497** 0.198 I_15 0.402 (0.031) 1.696 (0.163) -1.386 (0.141) -0.167 (0.090) 0.692 (0.101) 1.828 (0.174) 65.986 0.026 53.947 0.038 I_18 0.442 (0.032) 2.273 (0.205) -1.489 (0.129) -0.652 (0.090) 0.296 (0.079) 1.231 (0.114) 35.250 0.000 130.075** 0.085 I_20 0.479 (0.027) 2.784 (0.266) -1.481 (0.120) -0.847 (0.091) -0.058 (0.073) 0.583 (0.081) 52.469 0.024 43.361 0.025 Subscale 2 I_6 0.414 (0.029) 3.108 (0.328) -0.771 (0.087) 0.034 (0.072) 1.160 (0.097) 1.931 (0.148) 17.421 0.035 154.327** 0.095 I_7 0.387 (0.033) 3.457 (0.400) -0.592 (0.080) 0.207 (0.071) 1.368 (0.106) 1.942 (0.147) 11.851 0.000 137.516** 0.088 I_9 0.423 (0.030) 2.727 (0.271) -0.980 (0.098) -0.267 (0.077) 0.968 (0.093) 2.013 (0.158) 38.115** 0.068 219.680** 0.118 I_12 0.401 (0.034) 1.779 (0.179) -0.823 (0.110) -0.032 (0.087) 1.201 (0.124) 2.406 (0.228) 30.061 0.042 89.347** 0.064 Subscale 3 I_14 0.382 (0.033) 2.379 (0.313) -2.440 (0.229) -2.056 (0.182) -1.203 (0.114) -0.144 (0.079) 19.535 0.032 49.213 0.033 I_16 0.308 (0.036) 1.289 (0.163) -3.377 (0.411) -2.241 (0.255) -0.882 (0.134) 1.082 (0.150) 20.977 0.025 38.784 0.017 I_17 0.277 (0.038) 2.117 (0.254) -2.245 (0.210) -1.750 (0.156) -1.123 (0.112) 0.202 (0.085) 39.177* 0.048 84.174** 0.061 I_19 0.423 (0.034) 2.324 (0.288) -1.701 (0.150) -1.406 (0.127) -0.737 (0.091) 0.418 (0.086) 23.346 0.028 80.444** 0.059 Note: * : p < 0.01; ** : p < .001 Analyzing monotonicity, the Hi coefficient for each item reveals the quality within the scale to ordered people along trait levels. For instance, items I_20 and I_10 have the highest values (Hi = 0.479), indicating that they are the most effective at distinguishing between different levels of the latent trait being measured, while as item I_17 (Hi = 0.277), show weaker discrimination, which may warrant further review. The overall scale’s H coefficient is 0.404, reflecting moderate scalability, which suggests that the items collectively form a scale that reasonably discriminates among different levels of the latent trait. Considering the discrimination parameter of the items (alpha), it is possible to compare the results provided by the Hi coefficient. In this regard, it is observed that most items have good discriminative power within their corresponding subscales. For instance, item I_7 shows high discrimination (alpha = 3.457; SE = 0.400) between the latent trait levels of the participants in the corresponding subscale (subscale 2). The same applies to items I_6 and I_9, both within the same subscale 2. On the opposite side, items I_16 and I_17 have lower discrimination values (alpha = 1.289, SE = 0.163 and alpha = 2.117, SE = 0.254) in the subscale 3. Local independence is assessed by the absence of slope parameters higher than 4 and by small, standardized LD (S-X²) statistics for all item pairs. If these statistics are higher, it suggests that the items may be measuring something beyond the intended latent trait. The results indicate a possible dependence between items I_13 and I_18 ( Q₃ = -0.342), as well as between items I_13 and I_10 ( Q₃ = -0.313) within subscale 1. In subscale 2, items I_9 and I_7 also show potential dependence ( Q₃ = -0.221), with the rest of the item pairs having values between Q₃ = -0.166 (items I_7 and I_12) and Q₃ = -0.183 (items I_6 and I_7). In subscale 3, all item pairs appear to be independent, though the pair I_16 and I_17 shows a Q₃ value of -0.199, which may warrant further investigation. In Fig. 2 there are included the item information function and scale information tests. The analysis of the item information function (IIF) for the first scale reveals that most items contribute valuable insights across a broad spectrum of the latent trait continuum. The IIFs demonstrate peak information at different theta values, highlighting the effectiveness of these items in distinguishing respondents at various points along the latent trait scale. For instance, Item 1 (I_5), despite peaking around a theta value of 1.45, provides less information overall compared to other items, suggesting it may not differentiate as effectively across the entire range of responses. Similarly, Item 4 (I_11), with its peak around 0.63, offers relatively low information, indicating a weaker discriminatory power. On the other hand, Item 7 (I_18), although peaking at approximately 1.52, exhibits a flatter curve, implying that it does not provide distinct information across a wide range of theta values as robustly as other items. The IIF analysis for the second scale indicates that most items are highly effective in discriminating between different levels of the latent trait across a wide range of theta values. In contrast, the magnitude of information provided by each item varies. For example, Item 2 (I_7) shows the highest peak in information, making it particularly effective. In contrast, Item 4 (I_12) maintains a relatively consistent, though lower, level of information across a broader range of theta values, suggesting that while it contributes to the scale, it is less powerful in its discriminatory ability. Item 1 (I_6) provides substantial information across the latent trait continuum but peaks slightly lower than Item 2 (I_7), indicating that it is not as strong as the top-performing items. The IIF analysis for the third scale reveals that the items generally provide valuable information, though the magnitude and breadth of information vary among them. Item 1 (I_14) delivers consistent and substantial information across theta values, peaking at a relatively high level indicating strong discriminatory power. Item 3 (I_17) follows a similar pattern, albeit with slightly less overall information. Item 2 (I_16), while offering steady information, does so at a lower level, indicating it is somewhat less effective in distinguishing between respondents’ latent traits. Finally, Item 4 (I_19), despite peaking decently, exhibits a more rapid decline in information outside its peak range. Finally, the reliability analysis, assessed through McDonald’s Omega coefficient, revealed high internal consistency for both the total scale (ω = 0.94) and the individual subscales, with ω = 0.93 for subscale 1, ω = 0.89 for subscale 2, and ω = 0.85 for subscale 3. This suggests that the subscale items are consistently measuring their respective constructs. The convergent validity of the subscales is supported by significant correlations with the item I_1 (I like these subjects), which measures a positive attitude towards the subjects and is conceptually aligned with the subscales. Moderate to strong correlations between I_1 and the subscales (r = 0.41 for sub_1, r = 0.60 for sub_2, and r = 0.53 for sub_3) suggest that the subscales measure constructs related to interest and emotional reaction towards the subjects, indicating good convergent validity. In particular, the interest subscale (sub_2) shows the highest correlation with I_1 , reinforcing the idea that it effectively measures motivation and interest in the subjects. Regarding divergent validity, the item satis_general (general satisfaction) was used to assess whether the subscales measure constructs distinct from overall satisfaction with the academic degree. Weak correlations between satis_general and the subscales (r = 0.16 for sub_1, r = 0.18 for sub_2, and r = 0.19 for sub_3) support divergent validity, indicating that the subscales are measuring specific aspects related to anxiety, interest, and perceived uselessness, and not simply general satisfaction with the studies. Discussion The present study sought to gain a deeper understanding of the psychometric properties of a recently developed scale designed to measure education students’ attitudes toward their training in research methods, using Item Response Theory (IRT). This approach allowed for a more detailed analysis of the scale’s functioning at both the item and test levels, offering insights into how individual items contribute to the overall construct being measured. In addition to exploring these psychometric properties, the study aimed to identify ways to refine and improve the scale. By leveraging IRT, we could pinpoint items that performed well and those that might require revision or removal to enhance the scale’s precision and reliability. These findings not only contribute to the development of a more robust tool for assessing attitudes toward research methods but also offer broader implications for how attitude scales are constructed and validated in educational settings. This study aims to advance the application of methods like Item Response Theory in comparison to the traditional use of Classical Test Theory (CTT) in instrument validation. CTT remains more widely used for several reasons: it predates IRT and is more broadly disseminated (Hirose et al., 2014), it is perceived as more accessible, contrasting with IRT, which is seen as more complex in its implementation (Magno, 2009), and it typically requires fewer data points to generate reliable results (Andersen & Miller, 2020). However, IRT offers distinct advantages over CTT. One of the key limitations of CTT is that its parameters—such as item difficulty and trait estimation—are dependent on the specific sample used, meaning that results may vary between different groups of respondents. In contrast, IRT provides invariant estimates, allowing for consistent comparison across different samples. This invariance is crucial when aiming to create measurement tools that are generalizable across diverse populations (Hays et al., 2000). Furthermore, IRT has the added benefit of detecting differential item functioning (DIF), which helps identify whether specific items behave differently for subgroups of interest, such as by gender, age, or educational background. The importance of item and scale invariance in IRT lies at the core of its theoretical strengths over CTT. IRT’s ability to ensure that scores are interpreted consistently, regardless of the sample, offers a significant advantage in educational research and beyond. This makes IRT a more robust approach for developing tools that need to function reliably across various contexts. Studies by Adegoke (2013), Jabrayilov et al. (2016), Negash et al. (2021), and Sanchez-Rodriguez et al. (2022) highlight these theoretical and practical benefits, illustrating how IRT’s application can improve the quality and reliability of educational assessments. The overall performance of the items in the scale suggests that it holds promise for future studies, with most items demonstrating solid psychometric properties. These results indicate that the scale is likely to be useful for measuring education students’ attitudes toward research methods. However, a few items presented issues that warrant attention, particularly Items I_17 and I_16, which exhibited low discrimination. This means these items struggled to effectively distinguish between individuals with different levels of the attitude being measured, which is a key function of any reliable assessment tool. Additionally, Item I_17 also showed less differentiation, which further limits its utility in providing meaningful insights about respondents’ attitudes. Low item discrimination is a significant concern because it can undermine both the validity and reliability of the scale. When items fail to distinguish between respondents with varying levels of the targeted trait or ability, as was the case with I_17 and I_16, the overall measurement tool becomes less effective in providing accurate data (Ernst & Albers, 2017). For a scale to function properly, especially in educational contexts where precise measurement is crucial, items must reliably differentiate among individuals, which these two items struggled to achieve. In addition to discrimination issues, potential item dependencies were identified between Items I_13 and I_18, raising concerns about their impact on the scale’s psychometric properties. Item I_13 (“I feel insecure when solving research methods problems”) was intended to capture aspects related to vocative dimensions, such as how students perceive their own capabilities in research, which is closely related to self-efficacy (Birney et al., 2022). However, its connection to Item I_18 (“I feel frustrated when taking research methods tests”) suggests that these items might be too closely linked in terms of the emotions they evoke, thereby introducing potential redundancy or dependency in the scale. Item I_18 was included in the scale to explore the relationship between frustration and low examinee effort, which is an important factor in test performance (Wise & DeMars, 2010). However, the overlap between these two items could distort the assessment’s ability to measure distinct constructs accurately. Item dependencies are problematic because they can introduce several psychometric issues, including biased parameter estimates and inflated reliability, which in turn compromise the validity of the scale (Baghaei, 2007; Arydoust et al., 2021). These dependencies can create artificial relationships between items, leading to misleading conclusions about the underlying constructs being measured. Given these challenges, it is clear that while Items I_13 and I_18 provide valuable insights into student self-efficacy and effort, their inclusion in the scale would be more effective if paired with separate instruments specifically designed to measure these constructs. In light of the identified dependency issues, it may be necessary to reconsider the inclusion of these items or adjust their formulation to reduce overlap and ensure the scale’s overall reliability and validity for future applications. With regard to the subscales, the findings reveal some important insights about how different aspects of attitudes toward research methods are measured. 1. Anxiety toward Research Methods (Subscale 1) The items in this subscale exhibit good discriminatory power overall, but improvements could be made by addressing the issues with specific items, particularly I_17 (“Research methods are not useful for the average professional”), I_13 (“I feel insecure when solving research methods problems”), and I_18 (“I feel frustrated when taking research methods exams”). These items, which have been previously identified as problematic due to low discrimination and potential dependencies, may be detracting from the overall effectiveness of this subscale. Potential Pedagogical Implications: The high levels of anxiety reported in the sample regarding research methods could have significant implications for educational programs. This is consistent with findings in the literature that show a large proportion of university students experience anxiety related to statistics and mathematics, which negatively impacts academic performance (Peiró-Signes, 2021; Puklek & Cukon, 2022; Hunt et al., 2023). Given this, it may be necessary for educational interventions to incorporate strategies aimed at reducing students’ anxiety toward research methods, such as providing additional support through workshops or targeted training sessions. These interventions could help mitigate the negative effects of anxiety on learning outcomes, fostering a more positive learning environment for students. 2. Interest in Research Methods (Subscale 2) The items in this subscale show a high level of discriminatory power, indicating that the subscale effectively measures students’ interest in research methods. However, there is room for improvement with Item I_12 (“I would like to have a job where I need to use research methods”). Enhancing this item’s capacity to discriminate between varying levels of interest would further strengthen the robustness of the scale. The interest captured by this subscale is closely related to students’ intrinsic motivation. A higher level of intrinsic motivation often correlates with a more positive attitude toward learning in general, which opens up important avenues for future research. Understanding how intrinsic motivation influences students’ engagement with research methods could lead to more effective teaching strategies that foster deeper interest and long-term engagement with the subject. 3. Perception of Uselessness and Difficulty of Research Methods (Subscale 3) The results show that students tend to score high on items in this subscale, reflecting the widely held perception that research methods are difficult and, in some cases, seen as irrelevant or useless. This finding aligns with the existing literature, which highlights that many students view research methods as challenging or disconnected from their practical needs (Harland, 2014; Nind & Llewthwaite, 2017). These negative perceptions are critical to address, as they can influence students’ willingness to engage with and apply research methods in both academic and professional settings. Additionally, the perception of research methods as professionally irrelevant may be driven by a perceived lack of connection between academic training and real-world professional applications (Khan et al., 2009; Turner et al., 2018). This suggests a need for curriculum designers to emphasize the practical relevance of research methods, demonstrating to students how these skills can be applied in professional contexts. Strengthening the perceived value of research methods within career development frameworks could enhance students’ motivation to learn and apply these techniques effectively. Overall, the scale demonstrates good psychometric parameters, even with the presence of the problematic items discussed earlier. Despite the issues identified with Items I_17, I_13, and I_18, the scale’s structure remains solid, providing a reliable tool for measuring attitudes toward research methods among education students. The modifications suggested in this study would further enhance the scale’s precision and usefulness. The scale’s effectiveness is also supported by the strong correlation observed with the criterion items, indicating robust convergent validity and acceptable divergent validity. The low levels of correlation between general satisfaction and the feelings of anxiety, interest, or perceived uselessness measured by the subscales suggest that these constructs operate independently, as intended. This means that general satisfaction with research methods training does not heavily influence the specific attitudes being measured, further validating the distinctiveness of each subscale. After implementing the suggested modifications, the scale retains its three-dimensional structure, continuing to provide sufficient information to accurately estimate students’ attitudes toward research methods. These three dimensions—anxiety, interest, and perceptions of uselessness or difficulty—remain integral to understanding the diverse attitudes held by students and offer valuable insights for educators seeking to improve research methods training. Beyond the scale itself, this research aligns with a broader societal recognition of the growing importance of data literacy. There is an increasing emphasis on the need for data literacy at various levels of society, particularly within community organizations and global initiatives. For instance, the United Nations has highlighted the importance of developing data literacy as a tool to address inequalities and empower communities (Hannigan et al., 2023). This reflects a larger global movement toward enhancing data literacy skills, which goes beyond traditional educational contexts and permeates sectors such as public policy, social justice, and community engagement. Moreover, the growing complexity of data-driven decision-making within public administration has contributed to a heightened awareness of the need for data literacy among public actors (Pavone, et al., 2024). This underscores the relevance of tools like the current scale, not only within educational settings but also as part of a larger effort to cultivate data-literate citizens capable of navigating an increasingly data-centric world. Developing robust tools to measure attitudes toward data-driven methodologies is an essential step in fostering these skills across diverse populations. Conclusion This study has developed and validated a new scale for measuring education students’ attitudes toward research methods, using Item Response Theory (IRT) to refine and enhance its psychometric properties. The final scale structured around three dimensions—anxiety, interest, and perceptions of uselessness or difficulty—provides a reliable and robust tool for assessing attitudes. Despite the presence of a few items requiring modification, the overall scale demonstrates good discriminatory power and validity, making it suitable for use in both academic and professional settings. One of the key applications of this new scale is its potential use as an initial screening tool to identify populations with deficits in data literacy or research methods training. By administering this scale, educators and institutions can detect students who may struggle with these concepts early in their academic journey, allowing for the implementation of targeted interventions aimed at improving their skills and reducing anxiety toward research methods. Furthermore, this scale offers significant value for future research evaluating data literacy training programs. By measuring changes in attitudes toward research methods before and after such interventions, researchers can gain important insights into the effectiveness of these programs and the specific areas where students may need additional support. The scale thus serves not only as an assessment tool but also as a guide for improving educational practices related to data literacy, an increasingly important skill in today’s data-driven world. Declarations Conflict of Interests On behalf of all authors, the corresponding author states that there is no conflict of interest. Ethical Approval This article reports a secondary analysis of data collected as part of a previous research study conducted at a Spanish public university. The original data collection involved anonymous, self-administered questionnaires completed by adult university students. According to the internal regulations of the institutional ethics committee, studies based on anonymous surveys that do not collect sensitive personal data, nor involve any clinical, invasive or biological procedures, are exempt from prior ethical review. The original study met these criteria and was therefore not submitted for ethical approval at that time. The data used in this study are fully anonymised and available in an open-access repository [link redacted for peer review]. All procedures were conducted in accordance with the ethical standards of the 1964 Helsinki Declaration and its later amendments. Informed Consent Informed consent was obtained from all participants prior to data collection. 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Caballero","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYFACHsYDDAxyEPYHIrUwALUYg5mMM0jWwsxDjAZz9rMHDnxgMMjnn9388LFt2zZ5Bvb2B3i1WPbkJRycwWBgOePOMWPj3Lbbhg08ZwzwajE4kGNwmIfhjwHDjRw2aaAWxgaJHPwOMzj/BqTFwEAepMWy7bZ9g/xz/A4zuJED0WIA0sLYdjuxQYIBv8MsZ7wD+gWow/BGmrFhz7nbyW08Ofi1mPPnHnzwocLAQO5G8sMHP8pu2/azHyfgMCQSAtjwqkdTPApGwSgYBaMAOwAAIL9F/i2dcl4AAAAASUVORK5CYII=","orcid":"","institution":"University of Malaga","correspondingAuthor":true,"prefix":"","firstName":"Pablo","middleName":"Daniel Franco","lastName":"Caballero","suffix":""},{"id":497049938,"identity":"207915ab-45b9-4e05-b6a3-2f8b5416ab88","order_by":2,"name":"Lourdes Aranda","email":"","orcid":"","institution":"University of Malaga","correspondingAuthor":false,"prefix":"","firstName":"Lourdes","middleName":"","lastName":"Aranda","suffix":""},{"id":497049939,"identity":"76c5c2af-5711-41ae-8b1f-6deec60b76ea","order_by":3,"name":"Estela Isequilla Alarcón","email":"","orcid":"","institution":"University of Malaga","correspondingAuthor":false,"prefix":"","firstName":"Estela","middleName":"Isequilla","lastName":"Alarcón","suffix":""}],"badges":[],"createdAt":"2025-05-05 10:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6593380/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6593380/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88633900,"identity":"690baf44-8668-4d3b-8fa3-6a6ed59c70c6","added_by":"auto","created_at":"2025-08-08 14:37:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":109294,"visible":true,"origin":"","legend":"\u003cp\u003eOption Characteristic Curves for items\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6593380/v1/b6690d20b4a295a81200e926.png"},{"id":88633896,"identity":"e82550e7-2b2c-4741-b788-12fc43b5f1ef","added_by":"auto","created_at":"2025-08-08 14:37:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23404,"visible":true,"origin":"","legend":"\u003cp\u003eItem information curves for items (first column) and test information curves for scales (second column)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6593380/v1/4144d7d283c5206842860c82.png"},{"id":108180881,"identity":"5814d5b1-0abb-4ef5-90f8-15ead71dd786","added_by":"auto","created_at":"2026-04-30 08:54:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":578693,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6593380/v1/a35e56c6-91aa-4e1d-87fb-dbb069043575.pdf"},{"id":88633899,"identity":"d9b75b30-649a-4a01-b80c-2804bc70c3e4","added_by":"auto","created_at":"2025-08-08 14:37:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16770,"visible":true,"origin":"","legend":"","description":"","filename":"Annex.docx","url":"https://assets-eu.researchsquare.com/files/rs-6593380/v1/fb3466cc28d341efc63e0eeb.docx"},{"id":88633901,"identity":"8434b2b2-5b59-45bf-b99c-765d8a7f5549","added_by":"auto","created_at":"2025-08-08 14:37:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22008,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6593380/v1/e37cee3f2a5acafa69cf13da.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Psychometric validity and reliability of a methods of research attitude scale: an item response theory analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe interest in data literacy is indeed experiencing a significant rise, as evidenced by a variety of studies and reports across multiple sectors, including education, healthcare, business, and public policy (Nwagwu, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This increasing focus on data literacy is being driven by a diverse group of stakeholders, each recognizing the critical importance of data skills in their respective fields (Fotopoulou, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ghodoosi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this sense, a substantial body of research highlights that educators are increasingly aware of the necessity for data literacy in enhancing teaching practices and improving educational outcomes (Kippers et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dodman et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, Kjelvik and Schultheis (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) emphasize that data literacy is becoming a vital asset for students preparing for careers in various fields, indicating a growing interest among educational institutions to integrate data literacy into curricula. The interest in data literacy is also gaining traction in public policy and governance.\u003c/p\u003e\u003cp\u003eMost data literacy research is conducted within formal learning environments, underscoring the role of educational institutions in this rising interest (Bowler \u0026amp; Shaw, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This integration is crucial because it ensures that future educators enter the profession with a foundational understanding of data-driven decision-making. However, there exists a notable variability in how comprehensively data literacy is incorporated, as some programs offer stand-alone courses while others embed data literacy concepts within existing coursework (Dunlap \u0026amp; Piro, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, the literature reviewed conclude that many education students exhibit negative attitudes towards research methods, often stemming from a perceived marginalization of this curriculum area within their educational programs. Turner et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) report that students frequently struggle with research methods education, which they attribute to a lack of attention in college-based settings compared to university-based education. This marginalization results in students demonstrating negative dispositions towards research methodologies, despite recognizing their relevance in real-life contexts.\u003c/p\u003e\u003cp\u003eIn any case, when investigating student attitudes, the initial challenge is to define the concept of attitude itself (Vila \u0026amp; Rubio, 2016). The Theory of Reasoned Action (Fishbein \u0026amp; Ajzen, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1975\u003c/span\u003e) highlights the influence of attitudes and social pressure on behavior. Similarly, the Theory of Planned Behavior (Ajzen, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) emphasizes the roles of attitudes, subjective norms, and perceived behavioral control in shaping intentions. Other models, like Social Learning Theory (Bandura, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) address how inconsistencies in beliefs and behaviors drive change. Additionally, frameworks such as Dual Process Theories (Chaiken, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) explain the complexity of attitude formation through systematic and heuristic processing routes.\u003c/p\u003e\u003cp\u003eWithin this broad spectrum, the three-dimensional theory of attitude proposed by Rosenberg and Hovland (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1960\u003c/span\u003e) stands out. This theory has been a foundational framework in psychology and education and has significantly shaped theoretical and empirical research. Their three-dimensional model has been also applied to studies on attitudes toward statistics, particularly among psychology and education students (e.g., Auzmendi, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Gil-Flores, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). This model suggests that attitudes are predispositions to respond to stimuli with cognitive, emotional, and behavioral (conative) reactions, which offers a comprehensive understanding of attitude formation and expression.\u003c/p\u003e\u003cp\u003eIn general, there is a significant body of research internationally on university students\u0026rsquo; attitudes toward scientific research (Fives \u0026amp; Gill, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Papanastasiou \u0026amp; Schumacker, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; B\u0026ouml;ttcher-Oschmann \u0026amp; Thiel, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Howard \u0026amp; Michael al., 2019; Byman, et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wishkoski, et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hidalgo, et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and even tradition on statistics attitudes (Roberts \u0026amp; Bilderback, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Wise, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Darias-Morales, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; M\u0026eacute;ndez \u0026amp; Mac\u0026iacute;a, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) the fact is that there is a considerably fewer studies explore research methodology as an academic discipline. Research methodology in the social sciences, particularly in the field of education, encompasses a broad spectrum of content that spans theoretical, procedural, and practical aspects. This includes an epistemological examination of various paradigms, approaches, techniques, and tools for conducting research (Fraenkel et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Patton in 2015).\u003c/p\u003e\u003cp\u003eTherefore, this gap underscores the importance of focusing research efforts on understanding how students engage with and comprehend methodological content, with the aim of identifying potential shortcomings and enhancing the teaching of these essential skills for their future professional growth (see B\u0026ouml;ttcher-Oschmann, et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSurveys and questionnaires are commonly employed to evaluate student attitudes, typically utilizing appreciative scale items (like Likert-scale kind) to gauge participants\u0026rsquo; agreement with specific methodological statements. For instance, Manning (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) investigated Assessment Literacy among teachers through questionnaires to assess educators\u0026rsquo; perspectives on assessment practices, indirectly reflecting their attitudes toward research methodologies in educational assessment. Similarly, Carmi et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) studying the data literacy mean and its relationship with digital media, employed a nationally representative survey of UK citizens. Other recent example is the paper by Oguguo et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) administering a survey to a Nigerian sample.\u003c/p\u003e\u003cp\u003eOther techniques have been used like interviews or systematic reviews. In this vein, Carlson et all. (2011) conducted a semi-structures interview with faculty at two USA universities focused on three areas: the nature and lifecycle of the researchers\u0026rsquo; data sets, their data management practices, and their needs for making their data available to others and curating their data for long-term access. Similarly, Sander (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) developed a framework for critical datafication literacy conducting mixed methods that included experts\u0026rsquo; interviews with creators of educational resources and qualitative survey with educators in teaching data technologies. On the other hand, Henderson and Corry (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) carried out a systematic review to identify and analyze articles on data literacy education in ERIC, Education Sources and JSTOR databases.\u003c/p\u003e\u003cp\u003eAlthough the arsenal of approaches and techniques is wider, screening assessment tests are usually crucial at the beginning of the assessment educational process. They play a crucial role in socio-educational interventions by enabling the early identification of at-risk individuals and informing tailored later strategies to enhance outcomes (Conley et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this context, the authors et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) developed a new scale specifically designed to assess attitudes toward research methodology (called ATRMQ) among education students, thereby expanding the tools available for exploring attitudes in this academic field. The scale was initially validated using reliability coefficients and Factor Analysis, as is commonly done in such studies. Nonetheless, this psychometric validation way based on Classical Test Theory (CTT) approach, it is recommended combine rather than more advanced frameworks such as Item Response Theory (IRT), by prestigious institutions (American Educational Research Association, American Psychological Association, and National Council on Measurement in Education, 2014).\u003c/p\u003e\u003cp\u003eTherefore, this study aims to present a psychometric evaluation of the ATRMQ in a sample of education students from Andalusia (Spain). Specifically, the study assesses item difficulty, discrimination, and test information. Additionally, it examines the concurrent validity of the test by analyzing the relationship between test scores and specific criterion items. Based on the findings of this evaluation, an improved version of the scale may be proposed and further tested with a new sample if deemed necessary.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eThis study uses a cross-sectional psychometric design to evaluate the validity and reliability of the Attitudes Toward Research Methodology Questionnaire (ATRMQ) in a sample of education students from Andalusia (Spain).\u003c/p\u003e\u003cp\u003e A total of 377 undergraduate Education students from a public university participated in this voluntary study after providing informed consent. Participants\u0026rsquo; ages ranged from 18 to 28 years, with an average of 20.91 (SD\u0026thinsp;=\u0026thinsp;2.11) and a kurtosis of 3.34. Among them, 88.8% were female and 11.1% were male. The majority were enrolled in Pedagogy Degree (44.3%), followed by Early Childhood Education Degree (37.4%) and Social Education Degree (18.3%). Based on self-reported employment status, 54% combined work and study, while 45.47% were full-time students.\u003c/p\u003e\u003cp\u003eThe instrument to gather data was a questionnaire including the following sections was used:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eSociodemographic Questions: data on age, gender, and employment status were collected.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAcademic Questions: the degree pursued was recorded, as well as the overall level of satisfaction with the degree and with the Research Methods (RM) academical content as criteria to analyze the convergence and divergence validity.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAttitude to Research Methods questionnaire (ATRMQ). The ATRMQ is a version of the ATSQ questionnaire by Ordo\u0026ntilde;ez, Romero, and Ruiz de Miguel (2016), which consists of 16 items grouped into 3 dimensions: negative emotion, positive emotion and utility attitude. The response options ranged from (1) strongly disagree, (2) disagree, (3) neither agree nor disagree, (4) agree to (5) strongly agree. An additional option, (6) I don\u0026rsquo;t know, was included to distinguish between respondents\u0026rsquo; lack of knowledge and a neutral stance. In a sample of 447 higher education students from Andalusia, the ATRMQ revealed a coherent three-dimensional latent structure through Confirmatory Factor Analysis. Besides, a single secondary-order factor called \u0026ldquo;attitude\u0026rdquo; was identified. The questionnaire demonstrated satisfactory internal consistency, with Cronbach\u0026rsquo;s alpha of 0.897 and McDonald\u0026rsquo;s Omega of 0.898 (Authors et al.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e, 2024).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe survey was conducted in person with all students. At the beginning of each data collection session, the research objectives and a brief overview of the test were explained to the students. Informed consent was obtained, and the confidentiality of the information collected was assured.\u003c/p\u003e\u003cp\u003eAfter ensured enrollment, the instrument was read, it was administered to the participants. Data collection took place in October and November of the first semester of the 2022\u0026ndash;2023 academic year, encompassing various degrees at the Faculty of Educational Sciences (Degree in Pedagogy, Degree in Early Childhood Education, and Degree in Social Education) at the Andalusian university.\u003c/p\u003e\u003cp\u003e The research project strictly followed the ethical principles outlined in the Declaration of Helsinki, ensuring the dignity, rights, safety, and well-being of all participants. Comprehensive protocols were implemented to secure informed consent, protect privacy, and maintain confidentiality. Permission to administer the questionnaires was obtained by emailing teachers in the classrooms where data collection took place, providing detailed information about the research objectives and the nature of the data being collected.\u003c/p\u003e\u003cp\u003eTo prepare the data for analysis, items 5, 8, 10, 13, 14, 15, 17, 18, 19, and 20 from the ATRMQ (refer to annex A), were reverse-coded. Furthermore, responses that included the option \u0026ldquo;6\u0026mdash;I don\u0026rsquo;t know\u0026rdquo; were excluded from the data matrix, getting then, the total number of sample (n\u0026thinsp;=\u0026thinsp;377). Cases with more than 10% of unanswered scale items were then eliminated to ensure the integrity of the analysis. Subsequently, missing values were imputed using the Predictive Mean Matching (Rubin, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe researchers tested the assumption that the scale measures a single dominant latent ability using a range of analytical techniques, including inter-item correlations, parallel analysis, Velicer\u0026rsquo;s minimum average partial test (Zwick \u0026amp; Velicer, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), and confirmatory factor analysis. To evaluate the CFA models\u0026rsquo; overall fit to the data, it was employed the comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR).\u003c/p\u003e\u003cp\u003eAchieving a combined cutoff threshold of CFI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.95, TLI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.95, RMSEA\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.08, SRMR\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.08 (Brown, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and standardized factors loading higher than |0.400| (Hair et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) indicates that the items comprising the scale are effective indicators of the latent trait.\u003c/p\u003e\u003cp\u003eA GRM was applied to the scale to obtain item discriminant/difficulty indices, eigenvalues, and to generate response option characteristic curves (OCCs) as well as information function curves for scales/items. Due to the polytomous and ordered categorical nature of the scale/items, the GRM was chosen as the preferred IRT model.\u003c/p\u003e\u003cp\u003eFor each item in the scale, 1 slope (discrimination, α) and 4 threshold (b) parameters were calculated. Higher α parameters denote items that more effectively distinguish between respondents\u0026rsquo; levels. α values ranging from 0.65 to 1.34 indicate \u0026ldquo;moderate\u0026rdquo;, from 1.35 to 1.75 \u0026ldquo;high\u0026rdquo;, and greater than 1.76 \u0026ldquo;very high\u0026rdquo; discrimination (Baker, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Item b parameters identified the point on the latent trait continuum where a respondent is most likely to select each response option, with higher b values indicating more \u0026ldquo;difficult\u0026rdquo; options.\u003c/p\u003e\u003cp\u003eThe hypothesis that the likelihood of choosing higher response options rises with an increasing latent trait (i.e., monotonicity) was evaluated using Mokken\u0026rsquo;s H scalability coefficient (Van der Ark, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) for each item and the overall scale. Monotonicity is confirmed if item scalability (Hi) coefficients are \u0026ge;\u0026thinsp;0.3. Between 0.3 and 0.4 is considered weakly scalable, from over 0.4 up to 0.5 as moderated and higher 0.5 as strongly scalable (Mokken, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo evaluate item goodness-of-fit in the GRM, we utilized the S-X\u0026sup2; statistic (Kang \u0026amp; Chen\u0026rsquo;s, 2008) assessing how well the observed item response patterns align with the model, with non-significant p-values indicating good item fit. Additionally, the PV-Q1 index was employed to further examine item-level fit, particularly for identifying any potential outliers or problematic items (Chalmer \u0026amp; Ng\u0026rsquo;s, 2017). Local dependence (LD) between items was examined using LD statistics (Chen \u0026amp; Thissen, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). LD statistics \u0026lt;|5| are small/inconsequential, |5| to |10| moderate/questionable, and \u0026gt;|10| large (Waddimba et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Finally, it was developed the reliability by McDonald\u0026rsquo;s Omega, and convergent and divergent validity by correlation between scores.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eDimensionality\u003c/h2\u003e\n \u003cp\u003eBartlett\u0026rsquo;s test of sphericity on test items was significant (Chi-square\u0026thinsp;=\u0026thinsp;2631.995; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating support for a single underlying latent factor. The KMO value for the scale was 0.91, and values for all items (from 0.80 for item I_17 to 0.96 for item I_5) getting the 0.800 threshold of sampling adequacy for factor analysis.\u003c/p\u003e\n \u003cp\u003eIn Velicer\u0026rsquo;s MAP test on the derivation data, the Eigenvalue of the first component (6.50) accounted for approximately 41% of the total variance. The second component, with an Eigenvalue of 1.64, accounted for 10% of the total variance, and the third component, with an Eigenvalue of 1.48, accounted for 9% of the total variance. Therefore, parallel analysis supported summarizing the test items with three components, indicating multidimensionality.\u003c/p\u003e\n \u003cp\u003eSo, behalf we have to consider the test a questionnaire composed of three scales indeed. Latent structure has been explored conducting a Exploratory Analysis Factor, using minimum residuals as method, and applying a oblimin rotation (see annex).\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eScale 1: Anxiety towards Research Methods. This scale groups items that reflect a negative and anxious attitude towards research methods. The items associated with this factor describe feelings of incapacity, nervousness, insecurity, and fear when facing research method problems. Items I_5, I_8, I_10, I_11, I_13, I_15, I_18, and I_20.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eScale 2: Interest in Research Methods. Includes items that reflect a positive interest in using and discussing research methods. Participants scoring high on this factor are likely to find satisfaction in studying and applying research methods. Items I_6, I_7, I_9, and I_12.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eScale 3: Perception of Uselessness and Difficulty in Research Methods. This scale groups items that reflect the perception that research methods are difficult, useless, or unnecessary in professional training. Participants scoring high on this factor are likely to perceive research methods as a complex subject with little practical relevance. Items I_14, I_16, I_17, and I_19.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eConfirmatory Factor Analyses (CFAs) conducted on the three scales model using the ULSMV estimator (considering the lack of normality in the variables) demonstrated an excellent fit to the data (SRMR\u0026thinsp;=\u0026thinsp;0.048; CFI\u0026thinsp;=\u0026thinsp;0.998; TLI\u0026thinsp;=\u0026thinsp;0.998). Standardized factor loadings (\u0026lambda;standardized) for every item exceeded 0.593 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for all variables. Regarding scales, item I_20 shows the highest loadings (\u0026lambda;standardized\u0026thinsp;=\u0026thinsp;0.827) in the first scale, item I_6 (\u0026lambda;standardized\u0026thinsp;=\u0026thinsp;0.828) in the second, and I_19 (\u0026lambda;standardized\u0026thinsp;=\u0026thinsp;0.888) in the third. The CFA conducted independently on each of the three subscales using the ULSMV estimator demonstrated a good fit to the data across all models. In all cases, RMSEA were 0 and CFI were 1. For subscale 1, TLI was 1.006, and SRMR was 0.024. Similarly, subscale 2 yielded a TLI of 1.005, and SRMR of 0.011. For subscale 3, the was 1.002, and SRMR was 0.021. The consistently perfect fit indices across all subscales confirm the appropriateness of the one-factor model for each subscale, indicating that the items within each subscale reliably measure a single underlying construct.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eScore distribution and reliability\u003c/h3\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates all item score distributions and correlations. Mean and standard deviation items scored range from a low of 2.29 (1.08) on item \u0026ldquo;I enjoy discussing research methods with others\u0026rdquo; (I_7) to a high of 4.30 (0.95) on item \u0026ldquo;Statistic is useless\u0026rdquo; (I_14). Annex lists the percentages with which respondents endorsed the items. Normality tests (conducting Shapiro-Wilk test and Anderson-Darling test) were all significant (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), so the null hypothesis of normal distribution of item scores was rejected. On the other hand, inter-item polychoric correlations range from a low of 0.104 between items I_17 and I_8 to a high of 0.677 between items I_6 and I_7. Finally, among the three tests McDonald\u0026rsquo;s Omega was 0.881 for the first test, 0.853 for the second, and 0.753 for the third. Among items, deletion of any items does not cause any increase of Omegas for tests. Regarding subscales descriptives, the data reveals distinct patterns across them, with subscale 3 showing the highest levels of agreement and the most pronounced negative skewness (see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). All subscales fail to meet the assumption of normality based on the Anderson-Darling test results (p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\n\u003ch3\u003eCalibration\u003c/h3\u003e\n\u003cp\u003eIn examining the Option Characteristic Curves (OCCs) in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, it becomes evident that certain items, such as I_20, I_10, and I_18, exhibit high discrimination. These items demonstrate well-separated curves that peak distinctly at various levels of the latent trait (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\theta\\:\\)\u003c/span\u003e\u003c/span\u003e), indicating their effectiveness in differentiating respondents based on their levels of the latent construct. Conversely, items with curves that are less distinct or exhibit more overlap may not be as proficient in distinguishing between varying levels of the latent trait. Specifically, the response categories for I_17 are not as clearly separated, and the curves exhibit overlap clearly. This suggests that I_17 may not be as effective in discriminating between different levels of the latent trait. These interpretations are confirmed with the information shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGraded Response Model of items: item scalability, discrimination and difficulty parameters; and Item Goodness of items: S-X\u0026sup2; and PV-Q1\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH_i(SE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ea(SE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eb1(SE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eb2(SE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eb3(SE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eb4(SE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS_X2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSEA.S_X2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePV_Q1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSEA.PV_Q1\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubescale 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.472 (0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.186 (0.201)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.490 (0.133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.725 (0.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.029 (0.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.082 (0.108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI_8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.382 (0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.746 (0.165)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.949 (0.176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.863 (0.110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003 (0.087)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.474 (0.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.936*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI_10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.479 (0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.816 (0.260)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.391 (0.115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.467 (0.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.400 (0.075)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.305 (0.110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.677**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI_11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.361 (0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.428 (0.146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.810 (0.186)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.703 (0.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.352 (0.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.632 (0.173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.249**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI_13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.382 (0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.008 (0.185)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.529 (0.141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.621 (0.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.275 (0.082)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.347 (0.126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e551.497**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI_15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.402 (0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.696 (0.163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.386 (0.141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.167 (0.090)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.692 (0.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.828 (0.174)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI_18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.442 (0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.273 (0.205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.489 (0.129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.652 (0.090)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.296 (0.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.231 (0.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e130.075**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI_20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.479 (0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.784 (0.266)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.481 (0.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.847 (0.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.058 (0.073)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.583 (0.081)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubscale 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI_6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.414 (0.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.108 (0.328)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.771 (0.087)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034 (0.072)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.160 (0.097)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.931 (0.148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e154.327**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI_7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.387 (0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.457 (0.400)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.592 (0.080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.207 (0.071)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.368 (0.106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.942 (0.147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137.516**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI_9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.423 (0.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.727 (0.271)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.980 (0.098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.267 (0.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.968 (0.093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.013 (0.158)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.115**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e219.680**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI_12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.401 (0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.779 (0.179)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.823 (0.110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.032 (0.087)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.201 (0.124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.406 (0.228)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.347**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubscale 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI_14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.382 (0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.379 (0.313)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.440 (0.229)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.056 (0.182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.203 (0.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.144 (0.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI_16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.308 (0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.289 (0.163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.377 (0.411)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.241 (0.255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.882 (0.134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.082 (0.150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI_17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.277 (0.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.117 (0.254)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.245 (0.210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.750 (0.156)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.123 (0.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.202 (0.085)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.177*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.174**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI_19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.423 (0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.324 (0.288)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.701 (0.150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.406 (0.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.737 (0.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.418 (0.086)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.444**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eNote: * : p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ** : p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAnalyzing monotonicity, the Hi coefficient for each item reveals the quality within the scale to ordered people along trait levels. For instance, items I_20 and I_10 have the highest values (Hi\u0026thinsp;=\u0026thinsp;0.479), indicating that they are the most effective at distinguishing between different levels of the latent trait being measured, while as item I_17 (Hi\u0026thinsp;=\u0026thinsp;0.277), show weaker discrimination, which may warrant further review. The overall scale\u0026rsquo;s H coefficient is 0.404, reflecting moderate scalability, which suggests that the items collectively form a scale that reasonably discriminates among different levels of the latent trait.\u003c/p\u003e\n\u003cp\u003eConsidering the discrimination parameter of the items (alpha), it is possible to compare the results provided by the Hi coefficient. In this regard, it is observed that most items have good discriminative power within their corresponding subscales. For instance, item I_7 shows high discrimination (alpha\u0026thinsp;=\u0026thinsp;3.457; SE\u0026thinsp;=\u0026thinsp;0.400) between the latent trait levels of the participants in the corresponding subscale (subscale 2). The same applies to items I_6 and I_9, both within the same subscale 2. On the opposite side, items I_16 and I_17 have lower discrimination values (alpha\u0026thinsp;=\u0026thinsp;1.289, SE\u0026thinsp;=\u0026thinsp;0.163 and alpha\u0026thinsp;=\u0026thinsp;2.117, SE\u0026thinsp;=\u0026thinsp;0.254) in the subscale 3.\u003c/p\u003e\n\u003cp\u003eLocal independence is assessed by the absence of slope parameters higher than 4 and by small, standardized LD (S-X\u0026sup2;) statistics for all item pairs. If these statistics are higher, it suggests that the items may be measuring something beyond the intended latent trait. The results indicate a possible dependence between items I_13 and I_18 (\u003cem\u003eQ₃\u003c/em\u003e = -0.342), as well as between items I_13 and I_10 (\u003cem\u003eQ₃\u003c/em\u003e = -0.313) within subscale 1. In subscale 2, items I_9 and I_7 also show potential dependence (\u003cem\u003eQ₃\u003c/em\u003e = -0.221), with the rest of the item pairs having values between \u003cem\u003eQ₃\u003c/em\u003e = -0.166 (items I_7 and I_12) and \u003cem\u003eQ₃\u003c/em\u003e = -0.183 (items I_6 and I_7). In subscale 3, all item pairs appear to be independent, though the pair I_16 and I_17 shows a \u003cem\u003eQ₃\u003c/em\u003e value of -0.199, which may warrant further investigation.\u003c/p\u003e\n\u003cp\u003eIn Fig.\u0026nbsp;2 there are included the item information function and scale information tests. The analysis of the item information function (IIF) for the first scale reveals that most items contribute valuable insights across a broad spectrum of the latent trait continuum. The IIFs demonstrate peak information at different theta values, highlighting the effectiveness of these items in distinguishing respondents at various points along the latent trait scale.\u003c/p\u003e\n\u003cp\u003eFor instance, Item 1 (I_5), despite peaking around a theta value of 1.45, provides less information overall compared to other items, suggesting it may not differentiate as effectively across the entire range of responses. Similarly, Item 4 (I_11), with its peak around 0.63, offers relatively low information, indicating a weaker discriminatory power. On the other hand, Item 7 (I_18), although peaking at approximately 1.52, exhibits a flatter curve, implying that it does not provide distinct information across a wide range of theta values as robustly as other items.\u003c/p\u003e\n\u003cp\u003eThe IIF analysis for the second scale indicates that most items are highly effective in discriminating between different levels of the latent trait across a wide range of theta values. In contrast, the magnitude of information provided by each item varies.\u003c/p\u003e\n\u003cp\u003eFor example, Item 2 (I_7) shows the highest peak in information, making it particularly effective. In contrast, Item 4 (I_12) maintains a relatively consistent, though lower, level of information across a broader range of theta values, suggesting that while it contributes to the scale, it is less powerful in its discriminatory ability. Item 1 (I_6) provides substantial information across the latent trait continuum but peaks slightly lower than Item 2 (I_7), indicating that it is not as strong as the top-performing items.\u003c/p\u003e\n\u003cp\u003eThe IIF analysis for the third scale reveals that the items generally provide valuable information, though the magnitude and breadth of information vary among them. Item 1 (I_14) delivers consistent and substantial information across theta values, peaking at a relatively high level indicating strong discriminatory power. Item 3 (I_17) follows a similar pattern, albeit with slightly less overall information.\u003c/p\u003e\n\u003cp\u003eItem 2 (I_16), while offering steady information, does so at a lower level, indicating it is somewhat less effective in distinguishing between respondents\u0026rsquo; latent traits. Finally, Item 4 (I_19), despite peaking decently, exhibits a more rapid decline in information outside its peak range.\u003c/p\u003e\n\u003cp\u003eFinally, the reliability analysis, assessed through McDonald\u0026rsquo;s Omega coefficient, revealed high internal consistency for both the total scale (\u0026omega;\u0026thinsp;=\u0026thinsp;0.94) and the individual subscales, with \u0026omega;\u0026thinsp;=\u0026thinsp;0.93 for subscale 1, \u0026omega;\u0026thinsp;=\u0026thinsp;0.89 for subscale 2, and \u0026omega;\u0026thinsp;=\u0026thinsp;0.85 for subscale 3. This suggests that the subscale items are consistently measuring their respective constructs.\u003c/p\u003e\n\u003cp\u003eThe convergent validity of the subscales is supported by significant correlations with the item \u003cem\u003eI_1\u003c/em\u003e (I like these subjects), which measures a positive attitude towards the subjects and is conceptually aligned with the subscales. Moderate to strong correlations between \u003cem\u003eI_1\u003c/em\u003e and the subscales (r\u0026thinsp;=\u0026thinsp;0.41 for sub_1, r\u0026thinsp;=\u0026thinsp;0.60 for sub_2, and r\u0026thinsp;=\u0026thinsp;0.53 for sub_3) suggest that the subscales measure constructs related to interest and emotional reaction towards the subjects, indicating good convergent validity. In particular, the interest subscale (sub_2) shows the highest correlation with \u003cem\u003eI_1\u003c/em\u003e, reinforcing the idea that it effectively measures motivation and interest in the subjects.\u003c/p\u003e\n\u003cp\u003eRegarding divergent validity, the item satis_general (general satisfaction) was used to assess whether the subscales measure constructs distinct from overall satisfaction with the academic degree. Weak correlations between satis_general and the subscales (r\u0026thinsp;=\u0026thinsp;0.16 for sub_1, r\u0026thinsp;=\u0026thinsp;0.18 for sub_2, and r\u0026thinsp;=\u0026thinsp;0.19 for sub_3) support divergent validity, indicating that the subscales are measuring specific aspects related to anxiety, interest, and perceived uselessness, and not simply general satisfaction with the studies.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study sought to gain a deeper understanding of the psychometric properties of a recently developed scale designed to measure education students\u0026rsquo; attitudes toward their training in research methods, using Item Response Theory (IRT). This approach allowed for a more detailed analysis of the scale\u0026rsquo;s functioning at both the item and test levels, offering insights into how individual items contribute to the overall construct being measured.\u003c/p\u003e\n\u003cp\u003eIn addition to exploring these psychometric properties, the study aimed to identify ways to refine and improve the scale. By leveraging IRT, we could pinpoint items that performed well and those that might require revision or removal to enhance the scale\u0026rsquo;s precision and reliability. These findings not only contribute to the development of a more robust tool for assessing attitudes toward research methods but also offer broader implications for how attitude scales are constructed and validated in educational settings.\u003c/p\u003e\n\u003cp\u003eThis study aims to advance the application of methods like Item Response Theory in comparison to the traditional use of Classical Test Theory (CTT) in instrument validation. CTT remains more widely used for several reasons: it predates IRT and is more broadly disseminated (Hirose et al., 2014), it is perceived as more accessible, contrasting with IRT, which is seen as more complex in its implementation (Magno, 2009), and it typically requires fewer data points to generate reliable results (Andersen \u0026amp; Miller, 2020).\u003c/p\u003e\n\u003cp\u003eHowever, IRT offers distinct advantages over CTT. One of the key limitations of CTT is that its parameters\u0026mdash;such as item difficulty and trait estimation\u0026mdash;are dependent on the specific sample used, meaning that results may vary between different groups of respondents. In contrast, IRT provides invariant estimates, allowing for consistent comparison across different samples. This invariance is crucial when aiming to create measurement tools that are generalizable across diverse populations (Hays et al., 2000). Furthermore, IRT has the added benefit of detecting differential item functioning (DIF), which helps identify whether specific items behave differently for subgroups of interest, such as by gender, age, or educational background.\u003c/p\u003e\n\u003cp\u003eThe importance of item and scale invariance in IRT lies at the core of its theoretical strengths over CTT. IRT\u0026rsquo;s ability to ensure that scores are interpreted consistently, regardless of the sample, offers a significant advantage in educational research and beyond. This makes IRT a more robust approach for developing tools that need to function reliably across various contexts. Studies by Adegoke (2013), Jabrayilov et al. (2016), Negash et al.\u0026nbsp;(2021), and Sanchez-Rodriguez et al.\u0026nbsp;(2022) highlight these theoretical and practical benefits, illustrating how IRT\u0026rsquo;s application can improve the quality and reliability of educational assessments.\u003c/p\u003e\n\u003cp\u003eThe overall performance of the items in the scale suggests that it holds promise for future studies, with most items demonstrating solid psychometric properties. These results indicate that the scale is likely to be useful for measuring education students\u0026rsquo; attitudes toward research methods. However, a few items presented issues that warrant attention, particularly Items I_17 and I_16, which exhibited low discrimination. This means these items struggled to effectively distinguish between individuals with different levels of the attitude being measured, which is a key function of any reliable assessment tool. Additionally, Item I_17 also showed less differentiation, which further limits its utility in providing meaningful insights about respondents\u0026rsquo; attitudes.\u003c/p\u003e\n\u003cp\u003eLow item discrimination is a significant concern because it can undermine both the validity and reliability of the scale. When items fail to distinguish between respondents with varying levels of the targeted trait or ability, as was the case with I_17 and I_16, the overall measurement tool becomes less effective in providing accurate data (Ernst \u0026amp; Albers, 2017). For a scale to function properly, especially in educational contexts where precise measurement is crucial, items must reliably differentiate among individuals, which these two items struggled to achieve.\u003c/p\u003e\n\u003cp\u003eIn addition to discrimination issues, potential item dependencies were identified between Items I_13 and I_18, raising concerns about their impact on the scale\u0026rsquo;s psychometric properties. Item I_13 (\u0026ldquo;I feel insecure when solving research methods problems\u0026rdquo;) was intended to capture aspects related to vocative dimensions, such as how students perceive their own capabilities in research, which is closely related to self-efficacy (Birney et al., 2022). However, its connection to Item I_18 (\u0026ldquo;I feel frustrated when taking research methods tests\u0026rdquo;) suggests that these items might be too closely linked in terms of the emotions they evoke, thereby introducing potential redundancy or dependency in the scale. Item I_18 was included in the scale to explore the relationship between frustration and low examinee effort, which is an important factor in test performance (Wise \u0026amp; DeMars, 2010). However, the overlap between these two items could distort the assessment\u0026rsquo;s ability to measure distinct constructs accurately.\u003c/p\u003e\n\u003cp\u003eItem dependencies are problematic because they can introduce several psychometric issues, including biased parameter estimates and inflated reliability, which in turn compromise the validity of the scale (Baghaei, 2007; Arydoust et al., 2021). These dependencies can create artificial relationships between items, leading to misleading conclusions about the underlying constructs being measured. Given these challenges, it is clear that while Items I_13 and I_18 provide valuable insights into student self-efficacy and effort, their inclusion in the scale would be more effective if paired with separate instruments specifically designed to measure these constructs. In light of the identified dependency issues, it may be necessary to reconsider the inclusion of these items or adjust their formulation to reduce overlap and ensure the scale\u0026rsquo;s overall reliability and validity for future applications.\u003c/p\u003e\n\u003cp\u003eWith regard to the subscales, the findings reveal some important insights about how different aspects of attitudes toward research methods are measured.\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eAnxiety toward Research Methods (Subscale 1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe items in this subscale exhibit good discriminatory power overall, but improvements could be made by addressing the issues with specific items, particularly I_17 (\u0026ldquo;Research methods are not useful for the average professional\u0026rdquo;), I_13 (\u0026ldquo;I feel insecure when solving research methods problems\u0026rdquo;), and I_18 (\u0026ldquo;I feel frustrated when taking research methods exams\u0026rdquo;). These items, which have been previously identified as problematic due to low discrimination and potential dependencies, may be detracting from the overall effectiveness of this subscale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential Pedagogical Implications:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe high levels of anxiety reported in the sample regarding research methods could have significant implications for educational programs. This is consistent with findings in the literature that show a large proportion of university students experience anxiety related to statistics and mathematics, which negatively impacts academic performance (Peir\u0026oacute;-Signes, 2021; Puklek \u0026amp; Cukon, 2022; Hunt et al., 2023). Given this, it may be necessary for educational interventions to incorporate strategies aimed at reducing students\u0026rsquo; anxiety toward research methods, such as providing additional support through workshops or targeted training sessions. These interventions could help mitigate the negative effects of anxiety on learning outcomes, fostering a more positive learning environment for students.\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eInterest in Research Methods (Subscale 2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe items in this subscale show a high level of discriminatory power, indicating that the subscale effectively measures students\u0026rsquo; interest in research methods. However, there is room for improvement with Item I_12 (\u0026ldquo;I would like to have a job where I need to use research methods\u0026rdquo;). Enhancing this item\u0026rsquo;s capacity to discriminate between varying levels of interest would further strengthen the robustness of the scale.\u003c/p\u003e\n\u003cp\u003eThe interest captured by this subscale is closely related to students\u0026rsquo; intrinsic motivation. A higher level of intrinsic motivation often correlates with a more positive attitude toward learning in general, which opens up important avenues for future research. Understanding how intrinsic motivation influences students\u0026rsquo; engagement with research methods could lead to more effective teaching strategies that foster deeper interest and long-term engagement with the subject.\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003ePerception of Uselessness and Difficulty of Research Methods (Subscale 3)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results show that students tend to score high on items in this subscale, reflecting the widely held perception that research methods are difficult and, in some cases, seen as irrelevant or useless. This finding aligns with the existing literature, which highlights that many students view research methods as challenging or disconnected from their practical needs (Harland, 2014; Nind \u0026amp; Llewthwaite, 2017). These negative perceptions are critical to address, as they can influence students\u0026rsquo; willingness to engage with and apply research methods in both academic and professional settings.\u003c/p\u003e\n\u003cp\u003eAdditionally, the perception of research methods as professionally irrelevant may be driven by a perceived lack of connection between academic training and real-world professional applications (Khan et al., 2009; Turner et al., 2018). This suggests a need for curriculum designers to emphasize the practical relevance of research methods, demonstrating to students how these skills can be applied in professional contexts. Strengthening the perceived value of research methods within career development frameworks could enhance students\u0026rsquo; motivation to learn and apply these techniques effectively.\u003c/p\u003e\n\u003cp\u003eOverall, the scale demonstrates good psychometric parameters, even with the presence of the problematic items discussed earlier. Despite the issues identified with Items I_17, I_13, and I_18, the scale\u0026rsquo;s structure remains solid, providing a reliable tool for measuring attitudes toward research methods among education students. The modifications suggested in this study would further enhance the scale\u0026rsquo;s precision and usefulness.\u003c/p\u003e\n\u003cp\u003eThe scale\u0026rsquo;s effectiveness is also supported by the strong correlation observed with the criterion items, indicating robust convergent validity and acceptable divergent validity. The low levels of correlation between general satisfaction and the feelings of anxiety, interest, or perceived uselessness measured by the subscales suggest that these constructs operate independently, as intended. This means that general satisfaction with research methods training does not heavily influence the specific attitudes being measured, further validating the distinctiveness of each subscale.\u003c/p\u003e\n\u003cp\u003eAfter implementing the suggested modifications, the scale retains its three-dimensional structure, continuing to provide sufficient information to accurately estimate students\u0026rsquo; attitudes toward research methods. These three dimensions\u0026mdash;anxiety, interest, and perceptions of uselessness or difficulty\u0026mdash;remain integral to understanding the diverse attitudes held by students and offer valuable insights for educators seeking to improve research methods training.\u003c/p\u003e\n\u003cp\u003eBeyond the scale itself, this research aligns with a broader societal recognition of the growing importance of data literacy. There is an increasing emphasis on the need for data literacy at various levels of society, particularly within community organizations and global initiatives. For instance, the United Nations has highlighted the importance of developing data literacy as a tool to address inequalities and empower communities (Hannigan et al., 2023). This reflects a larger global movement toward enhancing data literacy skills, which goes beyond traditional educational contexts and permeates sectors such as public policy, social justice, and community engagement.\u003c/p\u003e\n\u003cp\u003eMoreover, the growing complexity of data-driven decision-making within public administration has contributed to a heightened awareness of the need for data literacy among public actors (Pavone, et al., 2024). This underscores the relevance of tools like the current scale, not only within educational settings but also as part of a larger effort to cultivate data-literate citizens capable of navigating an increasingly data-centric world. Developing robust tools to measure attitudes toward data-driven methodologies is an essential step in fostering these skills across diverse populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study has developed and validated a new scale for measuring education students\u0026rsquo; attitudes toward research methods, using Item Response Theory (IRT) to refine and enhance its psychometric properties. The final scale structured around three dimensions\u0026mdash;anxiety, interest, and perceptions of uselessness or difficulty\u0026mdash;provides a reliable and robust tool for assessing attitudes. Despite the presence of a few items requiring modification, the overall scale demonstrates good discriminatory power and validity, making it suitable for use in both academic and professional settings.\u003c/p\u003e\u003cp\u003eOne of the key applications of this new scale is its potential use as an initial screening tool to identify populations with deficits in data literacy or research methods training. By administering this scale, educators and institutions can detect students who may struggle with these concepts early in their academic journey, allowing for the implementation of targeted interventions aimed at improving their skills and reducing anxiety toward research methods.\u003c/p\u003e\u003cp\u003eFurthermore, this scale offers significant value for future research evaluating data literacy training programs. By measuring changes in attitudes toward research methods before and after such interventions, researchers can gain important insights into the effectiveness of these programs and the specific areas where students may need additional support. The scale thus serves not only as an assessment tool but also as a guide for improving educational practices related to data literacy, an increasingly important skill in today\u0026rsquo;s data-driven world.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn behalf of all authors, the corresponding author states that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article reports a secondary analysis of data collected as part of a previous research study conducted at a Spanish public university. The original data collection involved anonymous, self-administered questionnaires completed by adult university students. According to the internal regulations of the institutional ethics committee, studies based on anonymous surveys that do not collect sensitive personal data, nor involve any clinical, invasive or biological procedures, are exempt from prior ethical review. The original study met these criteria and was therefore not submitted for ethical approval at that time. The data used in this study are fully anonymised and available in an open-access repository [link redacted for peer review]. All procedures were conducted in accordance with the ethical standards of the 1964 Helsinki Declaration and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants prior to data collection. Participants were informed about the purpose of the study, that their participation was voluntary, that responses would remain anonymous and confidential, and that they could withdraw at any time. Consent was given by continuing to complete and submit the questionnaire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.M.-T.: Conceptualization, Formal analysis, Writing \u0026ndash; original draft.P.D.F.-C.: Validation, Writing \u0026ndash; review \u0026amp; editing.All authors: Review and approval of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated by the survey research during and/or analyzed during the current study are available in the Zenodo repository, at https://doi.org/10.5281/zenodo.10913022\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdegoke, B. A. (2013). 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J., Tulane, S., King, M. Q., Butler, K., \u0026amp; Woodland, L. A. (2022). Student attitudes toward research in an undergraduate social science research methods course.\u003cem\u003e Higher Education Pedagogies\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1), 20-36. https://doi.org/10.1080/23752696.2022.2072362\u003c/li\u003e\n\u003cli\u003eZwick, W. R., \u0026amp; Velicer, W. F. (1986). Comparison of five rules for determining the number of components to retain.\u003cem\u003e Psychological Bulletin\u003c/em\u003e, \u003cem\u003e99\u003c/em\u003e(3), 1-59. https://doi.org/10.1037/0033-2909.99.3.432\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Attitudes Toward Research Methodology, Psychometric Analysis, Item Response Theory (IRT), Education Students, Data Literacy","lastPublishedDoi":"10.21203/rs.3.rs-6593380/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6593380/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe acquisition of data literacy skills is crucial for enhancing teaching practices and educational outcomes. Given that many education students exhibit negative attitudes towards research methods, it is essential to develop tools that accurately assess these perceptions to foster positive changes. This study evaluates the psychometric properties of the Attitudes Toward Research Methodology Questionnaire (ATRMQ), a tool specifically designed to measure education students\u0026rsquo; attitudes toward research methodologies. The ATRMQ was administered to a sample of 377 undergraduate education students in Andalusia, Spain. Psychometric analysis, based on Item Response Theory (IRT), assessed reliability, validity, item difficulty, and discrimination indices within its three-dimensional structure: anxiety, interest, and perceived usefulness. Results indicate that the ATRMQ effectively distinguishes between levels of attitude towards research methodology, demonstrating high internal consistency across dimensions. Findings provide valuable insights for refining the ATRMQ, and an updated version is proposed. The ATRMQ offers a robust tool for identifying attitudinal barriers in research methodology training, with implications for curriculum design aimed at improving data literacy and methodological competence among future educators. The study underscores the value of IRT in enhancing scale precision over Classical Test Theory (CTT), advocating its application in educational assessments.\u003c/p\u003e","manuscriptTitle":"Psychometric validity and reliability of a methods of research attitude scale: an item response theory analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-08 14:37:50","doi":"10.21203/rs.3.rs-6593380/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":"797b92f6-515f-40cd-9f83-e0e87ecb9475","owner":[],"postedDate":"August 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52799852,"name":"Social science/Education"},{"id":52799853,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-04-08T05:41:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-08 14:37:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6593380","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6593380","identity":"rs-6593380","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}