Development and Validation of Perceived News Media Bias Scale

Development and Validation of Perceived News Media Bias Scale | 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Article Development and Validation of Perceived News Media Bias Scale Ming Liu, Yanchao Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6732818/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The study aimed to develop a scale to measure Chinese university students' perception of western news media bias. A pool of 14 original items was created through a literature review, and after expert review and revision, 8 items were retained. A total of 1998 valid participants (891 for EFA and 1107 for CFA) was collected from an independent college Hebei Province, China. Independent samples t-tests was conducted for item discrimination. EFA revealed two dimensions, editorial bias and reporting bias, with high internal consistency. CFA confirmed the scale's construct validity. Convergent validity and discriminant validity were satisfactory. Significant correlations were found between the scale and NMLS, indicating good concurrent validity. The scale showed full measurement invariance across birthplace but residual invariance across gender was not supported. The article discussed limitations related to sample homogeneity, absence of certain types of news, restricted sample selection method, and potential contextual dimension variation. Recommendations were then provided. In conclusion, the scale demonstrated good reliability and validity, providing an effective assessment tool for further research on media bias. Humanities/Cultural and media studies Humanities/Language and linguistics Social science/Education news media media bias scale development scale validation Figures Figure 1 Figure 2 Introduction In the era of globalization and informatization, English has become the universally recognized language for communication among people worldwide (Hu & Wu, 2020 ; Rao, 2019 ), facilitating interactions across borders and cultures. As a populous nation, China values English and actively promotes its widespread study (Haidar & Fang, 2019 ). In China, the Ministry of Education (MOE) emphasizes English language learning and use in university content courses (MOE, 2004 ) and English is a mandatory subject that all students are required to study for two years. Introduced in 1987 to evaluate the College English Syllabus, the CET Band 4 aims to enhance English language proficiency. After 2016, its listening section includes two audio materials with English news reports covering diverse topics. The use of audio-visual and print English news resources is crucial for supporting English learners in China. However, a pressing concern is the presence of bias in news content, given the widely acknowledged biased tendencies of the news media (Baron, 2006 ). Media bias refers to a structural flaw in news coverage, often intentional, that has the potential to influence public opinion (Domke et al., 1999 ). Bias in news reporting involves favouring certain perspectives or ideas, influencing public opinion, shaping narratives, and impacting societal perceptions. Including biased news materials in teaching, learning, and examinations raises concerns about potential risks. This study aims to create a comprehensive scale for assessing college students' perceptions of news media bias. Given the potential consequences of biased news content, providing learners with tool to critically evaluate information is crucial. The research seeks to contribute to English language education by developing a reliable and valid measurement instrument, enabling educators and researchers to assess students' awareness and comprehension of news media bias effectively. Implementing this scale is expected to provide deeper insights into students' perspectives on news media bias, fostering a more informed and discerning readership within the realm of English language education. Literature review Media Bias Bias is an inherent aspect of human information processing, and substantial evidence indicates that major news outlets often exhibit a general class bias that favours the existing social order (Gans, 1979 ). Even highly reputable newspapers like The Times are not immune to allegations of slant or bias in their reporting (Fritz et al., 2004 ; Kuypers, 2006 ). The media should be seen not just as a mirror reflecting reality but as a dynamic force shaping reality through its selection and portrayal of content (Gitlin, 2003 ). The media content has the capacity to shape audiences' perceptions by favouring one party or outcome over another (Eberl et al., 2017 ; Entman, 2007 ). In other words, instead of simply portraying the world as it is, the media is instrumental in shaping and influencing the public's perceptions, beliefs, and interpretations of reality. The deliberate selection of news stories, the framing of issues, and the emphasis placed on specific perspectives all contribute to the construction of a particular narrative or version of reality. This recognition underscores the significant impact that the media has on shaping societal understandings and highlights the need for critical engagement with media messages to develop a more nuanced and accurate comprehension of the world around us. Although bias is an inevitable outcome of the selection process and is inseparable from reporting (Entman, 2007 ), it has the potential to give certain actors and their narratives a greater level of influence compared to others (Eberl et al., 2017 ; Gentzkow et al., 2015 ). Effects of Media Bias Earlier research has highlighted the effect of media bias on shaping individual and public perceptions of news events (Ardèvol-Abreu & Gil de Zúñiga, 2017). As the primary source of political information for citizens (P. Norris, 2000 ), media bias can notably shape political beliefs, influence party preferences (Eberl et al., 2017 ), and alter voting behavior (DellaVigna & Kaplan, 2007 ). Additionally, biased information exposure can result in negative societal outcomes, such as increased group polarization, diminished tolerance for differing opinions, and political segregation (Spohr, 2017 ). Additionally, biased information can impact collective decision-making processes (Spinde, 2021 ). Types of Media Bias Aliprandini & Flynn ( 2011 ) believe that media bias can be divided into three categories: editorial bias, reporting bias, and situational bias. To begin with, editorial bias arises when the editorial staff of a news organization gives preferential treatment to specific stories over others. Various factors can contribute to this bias, such as the characteristics of the news outlet, the preferences of the editorial team, or the intended audience. Additionally, reporting bias is another form of media bias, which arises when a news story is presented with a particular slant or tone. Bias, whether deliberate or accidental, can shape the audience's perception of the story. Furthermore, situational bias represents another form of media bias, which arises when a news story fails to provide a comprehensive view of a situation. This can occur when the story overlooks relevant facts or perspectives, or when it relies on stereotypes. Previous research has not yet achieved a definitive agreement on the specific methodologies and metrics for quantifying bias (Eisinger et al., 2007 ). The subjective nature of bias and the intricacies associated with its evaluation present difficulties in establishing a universally recognized framework for measurement, thus the development of a standardized and widely accepted measurement approach for bias remains a topic of ongoing scholarly discussion and exploration. Therefore, the current study aims to create and develop a scale specifically designed to measure students' perception of news media bias. This endeavour will enable us to identify areas where students may be more susceptible to bias, and to develop interventions that can assist them in becoming more critical consumers of news. In addition, the scale can assess the effectiveness of interventions aimed at reducing bias and identify emerging trends. Furthermore, by gaining insight into how students perceive bias, we can develop more effective educational materials and programs. Methods Ethical considerations The study received ethical approval from the corresponding author's working unit (Ref. No. QGXYLL20230013) on 1 st June 2023, and the research was carried out from 30 th June 2023 to 4 th July 2023 in compliance with ethical standards of Declaration of Helsinki. During the data collection process, participants were presented with a written informed consent form embedded within the questionnaire. They were asked to indicate their agreement by clicking the "Agree" button before they could proceed with the questionnaire and provide their responses. Informed consent was obtained from all participants for participation in the study. To protect the privacy and security of student information, no personally identifiable details, such as names or student IDs, were collected. Participant selection and data cleaning Two rounds of data collection were conducted at a university in Hebei Province, China, using convenience sampling via Wenjuanxing, an online questionnaire platform. We flagged participants with an average response time below 2 seconds as careless and insufficient responding participants, who were excluded from further analysis (Huang et al., 2012). Two sets of samples were then obtained. The first data collection resulted in a sample of 891 students for exploratory factor analysis (EFA). The second data collection yielded a sample of 1107 students for confirmatory factor analysis (CFA). According to the guidelines provided by Nunnally, the two sample sizes employed in the study were deemed adequate, meeting the recommended 10:1 ratio of respondents per item (Nunnally, 1967). Table 1 presents the demographic details of the sample population. Table 1. Demographic details of the sample population Demographic Variable Group Frequency EFA Dataset CFA Dataset Student status Freshman 823 1105 Sophomore 65 1 Junior 2 1 Senior 1 0 Birthplace Urban 195 305 Rural 696 802 Gender Male 161 371 Female 230 736 Faculty FEIE 83 158 FE 109 174 FIL 456 234 FB 64 169 FPE 30 59 FFTAD 149 313 Total 891 1107 Abbreviation: FEIE=Faculty of Electrical and Information Engineering, FE=Faculty of Engineering, FIL= Faculty of International Languages, FB= Faculty of Business, FPE= Faculty of Physical Education, and FFTAD=Faculty of Film and Television and Art Design Instruments Perceived News Media Bias Scale (PNMBS) Following the recommendations of Rattray and Jones (2007) who stressed the significance of using three main sources for scale development: consulting experts, gathering input from potential respondents, and reviewing relevant literature. Considering the extensive existing literature on media bias, this study primarily relied on the review of previous research articles as the source of items. Based on the literature, we have originally derived 14 original items, as detailed in Appendix A. These items attempted to understand students' perception of different types of bias in news report. To enhance the scale's validity, feedback was sought from three news media professionals and three university instructors teaching Listening to News Report . They evaluated the items in terms of relevance, representativeness, and clarity. Based on their expert recommendations, some items were supplemented with examples, such as "(e.g., leftover women as portrayal of older unmarried women or the use of 'Kong Flu' by Trump to refer to the COVID-19 virus in a derogatory and racially insensitive manner, particularly targeting individuals of East Asian descent)." In addition, based on expert recommendations, 6items were deleted from the questionnaire. Please refer to Appendix A for the reasons behind the deletion of the item. The final scale consisted of 8 items, all of which were scored using a five-point Likert scale without any reverse-scoring. This rigorous process ensures that the scale possesses high quality and validity in measuring individuals' perceptions of news media bias. Following the completion of the scale, scores were calculated for each dimension, reflecting the level of perceived news media bias within that specific dimension. Higher scores on the scale indicate a greater perception of news media bias in the respective dimension. News Media Literacy Scale (NMLS) To get criterion validity evidence of the PNMBS, the study calculated the correlation coefficients between the PNMBS and the News Media Literacy Scale ( NMLS ). NMLS (Ashley et al., 2013) is a measurement tool to specifically access students’ critical news media literacy, which consists of three dimensions: authors and audiences (AA), messages and meanings (MM), and representation and reality (RR). Participants assessed all items using a five-point Likert scale, with 1 denoting NEVER and 5 denoting ALWAYS . A higher score on the scale indicated a greater news media literacy. In this study, the reported reliability coefficients for the AA, MM, and RR dimensions were 0.914, 0.939, and 0.901, respectively. Analytical procedure First, descriptive analysis was conducted. Second, independent samples t-tests were used to assess the scale's ability to distinguish high and low scores between the top 27% and the bottom 27%. Third, EFA was conducted on the dataset A to establish its factorial structure (Norris & Lecavalier, 2010). Fourth, a confirmatory factor analysis (CFA) was performed by using a different dataset. Fifth, convergent validity and discriminant validity were then analysed. Sixth, the correlations of the PNMBS with participants’ responses to NMLS (Ashley et al., 2013) were gathered as evidence for the criterion validity of the PNMBS. Finally, multi-group CFAs were carried out to assess measurement invariance across gender and birthplace. Results Descriptive Analysis Descriptive statistics such as mean, standard deviation, skewness, and kurtosis were analyzed with JASP to verify the data's normality. The skewness and kurtosis indices were within the acceptable cut-off values of |3.0| and |8.0|, respectively, suggesting a univariate normal distribution (Kline, 2015). Item discrimination Using JASP, an item discrimination analysis was performed, dividing participants into the top and bottom 27% based on their total scores on the 8-item scale. An independent-samples t-test compared the item scores between these two groups. Results showed significant differences for all 8 items, with the top 27% scoring significantly higher on each item. These differences were statistically significant at the 0.01 level, demonstrating that the scale items effectively distinguished between the lowest and highest scoring participants. Exploratory factor analysis The JASP software (JASP Team, 2024) was applied to conduct an Exploratory Factor Analysis (EFA) on the 8 items. The Kaiser-Meyer-Olkin (KMO) measure indicated a high sampling adequacy with a value of 0.933. Bartlett's Test of Sphericity was also significant, yielding χ2= 7416.615 (p < 0.001), which confirmed the suitability of the data for factor analysis. From the results of the parallel analysis and the scree plot shown in Figure 1, two factors emerged, together explaining 79.6% of the total variance. The first factor is named as Reporting Bias . It refers to the phenomenon where articles are crafted with a specific tone or "spin" to shape readers' perceptions and discourage scepticism. This bias is evident in word choice, labels, and omission in news articles. It also extends to the reporting style in audio or video news broadcasts. These items encompass evaluations of the selection of words utilized in news articles as well as the intonation and delivery style of news reporters in audio or video broadcasts. The second factor is named as Editorial Bias . It involves biases in event and story selection, as well as placement. Selection bias pertains to the deliberate coverage of specific stories while disregarding others, including visual or audio elements that influence audience perception. Placement bias refers to strategic positioning, timing, size, and duration of news stories, as well as sequencing of events within a story. This factor focuses on the subjective decisions made by editors and news organizations that shape the narrative and framing of news. The items assess students' perception of editorial bias and its impact on news presentation and interpretation. In the two-factor model, the loadings presented in Table 2 had absolute values ranging from 0.728 to 0.885 for Editorial Bias, which explained 31.2% of the variance. For Reporting Bias, the loadings ranged from 0.746 to 0.878, accounting for 48.3% of the variance. The Cronbach's Alpha Coefficients for Editorial Bias and Reporting Bias were 0.920 and 0.950, respectively. Table 2. Results of EFA of the 8‐item PNMBS Dimension Item Factor 1 Factor 2 Uniqueness Explained Variance Cronbach's Alpha Editorial Bias EB1 0.728 0.248 31.2 0.920 EB2 0.885 0.178 EB3 0.841 0.184 Reporting Bias RB1 0.794 0.181 48.3 0.950 RB2 0.746 0.241 RB3 0.863 0.195 RB4 0.878 0.230 RB5 0.838 0.176 Note. Applied rotation method is promax. Abbreviation: PNMBS=Perceived News Media Bias Scale Fig 1. Scree plot for the two extracted factors of Perceived News Media Bias Scale Confirmatory Factor Analysis Univariate and Multivariate normality To assess univariate normality, the absolute values of skewness (all below 3) and kurtosis (all below 10) shown in Table 3 indicated that the variables were not problematic (Kline, 2015). Consequently, the data were considered univariate normally distributed. For multivariate normality, Mardia’s coefficient was 162.969, which surpassed the threshold of 80 calculated by the formula p (p+2), where p is the number of observed variables in the model (Raykov & Marcoulides, 2008). Given that the data did not adhere to multivariate normality, we opted to report multivariate bootstrap inferences rather than those derived from maximum likelihood (ML). Table 3. Result of Univariate and Multivariate normality Variable skew c.r. kurtosis c.r. F1 0.545 7.403 0.559 3.793 F2 0.521 7.077 0.663 4.503 F3 0.672 9.127 0.406 2.760 F4 0.799 10.856 0.733 4.980 F5 0.624 8.479 0.895 6.081 F6 0.702 9.542 0.799 5.430 F7 0.710 9.648 0.810 5.502 F8 0.685 9.310 1.073 7.287 Multivariate 162.969 214.333 A confirmatory factor analysis (CFA) was carried out to confirm the two-factor structure of the PNMBS, as shown in Figure 2. The outcomes of the CFA, summarized in Table 4, demonstrated that all items were retained. The fit indices, which included χ²=132.593, df=19, χ²/df = 6.979, CFI = 0.987, TLI = 0.981, SRMR = 0.017, and RMSEA = 0.074 (90% CI, .062-.086), indicated that the hypothesized two-factor model aligned well with the observed data, suggesting a good fit. Fig 2. Results of the CFA of the two‐factor model of PNMBS Table4. Fit indices for the PIPUAS Indices χ² df χ²/df RMSEA SRMR CFI TLI Acceptable fit - - 3~5 <0.08 0.9 >0.9 Model 132.593 19 6.979 0.074 0.017 0.987 0.981 Abbreviations: PIPUAS=Perceived Internship PUA scale; RMSEA=Root mean square error of approximation; SRMR= Standardized Root Mean Squared Residual; CFI=Comparative fit index; and TLI= Tucker-Lewis Index Convergent validity To validate the convergent validity of the PNMBS involved calculating the standard factor loadings, composite reliability (CR), and average variance extracted (AVE). The recommended thresholds for these metrics are greater than 0.50, 0.70, and 0.50, respectively (Hair et al., 2010). As shown in Figure 2, all items exhibited sufficiently high loadings on their respective constructs, ranging from 0.86 to 0.91, exceeding the recommended minimum of 0.50. Additionally, the CR values for the two factors, presented in Table 5, were 0.920 and 0.948, both exceeding the 0.70 threshold. Furthermore, the AVE values for the two factors were 0.792 and 0.785, surpassing the 0.50 cut-off and being smaller than the CR values. These findings indicate that the PNMBS has acceptable convergent validity. Table 5. Convergent validity of confirmatory factor analysis No. of items CR AVE Editorial Bias 3 0.920 0.792 Reporting Bias 5 0.948 0.785 Abbreviations: CR=composite reliability, and AVE=average variance extracted Discriminant validity To assess the discriminant validity of the PNMBS, the Fornell-Larcker criterion (1981) was applied, which states that discriminant validity is confirmed when the squared correlations between constructs are lower than the average variance extracted (AVE) for each construct. As presented in Table 6, the square root of the AVE values, which are 0.890 for Editorial Bias and 0.886 for Reporting Bias, surpassed the off-diagonal correlation values between the sub-constructs. This indicates that the two factors are distinct, as their AVE values are greater than the correlations between constructs. Table 6. Fornell–Larcker discriminant validity of PNMBS Editorial Bias Reporting Bias Editorial Bias 0.890 Reporting Bias 0.876*** 0.886 Note: *** Correlation is significant at the 0.01 level (2-tailed). Concurrent validity The concurrent validity of the PNMBS was evaluated through Pearson correlation coefficients with the NMLS (Ashley et al., 2013). As shown in Table 7, the two PNMBS factors and the overall PNMBS score were found to have significant positive correlations with the NMLS. These results offer robust evidence supporting the concurrent validity of the PNMBS's two-factor structure. Table 7. Correlation coefficients between the PNMBS and the NMLS 1 2 3 4 5 6 7 Editorial Bias -- Reporting Bias .819 ** -- Media Bias .956 ** .952 ** -- AA .702 ** .798 ** .786 ** -- MM .706 ** .769 ** .773 ** .877 ** -- RR .636 ** .687 ** .693 ** .755 ** .870 ** -- NML .723 ** .797 ** .796 ** .931 ** .970 ** .928 ** -- Note: ** Correlation is significant at the 0.01 level (2-tailed). Abbreviations: PNMBS=Perceived News Media Bias Scale, NMLS= News Media Literacy Scale, AA= Authors and Audiences, MM=Messages and Meanings, RR=Representation and Reality, NML= News Media Literacy Multigroup CFAs To evaluate the measurement invariance of the PNMBS, the study conducted two rounds of multigroup confirmatory factor analysis (CFA) based on student gender and birthplace. As indicated in Tables 8 and 9, the measurement structure of the PNMBS showed no significant differences across different birthplaces, with changes in CFI and TLI less than 0.01 and RMSEA changes under 0.015. For the demographic variable of gender, the PNMBS demonstrated configural, metric, and scalar invariance, although it did not meet the criteria for residual invariance. Table 8. Results of measurement invariance across gender Model Measurement invariance and model comparison χ2 df χ2/df Δχ2 Δ df p CFI ΔCFI TLI ΔTLI RMSEA ΔRMSEA M1=Unconstrained — 136.704 26 5.258 — — — 0.985 — 0.976 — 0.062 — M2=Measurement weights Configural invariance=M2-M1 142.959 31 4.612 6.255 5.000 0.282 0.985 0.000 0.98 0.004 0.057 -0.005 M3=Measurement intercepts Metric invariance=M3-M2 155.263 38 4.086 12.304 7.000 0.091 0.985 0.000 0.983 0.003 0.053 -0.004 M4=Structural covariances Scalar invariance+M4-M3 170.029 41 4.147 14.766 3.000 0.002 0.983 -0.002 0.983 0.000 0.053 0.000 M5=Measurement residuals Residual Invariance=M5-M4 297.555 48 6.199 127.526 7.000 0.000 0.967 -0.016 0.971 -0.012 0.069 0.016 Table 9. Results of measurement invariance across birthplace Model Measurement invariance and model comparison χ2 df χ2/df Δχ2 Δdf p CFI ΔCFI TLI ΔTLI RMSEA ΔRMSEA M1=Unconstrained — 185.472 38 4.881 — — — 0.984 — 0.976 — 0.059 — M2=Measurement weights Configural invariance=M2-M1 188.438 44 4.283 2.966 6.000 0.813 0.984 0.000 0.98 0.004 0.055 -0.004 M3=Measurement intercepts Metric invariance=M3-M2 204.343 52 3.93 15.905 8.000 0.044 0.983 -0.001 0.982 0.002 0.051 -0.004 M4=Structural covariances Scalar invariance+M4-M3 221.026 55 4.019 16.683 3.000 0.001 0.982 -0.001 0.981 -0.001 0.052 0.001 M5=Measurement residuals Residual Invariance=M5-M4 341.455 63 5.42 120.429 8.000 0.000 0.969 -0.013 0.973 -0.008 0.063 0.011 Discussion The present study developed the Perceived News Media Bias Scale (PNMBS) to assess individuals' perceptions of bias in news media. The research process involved several steps, including item generation, expert validation, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA). The scale demonstrated robust psychometric properties. Initially, 14 items were created based on an extensive literature review on news media bias. Through expert feedback, six items were eliminated, and two items were revised to enhance participants' comprehension of the scale. Subsequently, EFA results revealed two factors: editorial bias and reporting bias. The reliability of the two factors was high, with Cronbach's alpha coefficients of 0.92 for editorial bias and 0.95 for reporting bias. Next, the CFA results confirmed the structural validity of the PNMBS, as indicated by various fit indices, which demonstrated a good fit between the hypothesized factor structure and the data. Furthermore, convergent validity was established through standard factor loadings, CR, and AVE. What’s more, discriminant validity was confirmed by comparing the square root values of the AVE with the off-diagonal correlation values between sub-constructs using the Fornell-Larcker criterion. Moreover, concurrent validity was supported by examining the relationships between the PNMBS and other relevant measures. Finally, the measurement invariance of the PNMBS was satisfactory across the birthplace of students. However, partial measurement invariance was observed across gender, specifically satisfying configural invariance, metric invariance, and scalar invariance. However, residual invariance was not achieved, indicating the presence of some systematic biases or differences between different gender groups that go beyond differences in factor loadings alone. Therefore, researchers need to exercise caution when interpreting results in gender comparisons or studies on gender differences. Implications Developing a scale to evaluate students' perception of news media bias holds significant implications in the realm of English language instruction, particularly when utilizing printed or audio-visual English original news reports that may exhibit media bias. Firstly, this scale can serve as a valuable instrument for fostering critical thinking skills among students. By prompting students to adopt a critical stance towards news media and question the information presented, the scale encourages the examination of diverse perspectives, the evaluation of evidence, and the formation of informed judgments. As a result, students' critical thinking abilities are enhanced. Secondly, the outcomes derived from the scale offer valuable insights into students' perceptions of news media bias. This information empowers educators to tailor instructional materials and activities to address specific areas of concern. By selecting news articles, videos, or other media resources that exemplify various types of bias, educators can facilitate targeted discussions and foster enhanced learning experiences. Thirdly, the development of a scale to evaluate students' perception of news media bias contributes to the promotion of media literacy skills among students. Through this scale, students are equipped with the ability to critically analyse and evaluate news sources, enabling them to identify potential biases and cultivate a more nuanced understanding of media messages. Limitations Despite the robustness of the scale, certain limitations should be considered when interpreting the results. First, the scale only considers news broadcasts and print media that resemble the format of news listening and reading comprehension commonly found in the College English Test Band 4 (CET-4) in China, failing to cover a wider spectrum of media forms such as news talk shows or interviews. Future studies could expand the range of media formats to include a broader variety of media types. This would enable a more comprehensive assessment of individuals' perceptions of media bias. What’s more, the scale primarily focuses on traditional news media and does not consider social media platforms. Given the rising importance of user-generated media, future research could expand the scale's scope to encompass a broader range of media types, including self-media, to assess perceptions of bias in a more comprehensive manner. In addition, the scale is limited to Western media materials and focuses solely on the English language. It does not account for other languages, such as Japanese, French, or individuals' native languages such as Chinese. Future studies could consider cross-cultural and multilingual comparisons to explore variations in media bias perceptions across different cultural and linguistic backgrounds. Second, the scale's validation and reliability were established using a specific sample, which may limit its generalizability to other populations. Future studies should consider using more diverse samples, including participants from different cultural backgrounds, age groups, and educational settings, to ensure the scale's applicability across various contexts. Third, using response time as a criterion to screen for careless responding participants has limitations as it only measures speed and attention, neglecting the quality of answers. Factors like pressure and individual differences can influence response time, leading to potential confounding effects. To address this, additional techniques can be employed, such as incorporating materials that involve media bias for participants to evaluate. Scores obtained from such evaluations can be used to calculate Mahalanobis distance, enabling the identification of participants who exhibit careless responding. Fourth, in the existing literature, three dimensions of news media bias were identified, but the final measurement instrument only incorporates two dimensions, omitting the situational bias dimension. This omission stemmed from the exclusion of specific items related to situational bias based on expert recommendations. The primary reasons for this omission were as follows: News materials students receive in class usually focus on reporting specific events, offering limited coverage of other actions or activities involving individuals mentioned in the news; the news materials students receive are typically supplementary content provided with textbooks or sourced from materials such as the College English Test Band 4 (CET-4) news listening or reading materials. Consequently, it is unlikely to find comparative reports on the same event from different organizations or individuals. To address this limitation in future research, the development of a measurement instrument for assessing university students' perceptions of bias in new media platforms, such as DouYin (Chinese version of TikTok) should be considered. In this new instrument, it is advisable to include a third dimension, "situational bias," alongside the existing dimensions. To achieve this, researchers can design and incorporate items that specifically capture situational bias within the context of new media consumption. This expansion would allow for a more comprehensive understanding of media bias and its impact on young adults' perceptions, thus contributing to a more holistic analysis of media influence in the digital age. By addressing these limitations and conducting future research as suggested, we can enhance the "Perception of News Media Bias" scale, ensuring its effectiveness and applicability in assessing individuals' perceptions of bias in news media. Conclusion This study built upon prior research on media bias and aimed to develop a robust measurement scale to assess college students' perception of media bias. It is important to acknowledge that the development of this scale was exploratory in nature, representing an initial step in understanding and measuring college students' perception of media bias. Consequently, future researchers are encouraged to undertake further examination and refinement of the scale in subsequent studies. In conclusion, the Perceived News Media Bias Scale (PNMBS) demonstrated strong psychometric properties and validity. The scale effectively captured the dimensions of editorial bias and reporting bias, providing a valuable tool for assessing individuals' perceptions of bias in news media. Declarations Author Contribution YYC was responsible for conceptualizing and drafting the manuscript and conducting data analysis, while LM handled the manuscript revisions. 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J Bus Psychol 27:99–114 JASP Team (2024) JASP (Version 0.19.0) Kline RB (2015) Principles and practice of structural equation modeling. Guilford Kuypers JA (2006) Bush’s war: Media bias and justifications for war in a terrorist age. Rowman & Littlefield MOE (2004) Notice on Issuing Teaching Requirements for College English Courses (Trial Implementation)关于印发《大学英语课程教学要求(试行)》的通知 . http://www.moe.gov.cn/s78/A08/tongzhi/201001/t20100129_124828.html Norris M, Lecavalier L (2010) Evaluating the use of exploratory factor analysis in developmental disability psychological research. J Autism Dev Disord 40(1):8–20 Norris P (2000) A virtuous circle: Political communications in postindustrial societies. Cambridge University Press Nunnally JC (1967) Psychometric theory. Psychometric theory. McGraw-Hill Rao PS (2019) The role of English as a global language. Res J Engl 4(1):65–79 Rattray J, Jones MC (2007) Essential elements of questionnaire design and development. J Clin Nurs 16(2):234–243 Raykov T, Marcoulides GA (2008) An introduction to applied multivariate analysis. Routledge Spinde T (2021) An interdisciplinary approach for the automated detection and visualization of media bias in news articles. 2021 International Conference on Data Mining Workshops (ICDMW) , 1096–1103 Spohr D (2017) Fake news and ideological polarization: Filter bubbles and selective exposure on social media. Bus Inform Rev 34(3):150–160 Appendix A Item pool and reasons for deleting some items Perceived News Media Bias Scale (PNMBS) Additional Declarations No competing interests reported. Supplementary Files EFAdataset.xlsx CFAdataset.xls Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 Mar, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers invited by journal 26 Jan, 2026 Editor invited by journal 27 Jun, 2025 Editor assigned by journal 02 Jun, 2025 Submission checks completed at journal 02 Jun, 2025 First submitted to journal 23 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6732818","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":581025924,"identity":"8ea485d7-aeb0-4026-874b-fae2bd4b942f","order_by":0,"name":"Ming Liu","email":"","orcid":"","institution":"Hebei Sport University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Liu","suffix":""},{"id":581025925,"identity":"656fe4a1-94fe-4abb-94f8-b617d42202fe","order_by":1,"name":"Yanchao Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACNvaGxAcf/9TI8bM3EKmFj+fAY8OZDceMJXsOEKlFTiLxmTRvA3PihhsJxDqMITlNcuYOtsSGm4833mCosYkmQsuxZIuPZ2SMG2enFVswHEvLbSCohbEn8eYMNjbZZukcMwnGhsNEaGHm/yDNw8bM2CZ5hlgtbAxJ0rxtzIo9EjzEauFhSDacceaYsQQP0C8JxPhFfv6DxAcfKmrk7I8f3njjQ40NYS3IwEAigRTlEC2k6hgFo2AUjIKRAQBzm0AFh23BsgAAAABJRU5ErkJggg==","orcid":"","institution":"Macau Millennium College","correspondingAuthor":true,"prefix":"","firstName":"Yanchao","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-05-23 12:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6732818/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6732818/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101459522,"identity":"2815029e-7ff4-4caf-9b5d-cbc4d74f9274","added_by":"auto","created_at":"2026-01-30 01:30:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92453,"visible":true,"origin":"","legend":"\u003cp\u003eScree plot for the two extracted factors of Perceived News Media Bias Scale\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6732818/v1/f13ac3d9271a2f8d5674d9fd.png"},{"id":101459523,"identity":"367b1b59-be37-4c9f-b1be-33932eeaa0a8","added_by":"auto","created_at":"2026-01-30 01:30:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":487212,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the CFA of the two‐factor model of PNMBS\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6732818/v1/3b9053f0effc8712add42b88.png"},{"id":101754975,"identity":"186d16c2-cdc0-4424-b8b2-de6fd8cf6971","added_by":"auto","created_at":"2026-02-03 10:48:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1277923,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6732818/v1/cf307997-5069-4aa6-9a85-6a2ec6cf065b.pdf"},{"id":101751740,"identity":"39f32e3f-6f2d-4115-b05b-9acb4c3a05fe","added_by":"auto","created_at":"2026-02-03 10:23:03","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":43695,"visible":true,"origin":"","legend":"","description":"","filename":"EFAdataset.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6732818/v1/6593200adca02fdb3ef87f28.xlsx"},{"id":101459524,"identity":"2050a4d1-b7f9-474f-a9cb-24f91f4cbc35","added_by":"auto","created_at":"2026-01-30 01:30:17","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":278528,"visible":true,"origin":"","legend":"","description":"","filename":"CFAdataset.xls","url":"https://assets-eu.researchsquare.com/files/rs-6732818/v1/97fa195c1c11af24d9f00c46.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of Perceived News Media Bias Scale","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the era of globalization and informatization, English has become the universally recognized language for communication among people worldwide (Hu \u0026amp; Wu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rao, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), facilitating interactions across borders and cultures. As a populous nation, China values English and actively promotes its widespread study (Haidar \u0026amp; Fang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In China, the Ministry of Education (MOE) emphasizes English language learning and use in university content courses (MOE, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and English is a mandatory subject that all students are required to study for two years. Introduced in 1987 to evaluate the College English Syllabus, the CET Band 4 aims to enhance English language proficiency. After 2016, its listening section includes two audio materials with English news reports covering diverse topics.\u003c/p\u003e \u003cp\u003eThe use of audio-visual and print English news resources is crucial for supporting English learners in China. However, a pressing concern is the presence of bias in news content, given the widely acknowledged biased tendencies of the news media (Baron, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Media bias refers to a structural flaw in news coverage, often intentional, that has the potential to influence public opinion (Domke et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Bias in news reporting involves favouring certain perspectives or ideas, influencing public opinion, shaping narratives, and impacting societal perceptions. Including biased news materials in teaching, learning, and examinations raises concerns about potential risks.\u003c/p\u003e \u003cp\u003eThis study aims to create a comprehensive scale for assessing college students' perceptions of news media bias. Given the potential consequences of biased news content, providing learners with tool to critically evaluate information is crucial. The research seeks to contribute to English language education by developing a reliable and valid measurement instrument, enabling educators and researchers to assess students' awareness and comprehension of news media bias effectively. Implementing this scale is expected to provide deeper insights into students' perspectives on news media bias, fostering a more informed and discerning readership within the realm of English language education.\u003c/p\u003e"},{"header":"Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMedia Bias\u003c/h2\u003e \u003cp\u003eBias is an inherent aspect of human information processing, and substantial evidence indicates that major news outlets often exhibit a general class bias that favours the existing social order (Gans, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Even highly reputable newspapers like \u003cem\u003eThe Times\u003c/em\u003e are not immune to allegations of slant or bias in their reporting (Fritz et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Kuypers, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The media should be seen not just as a mirror reflecting reality but as a dynamic force shaping reality through its selection and portrayal of content (Gitlin, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The media content has the capacity to shape audiences' perceptions by favouring one party or outcome over another (Eberl et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Entman, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In other words, instead of simply portraying the world as it is, the media is instrumental in shaping and influencing the public's perceptions, beliefs, and interpretations of reality. The deliberate selection of news stories, the framing of issues, and the emphasis placed on specific perspectives all contribute to the construction of a particular narrative or version of reality. This recognition underscores the significant impact that the media has on shaping societal understandings and highlights the need for critical engagement with media messages to develop a more nuanced and accurate comprehension of the world around us. Although bias is an inevitable outcome of the selection process and is inseparable from reporting (Entman, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), it has the potential to give certain actors and their narratives a greater level of influence compared to others (Eberl et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gentzkow et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEffects of Media Bias\u003c/h3\u003e\n\u003cp\u003eEarlier research has highlighted the effect of media bias on shaping individual and public perceptions of news events (Ard\u0026egrave;vol-Abreu \u0026amp; Gil de Z\u0026uacute;\u0026ntilde;iga, 2017). As the primary source of political information for citizens (P. Norris, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), media bias can notably shape political beliefs, influence party preferences (Eberl et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and alter voting behavior (DellaVigna \u0026amp; Kaplan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Additionally, biased information exposure can result in negative societal outcomes, such as increased group polarization, diminished tolerance for differing opinions, and political segregation (Spohr, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, biased information can impact collective decision-making processes (Spinde, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eTypes of Media Bias\u003c/h3\u003e\n\u003cp\u003eAliprandini \u0026amp; Flynn (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) believe that media bias can be divided into three categories: editorial bias, reporting bias, and situational bias. To begin with, editorial bias arises when the editorial staff of a news organization gives preferential treatment to specific stories over others. Various factors can contribute to this bias, such as the characteristics of the news outlet, the preferences of the editorial team, or the intended audience. Additionally, reporting bias is another form of media bias, which arises when a news story is presented with a particular slant or tone. Bias, whether deliberate or accidental, can shape the audience's perception of the story. Furthermore, situational bias represents another form of media bias, which arises when a news story fails to provide a comprehensive view of a situation. This can occur when the story overlooks relevant facts or perspectives, or when it relies on stereotypes.\u003c/p\u003e \u003cp\u003ePrevious research has not yet achieved a definitive agreement on the specific methodologies and metrics for quantifying bias (Eisinger et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The subjective nature of bias and the intricacies associated with its evaluation present difficulties in establishing a universally recognized framework for measurement, thus the development of a standardized and widely accepted measurement approach for bias remains a topic of ongoing scholarly discussion and exploration. Therefore, the current study aims to create and develop a scale specifically designed to measure students' perception of news media bias. This endeavour will enable us to identify areas where students may be more susceptible to bias, and to develop interventions that can assist them in becoming more critical consumers of news. In addition, the scale can assess the effectiveness of interventions aimed at reducing bias and identify emerging trends. Furthermore, by gaining insight into how students perceive bias, we can develop more effective educational materials and programs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received ethical approval from the corresponding author\u0026apos;s working unit (Ref. No. QGXYLL20230013) on 1\u003csup\u003est\u003c/sup\u003e June 2023, and the research was carried out from 30\u003csup\u003eth\u003c/sup\u003e June 2023 to 4\u003csup\u003eth\u003c/sup\u003e July 2023 in compliance with ethical standards of Declaration of Helsinki. During the data collection process, participants were presented with a written informed consent form embedded within the questionnaire. They were asked to indicate their agreement by clicking the \u0026quot;Agree\u0026quot; button before they could proceed with the questionnaire and provide their responses. Informed consent was obtained from all participants for participation in the study. To protect the privacy and security of student information, no personally identifiable details, such as names or student IDs, were collected.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipant selection and data cleaning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo rounds of data collection were conducted at a university in Hebei Province, China, using convenience sampling via Wenjuanxing, an online questionnaire platform. We flagged participants with an average response time below 2 seconds as careless and insufficient responding participants, who were excluded from further analysis (Huang et al., 2012).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwo sets of samples were then obtained. The first data collection resulted in a sample of 891 students for exploratory factor analysis (EFA). The second data collection yielded a sample of 1107 students for confirmatory factor analysis (CFA). According to the guidelines provided by Nunnally, the two sample sizes employed in the study were deemed adequate, meeting the recommended 10:1 ratio of respondents per item (Nunnally, 1967). Table 1 presents the demographic details of the sample population.\u003c/p\u003e\n\u003cp\u003eTable 1. Demographic details of the sample population\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eDemographic Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eEFA Dataset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eCFA Dataset\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eStudent status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eFreshman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e1105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eSophomore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eJunior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eSenior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eBirthplace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e802\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eFaculty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eFEIE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eFE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eFIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eFB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eFPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eFFTAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e313\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e1107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviation: FEIE=Faculty of Electrical and Information Engineering, FE=Faculty of Engineering, FIL= Faculty of International Languages, FB= Faculty of Business, FPE= Faculty of Physical Education, and FFTAD=Faculty of Film and Television and Art Design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstruments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceived News Media Bias Scale (PNMBS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the recommendations of Rattray and Jones (2007) who stressed the significance of using three main sources for scale development: consulting experts, gathering input from potential respondents, and reviewing relevant literature. Considering the extensive existing literature on media bias, this study primarily relied on the review of previous research articles as the source of items.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the literature, we have originally derived 14 original items, as detailed in Appendix A. These items attempted to understand students\u0026apos; perception of different types of bias in news report. To enhance the scale\u0026apos;s validity, feedback was sought from three news media professionals and three university instructors teaching \u003cem\u003eListening to News Report\u003c/em\u003e. They evaluated the items in terms of relevance, representativeness, and clarity. Based on their expert recommendations, some items were supplemented with examples, such as \u0026quot;(e.g., leftover women as portrayal of older unmarried women or the use of \u0026apos;Kong Flu\u0026apos; by Trump to refer to the COVID-19 virus in a derogatory and racially insensitive manner, particularly targeting individuals of East Asian descent).\u0026quot; In addition, based on expert recommendations, 6items were deleted from the questionnaire. Please refer to Appendix A for the reasons behind the deletion of the item.\u003c/p\u003e\n\u003cp\u003eThe final scale consisted of 8 items, all of which were scored using a five-point Likert scale without any reverse-scoring. This rigorous process ensures that the scale possesses high quality and validity in measuring individuals\u0026apos; perceptions of news media bias. Following the completion of the scale, scores were calculated for each dimension, reflecting the level of perceived news media bias within that specific dimension. Higher scores on the scale indicate a greater perception of news media bias in the respective dimension.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNews Media Literacy Scale (NMLS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo get criterion validity evidence of the PNMBS, the study calculated the correlation coefficients between the PNMBS and the News Media Literacy Scale (\u003cstrong\u003eNMLS\u003c/strong\u003e). \u003cstrong\u003eNMLS\u0026nbsp;\u003c/strong\u003e(Ashley et al., 2013) is a measurement tool to specifically access students\u0026rsquo; critical news media literacy, which consists of three dimensions: authors and audiences (AA), messages and meanings (MM), and representation and reality (RR). Participants assessed all items using a five-point Likert scale, with 1 denoting NEVER and 5 denoting \u003cem\u003eALWAYS\u003c/em\u003e. A higher score on the scale indicated a greater news media literacy. In this study, the reported reliability coefficients for the AA, MM, and RR dimensions were 0.914, 0.939, and 0.901, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytical procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, descriptive analysis was conducted. Second, independent samples t-tests were used to assess the scale\u0026apos;s ability to distinguish high and low scores between the top 27% and the bottom 27%. Third, EFA was conducted on the dataset A to establish its factorial structure (Norris \u0026amp; Lecavalier, 2010). Fourth, a confirmatory factor analysis (CFA) was performed by using a different dataset. Fifth, convergent validity and discriminant validity were then analysed. Sixth, the correlations of the PNMBS with participants\u0026rsquo; responses to NMLS\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(Ashley et al., 2013) were gathered as evidence for the criterion validity of the PNMBS. Finally, multi-group CFAs were carried out to assess measurement invariance across gender and birthplace.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics such as mean, standard deviation, skewness, and kurtosis were analyzed with JASP to verify the data\u0026apos;s normality. The skewness and kurtosis indices were within the acceptable cut-off values of |3.0| and |8.0|, respectively, suggesting a univariate normal distribution (Kline, 2015).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eItem discrimination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing JASP, an item discrimination analysis was performed, dividing participants into the top and bottom 27% based on their total scores on the 8-item scale. An independent-samples t-test compared the item scores between these two groups. Results showed significant differences for all 8 items, with the top 27% scoring significantly higher on each item. These differences were statistically significant at the 0.01 level, demonstrating that the scale items effectively distinguished between the lowest and highest scoring participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExploratory factor analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe JASP software (JASP Team, 2024) was applied to conduct an Exploratory Factor Analysis (EFA) on the 8 items. The Kaiser-Meyer-Olkin (KMO) measure indicated a high sampling adequacy with a value of 0.933. Bartlett\u0026apos;s Test of Sphericity was also significant, yielding \u0026chi;2= 7416.615 (p \u0026lt; 0.001), which confirmed the suitability of the data for factor analysis. From the results of the parallel analysis and the scree plot shown in Figure 1, two factors emerged, together explaining 79.6% of the total variance.\u003c/p\u003e\n\u003cp\u003eThe first factor is named as \u003cem\u003eReporting Bias\u003c/em\u003e. It refers to the phenomenon where articles are crafted with a specific tone or \u0026quot;spin\u0026quot; to shape readers\u0026apos; perceptions and discourage scepticism. This bias is evident in word choice, labels, and omission in news articles. It also extends to the reporting style in audio or video news broadcasts. These items encompass evaluations of the selection of words utilized in news articles as well as the intonation and delivery style of news reporters in audio or video broadcasts.\u003c/p\u003e\n\u003cp\u003eThe second factor is named as \u003cem\u003eEditorial Bias\u003c/em\u003e. It involves biases in event and story selection, as well as placement. Selection bias pertains to the deliberate coverage of specific stories while disregarding others, including visual or audio elements that influence audience perception. Placement bias refers to strategic positioning, timing, size, and duration of news stories, as well as sequencing of events within a story. This factor focuses on the subjective decisions made by editors and news organizations that shape the narrative and framing of news. The items assess students\u0026apos; perception of editorial bias and its impact on news presentation and interpretation.\u003c/p\u003e\n\u003cp\u003eIn the two-factor model, the loadings presented in Table 2 had absolute values ranging from 0.728 to 0.885 for Editorial Bias, which explained 31.2% of the variance. For Reporting Bias, the loadings ranged from 0.746 to 0.878, accounting for 48.3% of the variance. The Cronbach\u0026apos;s Alpha Coefficients for Editorial Bias and Reporting Bias were 0.920 and 0.950, respectively.\u003c/p\u003e\n\u003cp\u003eTable 2. Results of EFA of the 8‐item \u003cstrong\u003ePNMBS\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"690\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eDimension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eUniqueness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eExplained Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eCronbach\u0026apos;s Alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003eEditorial\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBias\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cem\u003eEB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.728\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.248\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cem\u003e31.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cem\u003eEB2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.885\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.178\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cem\u003eEB3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.841\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.184\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003eReporting\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBias\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cem\u003eRB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.794\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.181\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cem\u003e48.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cem\u003eRB2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.746\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.241\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cem\u003eRB3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.863\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.195\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cem\u003eRB4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.878\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.230\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cem\u003eRB5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.838\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.176\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;Note. Applied rotation method is promax.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviation: PNMBS=Perceived News Media Bias Scale\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFig 1. Scree plot for the two extracted factors of\u0026nbsp;Perceived News Media Bias Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfirmatory Factor Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate and Multivariate normality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess univariate normality, the absolute values of skewness (all below 3) and kurtosis (all below 10) shown in Table 3 indicated that the variables were not problematic (Kline, 2015). Consequently, the data were considered univariate normally distributed. For multivariate normality, Mardia\u0026rsquo;s coefficient was 162.969, which surpassed the threshold of 80 calculated by the formula p (p+2), where p is the number of observed variables in the model (Raykov \u0026amp; Marcoulides, 2008). Given that the data did not adhere to multivariate normality, we opted to report multivariate bootstrap inferences rather than those derived from maximum likelihood (ML).\u003c/p\u003e\n\u003cp\u003eTable 3. Result of Univariate and Multivariate normality\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"447\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eskew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003ec.r.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003ekurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003ec.r.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4.503\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e9.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eF4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e10.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4.980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eF5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e8.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e6.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eF6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e9.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5.430\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eF7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e9.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eF8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e9.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eMultivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e162.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e214.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA confirmatory factor analysis (CFA) was carried out to confirm the two-factor structure of the PNMBS, as shown in Figure 2. The outcomes of the CFA, summarized in Table 4, demonstrated that all items were retained. The fit indices, which included \u0026chi;\u0026sup2;=132.593, df=19, \u0026chi;\u0026sup2;/df = 6.979, CFI = 0.987, TLI = 0.981, SRMR = 0.017, and RMSEA = 0.074 (90% CI, .062-.086), indicated that the hypothesized two-factor model aligned well with the observed data, suggesting a good fit.\u003c/p\u003e\n\u003cp\u003eFig 2. Results of the CFA of the two‐factor model of PNMBS\u003c/p\u003e\n\u003cp\u003eTable4. Fit indices for the\u0026nbsp;PIPUAS\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"498\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eIndices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;/df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAcceptable fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3~5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e132.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: PIPUAS=Perceived Internship PUA scale; RMSEA=Root mean square error of approximation; SRMR= Standardized Root Mean Squared Residual; CFI=Comparative fit index; and TLI= Tucker-Lewis Index\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConvergent validity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the convergent validity of the PNMBS involved calculating the standard factor loadings, composite reliability (CR), and average variance extracted (AVE). The recommended thresholds for these metrics are greater than 0.50, 0.70, and 0.50, respectively (Hair et al., 2010). As shown in Figure 2, all items exhibited sufficiently high loadings on their respective constructs, ranging from 0.86 to 0.91, exceeding the recommended minimum of 0.50. Additionally, the CR values for the two factors, presented in Table 5, were 0.920 and 0.948, both exceeding the 0.70 threshold. Furthermore, the AVE values for the two factors were 0.792 and 0.785, surpassing the 0.50 cut-off and being smaller than the CR values. These findings indicate that the PNMBS has acceptable convergent validity.\u003c/p\u003e\n\u003cp\u003eTable 5. Convergent validity of confirmatory factor analysis\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"406\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eNo. of items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAVE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eEditorial Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eReporting Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;Abbreviations: CR=composite reliability, and AVE=average variance extracted\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscriminant validity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the discriminant validity of the PNMBS, the Fornell-Larcker criterion \u0026nbsp;(1981) was applied, which states that discriminant validity is confirmed when the squared correlations between constructs are lower than the average variance extracted (AVE) for each construct. As presented in Table 6, the square root of the AVE values, which are 0.890 for Editorial Bias and 0.886 for Reporting Bias, surpassed the off-diagonal correlation values between the sub-constructs. This indicates that the two factors are distinct, as their AVE values are greater than the correlations between constructs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6. Fornell\u0026ndash;Larcker discriminant validity of \u003cstrong\u003ePNMBS\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"355\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eEditorial Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eReporting Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eEditorial Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.890\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eReporting Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0.876***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.886\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote:\u0026nbsp;\u003c/em\u003e***\u003cem\u003eCorrelation is significant at the 0.01 level (2-tailed).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConcurrent validity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe concurrent validity of the PNMBS was evaluated through Pearson correlation coefficients with the NMLS (Ashley et al., 2013). As shown in Table 7, the two PNMBS factors and the overall PNMBS score were found to have significant positive correlations with the NMLS. These results offer robust evidence supporting the concurrent validity of the PNMBS\u0026apos;s two-factor structure.\u003c/p\u003e\n\u003cp\u003eTable 7. Correlation coefficients between the \u003cstrong\u003ePNMBS\u003c/strong\u003e and the NMLS\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eEditorial Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eReporting Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e.819\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eMedia Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e.956\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.952\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e.702\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.798\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.786\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e.706\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.769\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.773\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.877\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e.636\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.687\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.693\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.755\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.870\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eNML\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e.723\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.797\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.796\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.931\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.970\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.928\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: ** Correlation is significant at the 0.01 level (2-tailed).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: PNMBS=Perceived News Media Bias Scale, NMLS= News Media Literacy Scale, AA= Authors and Audiences, MM=Messages and Meanings, RR=Representation and Reality, NML= News Media Literacy\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultigroup CFAs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the measurement invariance of the PNMBS, the study conducted two rounds of multigroup confirmatory factor analysis (CFA) based on student gender and birthplace. As indicated in Tables 8 and 9, the measurement structure of the PNMBS showed no significant differences across different birthplaces, with changes in CFI and TLI less than 0.01 and RMSEA changes under 0.015. For the demographic variable of gender, the PNMBS demonstrated configural, metric, and scalar invariance, although it did not meet the criteria for residual invariance.\u003c/p\u003e\n\u003cp\u003eTable 8. Results of measurement invariance across gender\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"983\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMeasurement invariance and model comparison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026chi;2/df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026Delta;\u0026chi;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026Delta;\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026Delta;CFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026Delta;TLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026Delta;RMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eM1=Unconstrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e136.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e5.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eM2=Measurement weights\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eConfigural invariance=M2-M1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e142.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e4.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6.255\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e5.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eM3=Measurement intercepts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMetric invariance=M3-M2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e155.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e4.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e12.304\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e7.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eM4=Structural covariances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eScalar invariance+M4-M3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e170.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e4.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e14.766\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e3.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eM5=Measurement residuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eResidual Invariance=M5-M4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e297.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e6.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e127.526\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e7.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.016\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.012\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.016\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 9. Results of measurement invariance across birthplace\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"999\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eMeasurement invariance and model comparison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026chi;2/df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026Delta;\u0026chi;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026Delta;df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026Delta;CFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026Delta;TLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026Delta;RMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eM1=Unconstrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e185.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eM2=Measurement weights\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eConfigural invariance=M2-M1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e188.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.966\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eM3=Measurement intercepts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eMetric invariance=M3-M2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e204.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e15.905\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e8.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eM4=Structural covariances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eScalar invariance+M4-M3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e221.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e16.683\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eM5=Measurement residuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eResidual Invariance=M5-M4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e341.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e120.429\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e8.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.013\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.011\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study developed the Perceived News Media Bias Scale (PNMBS) to assess individuals' perceptions of bias in news media. The research process involved several steps, including item generation, expert validation, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA). The scale demonstrated robust psychometric properties.\u003c/p\u003e\n\u003cp\u003eInitially, 14 items were created based on an extensive literature review on news media bias. Through expert feedback, six items were eliminated, and two items were revised to enhance participants' comprehension of the scale.\u003c/p\u003e\n\u003cp\u003eSubsequently, EFA results revealed two factors: editorial bias and reporting bias. The reliability of the two factors was high, with Cronbach's alpha coefficients of 0.92 for editorial bias and 0.95 for reporting bias. Next, the CFA results confirmed the structural validity of the PNMBS, as indicated by various fit indices, which demonstrated a good fit between the hypothesized factor structure and the data.\u003c/p\u003e\n\u003cp\u003eFurthermore, convergent validity was established through standard factor loadings, CR, and AVE. What’s more, discriminant validity was confirmed by comparing the square root values of the AVE with the off-diagonal correlation values between sub-constructs using the Fornell-Larcker criterion. Moreover, concurrent validity was supported by examining the relationships between the PNMBS and other relevant measures.\u003c/p\u003e\n\u003cp\u003eFinally, the measurement invariance of the PNMBS was satisfactory across the birthplace of students. However, partial measurement invariance was observed across gender, specifically satisfying configural invariance, metric invariance, and scalar invariance. However, residual invariance was not achieved, indicating the presence of some systematic biases or differences between different gender groups that go beyond differences in factor loadings alone. Therefore, researchers need to exercise caution when interpreting results in gender comparisons or studies on gender differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDeveloping a scale to evaluate students' perception of news media bias holds significant implications in the realm of English language instruction, particularly when utilizing printed or audio-visual English original news reports that may exhibit media bias.\u003c/p\u003e\n\u003cp\u003eFirstly, this scale can serve as a valuable instrument for fostering critical thinking skills among students. By prompting students to adopt a critical stance towards news media and question the information presented, the scale encourages the examination of diverse perspectives, the evaluation of evidence, and the formation of informed judgments. As a result, students' critical thinking abilities are enhanced.\u003c/p\u003e\n\u003cp\u003eSecondly, the outcomes derived from the scale offer valuable insights into students' perceptions of news media bias. This information empowers educators to tailor instructional materials and activities to address specific areas of concern. By selecting news articles, videos, or other media resources that exemplify various types of bias, educators can facilitate targeted discussions and foster enhanced learning experiences.\u003c/p\u003e\n\u003cp\u003eThirdly, the development of a scale to evaluate students' perception of news media bias contributes to the promotion of media literacy skills among students. Through this scale, students are equipped with the ability to critically analyse and evaluate news sources, enabling them to identify potential biases and cultivate a more nuanced understanding of media messages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite the robustness of the scale, certain limitations should be considered when interpreting the results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst, the scale only considers news broadcasts and print media that resemble the format of news listening and reading comprehension commonly found in the College English Test Band 4 (CET-4) in China, failing to cover a wider spectrum of media forms such as news talk shows or interviews. Future studies could expand the range of media formats to include a broader variety of media types. This would enable a more comprehensive assessment of individuals' perceptions of media bias. What’s more, the scale primarily focuses on traditional news media and does not consider social media platforms. Given the rising importance of user-generated media, future research could expand the scale's scope to encompass a broader range of media types, including self-media, to assess perceptions of bias in a more comprehensive manner. In addition, the scale is limited to Western media materials and focuses solely on the English language. It does not account for other languages, such as Japanese, French, or individuals' native languages such as Chinese. Future studies could consider cross-cultural and multilingual comparisons to explore variations in media bias perceptions across different cultural and linguistic backgrounds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecond, the scale's validation and reliability were established using a specific sample, which may limit its generalizability to other populations. Future studies should consider using more diverse samples, including participants from different cultural backgrounds, age groups, and educational settings, to ensure the scale's applicability across various contexts.\u003c/p\u003e\n\u003cp\u003eThird, using response time as a criterion to screen for careless responding participants has limitations as it only measures speed and attention, neglecting the quality of answers. Factors like pressure and individual differences can influence response time, leading to potential confounding effects. To address this, additional techniques can be employed, such as incorporating materials that involve media bias for participants to evaluate. Scores obtained from such evaluations can be used to calculate Mahalanobis distance, enabling the identification of participants who exhibit careless responding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFourth, in the existing literature, three dimensions of news media bias were identified, but the final measurement instrument only incorporates two dimensions, omitting the situational bias dimension. This omission stemmed from the exclusion of specific items related to situational bias based on expert recommendations. The primary reasons for this omission were as follows: News materials students receive in class usually focus on reporting specific events, offering limited coverage of other actions or activities involving individuals mentioned in the news; the news materials students receive are typically supplementary content provided with textbooks or sourced from materials such as the College English Test Band 4 (CET-4) news listening or reading materials. Consequently, it is unlikely to find comparative reports on the same event from different organizations or individuals. To address this limitation in future research, the development of a measurement instrument for assessing university students' perceptions of bias in new media platforms, such as DouYin (Chinese version of TikTok) should be considered. In this new instrument, it is advisable to include a third dimension, \"situational bias,\" alongside the existing dimensions. To achieve this, researchers can design and incorporate items that specifically capture situational bias within the context of new media consumption. This expansion would allow for a more comprehensive understanding of media bias and its impact on young adults' perceptions, thus contributing to a more holistic analysis of media influence in the digital age.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy addressing these limitations and conducting future research as suggested, we can enhance the \"Perception of News Media Bias\" scale, ensuring its effectiveness and applicability in assessing individuals' perceptions of bias in news media.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study built upon prior research on media bias and aimed to develop a robust measurement scale to assess college students\u0026apos; perception of media bias. It is important to acknowledge that the development of this scale was exploratory in nature, representing an initial step in understanding and measuring college students\u0026apos; perception of media bias. Consequently, future researchers are encouraged to undertake further examination and refinement of the scale in subsequent studies. In conclusion, the Perceived News Media Bias Scale (PNMBS) demonstrated strong psychometric properties and validity. The scale effectively captured the dimensions of editorial bias and reporting bias, providing a valuable tool for assessing individuals\u0026apos; perceptions of bias in news media.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYYC was responsible for conceptualizing and drafting the manuscript and conducting data analysis, while LM handled the manuscript revisions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAliprandini M, Flynn SI (2011) Media Bias: An Overview. 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Bus Inform Rev 34(3):150\u0026ndash;160\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAppendix A Item pool and reasons for deleting some items\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerceived News Media Bias Scale (PNMBS)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"news media, media bias, scale development, scale validation","lastPublishedDoi":"10.21203/rs.3.rs-6732818/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6732818/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study aimed to develop a scale to measure Chinese university students' perception of western news media bias. A pool of 14 original items was created through a literature review, and after expert review and revision, 8 items were retained. A total of 1998 valid participants (891 for EFA and 1107 for CFA) was collected from an independent college Hebei Province, China. Independent samples t-tests was conducted for item discrimination. EFA revealed two dimensions, editorial bias and reporting bias, with high internal consistency. CFA confirmed the scale's construct validity. Convergent validity and discriminant validity were satisfactory. Significant correlations were found between the scale and NMLS, indicating good concurrent validity. The scale showed full measurement invariance across birthplace but residual invariance across gender was not supported. The article discussed limitations related to sample homogeneity, absence of certain types of news, restricted sample selection method, and potential contextual dimension variation. Recommendations were then provided. In conclusion, the scale demonstrated good reliability and validity, providing an effective assessment tool for further research on media bias.\u003c/p\u003e","manuscriptTitle":"Development and Validation of Perceived News Media Bias Scale","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-30 01:30:12","doi":"10.21203/rs.3.rs-6732818/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"303413906421597151459951503209569231219","date":"2026-03-08T18:40:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303413906421597151459951503209569231219","date":"2026-02-05T12:29:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-26T23:15:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-27T09:57:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-02T07:12:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-02T07:12:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-05-23T12:10:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9b2dd95b-f0a5-48c5-bf1e-cc5a18022c53","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61806478,"name":"Humanities/Cultural and media studies"},{"id":61806479,"name":"Humanities/Language and linguistics"},{"id":61806480,"name":"Social science/Education"}],"tags":[],"updatedAt":"2026-01-30T01:30:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-30 01:30:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6732818","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6732818","identity":"rs-6732818","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}