Development, Rasch Validation, and Confirmatory Factor Analysis of the Individual's Social Responsibility Scale

doi:10.21203/rs.3.rs-5287597/v1

Background: This study aimed to systematically develop and validate a measurement instrument for individual’s social responsibility (ISRS). It seeks to ensure robust psychometric properties and construct validity through Rasch model and confirmatory factor analysis (CFA).

Methods: This study employed a cross-sectional design and collected questionnaire responses from 812 individuals in Saudi Arabia through a random sample. The study collected data using demographic variables and the Arabic-language ISRS (16-items), developed and validated through Rasch analysis and CFA using the Wisntep (version 5.3.2) and AMOS (version 23) software, respectively.

The CFA results demonstrated a good fit for the model, with indices such as RMSEA (0.067) falling within the recommended range and CFI score of 0.96, exceeding the threshold of >0.95. Rasch analysis confirmed the reliability of the ISRS items and subscales, with adjustments made to the Likert scale for enhanced precision, based on the results of ISRS1, ISRS4-8, ISRS14, and ISRS16. Multidimensionality assessments revealed significant variations in explained variances, with participant hierarchy analyses supporting efficient test item targeting.

This study emphasizes the significance of community engagement, personal commitment, and social concern in fostering individual social responsibility among Saudi Arabian individuals, providing valuable insights for future research and interventions aimed at promoting social responsibility in the region.

Individual Social Responsibility

Saudi Arabia

Confirmatory Factor Analysis

Rasch Analysis

Demographic Variables

For decades, researchers have studied corporate social responsibility (CSR), which is important for academia and firms globally. CSR programs in organizations defend against reputation scandals and other undesirable outcomes. An organization's social contributions boost financial performance (1, 2). In recent decades, much responsibility-related research has focused on organizational benefits such as improved consumer behavior, reputation, and customer commitment (3–5). In later CSR studies, the micro-perspective focused on how CSR psychologically affects stakeholders, specifically employees' views on CSR (6). This contrasts with meta-analyses of employee outcomes, including identification, work satisfaction, and organizational citizenship behavior (1).

CSR has arisen as a reaction to growing societal expectations for public and private organizations to be more accountable, ethical, transparent, and reputable (7). Nevertheless, the implementation of these corporate strategies is contingent upon the simultaneous development of individual responsible behaviors that are in line with the stated principles and values that are increasingly influential in the social and economic domains (8). After the government and businesses, citizens are regarded as the third most important group in terms of their responsibility to society, meeting the needs of the environment, and helping resolve the crisis. This understanding of the role that citizens play in creating a sustainable society is a synchronous evolution of the corporate system and is not independent of it (9).

In sociology and psychology research, scales such as the one developed encompassing dimensions such as community engagement, social responsibility, belongingness and pride, and goal alignment are of paramount significance (10, 11). They serve as essential tools for quantifying and evaluating intricate social constructs, particularly individual behaviors and attitudes towards their communities. By utilizing such scales, researchers have developed a structured method to measure and analyze the multifaceted nature of social responsibility, offering insights into the interconnections between personal values, community engagement, and alignment with communal goals (12, 13). These instruments allow for a systematic exploration of human behaviors and attitudes in societal contexts, aiding in the identification of patterns, trends, and correlations within social interactions (14). Consequently, these scales play a pivotal role in advancing our understanding of human behavior and social dynamics, contributing to a comprehensive study of individual roles within communities (15, 16).

Existing literature lacks a comprehensive and validated scale for measuring an individual's social responsibility (ISRS). While various studies have explored aspects of social responsibility, there is a notable gap in the availability of a standardized tool that can accurately and reliably assess an individual's social responsibility across different dimensions. A previous research project formulated the concept of personal social responsibility by drawing upon the principles of consumers and CSR (17). A previous study introduced the notion of Personal Social Responsibility (PSR) as an innovative concept that expands on the ideas of CSR and Consumer Social Responsibility. This provides a conceptual framework to establish a basis for future empirical investigations. This study was grounded in a conceptual framework that encompasses the fundamental aspects of social responsibility, such as community engagement, social responsibility, belonging and pride, and goal alignment. These concepts serve as the foundation for creating items that encapsulate the core of social responsibility in individuals. Therefore, the primary objective of this research is to develop and validate using the Rasch model and conduct confirmatory factor analysis on the ISRS, aiming to create a comprehensive tool that captures the multidimensional nature of social responsibility in individuals. This study sought to validate the scale through Rasch analysis to ensure its psychometric properties and reliability, and then conducted confirmatory factor analysis to confirm its underlying structure and assess construct validity. The intended outcome is to provide a reliable and valid instrument capable of measuring ISR across diverse contexts, thus contributing to the literature by addressing the research gap in social responsibility assessment and offering a standardized tool for future research and practical applications.

Study design and sample size

A cross-sectional strategy was selected. The study instruments, comprising the demographic information form and the Individual Social Responsibility Scale, were electronically prepared using Google Forms. Participants were selected through snowball sampling by distributing invitations via email and social media platforms (18). A sample size of ten individuals per item was recommended for the Rasch analysis (19). Simulation studies indicate that a sample size of N = 150 is appropriate for a Confirmatory Factor Analysis (CFA) model with variables that follow a normal distribution and no missing data (20). The research sample consisted of 813 individuals from both private and government departments in Saudi Arabia.

Item generation

To adopt a methodical approach to producing ISRS items, the present study adhered to prior research (17, 21). The domain of the construct was specified using literature review and qualitative methods (in-depth interviews and group discussions). After reviewing previous research, five researchers were interviewed in-depth, and a convenience sample of respondents was interviewed in a focus group to (1) define the construct and dimensionality of ISRS, and (2) translate the dimensions identified in the literature. Each in-depth interview lasted 30–45 min, and the focus group 1.5 hours. All started with questions about the participants' or others' behaviors. Subsequently, they had to concentrate on specific behaviors associated with every dimension described in the interviews and discovered in the literature review. Next, they were requested to provide a brief definition and summary of ISR's key concepts. This construct is introduced and described using the findings of the qualitative research as well as the literature review. A discussion of its societal consequences follows. This iterative process, merging literature insights with qualitative data, was instrumental in shaping a comprehensive instrument to assess ISR across diverse contexts. Construct 1, community engagement & civic responsibility, encompassed items focusing on individuals' responsibility, societal norms, and pride in their community and country, reflecting civic duty and involvement. Construct 2: community involvement & personal commitment revolves around community participation, volunteer work, and personal commitment to community goals, emphasizing active involvement in community-related activities. Construct 3, social concern and duty, captures a sense of social responsibility, care for community welfare, and the duty to contribute to solving community problems, underlining a commitment to community well-being and development.

Data collection and measures

The time frame for this study was January 2024–May 2024. A questionnaire that participants self-administered was used to gather data for the study. Electronic methods were employed to gather the data. To guarantee that the participants would find the survey most appropriate, it was conducted in Arabic. Data on demographic factors, including industry, day shift, gender, and marital status, were collected using the questionnaire. Involvement in the study was entirely voluntary. Participants’ ages ranged from 21 to 75 years, with a mean age of 34 years and a standard deviation of 13. The second section of the questionnaire included a translated version of the ISRS in Arabic. The ISRS (Supplementary Table 1) was rendered Arabic by the researcher and subsequently scrutinized by a proficient Arabic translation. Two independent researchers thoroughly examined the information to guarantee content correctness. The participants were instructed to indicate their level of agreement with each item using a Likert scale ranging from 5 (strongly agree) to 1 (strongly disagree).

Application of Rasch Analysis

The Rasch analysis employed in this study followed the guidelines proposed by Linacre (22). Rasch analysis was conducted using Winsteps software version 5.3.2. Strong signs of the overall fit are a chi-square value that is not statistically significant and item and person residuals with a mean (MnSq) close to zero. Threshold maps and response category curves were used to identify items associated with threshold disorders. The residuals, which were beyond the range of 2.5 were considered inconsistent when analyzing the item and person statistics. The internal consistency of the data was assessed using the person separation index (PSI), which yielded a score of 0.7 or more (23). The dimensions of the scale were examined. Component analysis was used to evaluate the ISRS's internal validity. Determining the degree of dimensionality is aided by principal component analysis of the residuals and examination of item fit statistics. An instrument must explain more than 50% of the total variance in order for it to be considered unidimensional. The difficulty of a task is determined by the hierarchy of the item, taking into account a person's aptitude. The Rasch model determines the positions (calibrations) of the items along a measurement scale. The severity of each item was determined using calibration. Rasch's model assesses the degree of conformity of the items within a group. The items' greater magnitude determines their difficulty, which is defined by the log-odds units (logits) used for item calibration. The distribution of the items and the participants lined up exactly. An item map was employed in this study to measure each item's difficulty. This is referred to as a Wright Map in general. The Rasch model is used in the data to display measurements of people and objects. The same logit scale was used to plot the dimensions of the human and the item. A researcher can use a Wright Map to examine how test items are distributed, where respondents are located geographically, and how test takers perform by using the test item collection to clarify test takers' abilities (15, 21–23).

AMOS analysis: CFA

Using IBM-AMOS 24, a confirmatory factor analysis (CFA) was employed to validate the ISRS factor structure. The AMOS-based model was evaluated using root mean square error of approximation (RMSEA), chi-squared/df (cmin/df), goodness of fit (GFI), adjusted goodness of fit index (AGFI), comparative fit index (CFI), and PCLOSE (24).

Ethics approval and consent to participate

All participants provided informed consent prior to participation in the study. The study received approval from the research ethics committee of the King Saud University and was conducted in accordance with the Helsinki Declaration. All information provided by the participants was collected in a deidentifable form.

Characteristics of the study sample

Table 1. outlines the descriptive statistics of a study sample comprising of 812 individuals across various demographic and work-related variables. The study included 321 males (39.6%) and 491 females (60.4%). Regarding marital status, 208 individuals were single (25.7%), 559 were married (68.8%), 36 were divorced (4.4%), and nine were widowed (1.1%). In terms of working shifts, 555 individuals worked morning shifts (68.4%), 88 worked night shifts (10.8%), and 169 worked both shifts (20.8%). In terms of sector, 578 individuals worked in the government sector (71.2%), 158 in the private sector (19.4%), and 76 were freelancers (9.3%).

Table 1. Descriptive statistics of the study sample

Variables	Frequency(%)
Gender
Male	321(39.55)
Female	491(60.45)
Marital Status
Single	208(25.7)
Married	559(68.8)
Divorced	36(4.40
Widow	9(1.1)
Shift of day
Morning	555(68.4)
Nighttime	88(10.8)
Both	169(20.8)
Sector
Government	578(71.2)
Private	158(19.4)
Free lancer	76(9.3)
Total	812(100.0)

CFA analysis

Factor loading

AMOS 23.0 was utilized to conduct a CFA to assess the measurement models of the ISRS. The CFA results depicted in Figure 1 utilized the overall goodness-of-fit chi-square as a fit index, indicating the data's alignment with a potential tri-dimensional ISRS model. During the analysis, each item's factor loadings were scrutinized and all items were retained based on their satisfactory factor loadings. Notably, the factor loadings between the items (IRSS1 and IRSS16) and their respective constructs (C1=Community Engagement & Civic Responsibility, C2=Community Involvement & Personal Commitment, and C3=Social Concern & Duty) exceeded the recommended threshold (> 0.60).

Model fit

The AMOS-based CFA results, presented in Table 2, indicate a good fit for the ISRS model. The root mean square error of approximation (RMSEA) value of 0.067 fell within the recommended range of <0.05 to 0.10, suggesting a reasonable fit of the model to the data. The comparative fitness index (CFI) score of 0.96 exceeded the standard threshold of >0.95, indicating a robust fit between the suggested model and observed data. The chi-square/df ratio of 4.57 is below the recommended threshold of <5.0, further supports the adequacy of the model fit. While the goodness of fit (GFI) value of 0.95 meets the criterion of >0.95, the adjusted goodness of fit index (AGFI) value of 0.87 slightly falls below the recommended threshold of >0.80. Overall, these results collectively suggest that the ISRS model demonstrates a satisfactory fit to the data, with most fit indices meeting or surpassing standard benchmarks for model evaluation.

Table 2. Confirmatory factor analysis

Measure	Recommended Range or Value	ISRS results
Root-mean square-error of approximation (RMSEA)	<0.05–0.10	0.067
Comparative fit-index (CFI)	>0.95	0.96
Chi-square/df (cmin/df)	<5.0	4.57
Good-of-fit (GFI)	>0.95	0.95
Adjusted goodness of fit-index (AGFI)	>0.80	0.87

Rasch Analysis

Category Probability Curve

In Rasch analysis, category probability curves indicate the likelihood of selecting a response option for a polytomous item based on the underlying trait level. These curves help to assess the response category order and item functioning. They guide adjustments to improve the measurement precision. An assessment was conducted to determine the efficacy of the rating scale in its intended application to the ISRS sub-dimensions. The dataset does not meet the desired 10 responses per subcategory for several options on the ISRS Likert scale, particularly for ISRS1, ISRS4-8, ISRS14, and ISRS16. The fourth and fifth options were consolidated on a Likert scale (see Figure 2, Panel A). This decision was made to obtain more precise evaluations of the task's level of difficulty. An exemplar depiction of the three constructions is displayed for pre- and post-decoding of the scale.

Items and Person fit to the Rasch Model

The MnSq and Zstd values remained within the established boundaries and did not suggest that the data were stable with the Rasch model, as indicated by the suggested indicators. Individual Rasch analyses were performed on each subscale. The MnSq item ranged from 0.05 to 1.08, while the Zstd values ranged from 0.94 1.08. Our goal was to thoroughly examine all items found in any of the three dimensions as long as this instrument was multi-dimensional. The MnSq and Zstd values did not stray from the boundaries required to conform to the Rasch model (Table 3). All items exhibited suitable item fit statistics, as indicated by their item fit values, and their loadings fell within an acceptable range. In addition, the majority of the participants demonstrated a reasonable level of conformity to the Rasch model. However, 36 people were excluded because their data did not align with the model to improve the effectiveness of the scale. Individuals were adequately segregated from each other, as indicated by the person separation index for all three constructs. The ISRS items and their subscales were capable to differentiate and categorize the items, as evidenced by the item separation indices, which varied from 3.48 5.83. The item reliability statistics, which ranged from 0.75 to 0.92, indicated that both the ISRS and its subscales were considered reliable assessments.

Table 3. Rasch model fits and reliability indices.

Scale		IMnSq	Zs-td	OMnSq	Zs-td	Reliability	Separation-index
Construct1	Person	0.78	-1.02	0.99	-0.81	0.75	3.91
	Item	0.98	0.33	0.81	0.50	0.85	4.64
Construct2	Person	1.01	-0.58	0.77	0.05	0.92	3.48
	Item	1.05	0.94	1.09	-0.78	0.87	4.68
Construct3	Person	0.89	-1.2	1.1	-0.29	0.90	5.17
	Item	0.84	0.91	0.86	1.08	0.79	4.66
IRSRS						0.86	5.83

Multidimensionality of ISRS

The multidimensionality of the ISRS was assessed using eigenvalues and explained variances. The first component, often known as the "first contrast,” exhibited a significant level of multidimensionality, accounting for 12.1% of the total variability in the data with an Eigen-value of 2.84. The second and third contrasts account for 8.4% and 7.2% of the total observed variance, respectively. The Eigen-values were 2.3 and 1.56. The internal validity of the scale was supported by the point that the most significant initial contrast component accounted for 67.9% of the variation.

The participant hierarchy (Wright Map)

The item map, also known as the participant hierarchy, illustrates the sequential association between participants and items (Figure 3). The participants in this study demonstrated a wider spectrum of characteristics than the items. This provides evidence that there is a correlation between the skill level of the sample and the skill level demonstrated in the items. The test-item targeting was effectively conducted as the average of the item measurements was more than three standard deviations below the average of the person measures.

Given the current state of research, the need to create an ISRS and perform this unique study becomes even clearer. Previous research on social responsibility has mostly concentrated on organizational practices and Corporate Social Responsibility (CSR) (1-9, 12, 13, 25). Although minute research has been conducted on personal social responsibility, it is interesting to highlight that current scales have mostly been created from a consumer's point of view (12) instead of directly evaluating particular actions and attitudes toward community involvement and civic responsibilities. Therefore, the special contribution of this study is in closing this gap by means of a customized scale, the ISRS, which specifically targets and assesses individual-level social responsibility, thus enhancing scholarly debate and pragmatic uses in the field of social responsibility research. Therefore, this study intended to develop and validate the ISRS through three key stages: scale development, Rasch validation, and confirmatory factor analysis (26). The objective was to create a reliable measurement tool that captures individual social responsibility dimensions, such as community engagement and civic duty, ensuring validity and alignment with the intended constructs. The findings of this study revealed that the sixteen-item scale had multiple dimensions. Moreover, the results provide the application of multidimensional-scale credibility. The data suggest that each of the three subscales can be characterized using a unidimensional scale. Furthermore, the indicators of the subscales had a remarkably high degree of internal consistency. These outcomes provide additional confirmation at a more detailed level and are largely consistent with those of previous studies that used a multiple construct scale (27-29). This study provides the initial evidence to substantiate this claim.

Based on the confirmatory validity analysis of this study, it was determined that the final model of the structural ISRS is adequate and valid for all three subscales examined. In the present investigation, we observed satisfactory outcomes. An adequate model is characterized by an RMSEA value that falls within acceptable thresholds, GFI and AGFI values that are close to 1, and a CFI value beyond 0.9 (30, 31). Our study, utilizing the pattern fitness index, determined that our data met the specified criteria. Following the findings of this study, numerous scholars have investigated the confirmatory factor analysis (CFA) of the formulated (32-34).

It is important to recognize this limitation when examining the results of this study. This constraint pertains to the small sample size. To validate the findings of this study, it would be necessary to conduct further research using samples collected from different locations in the country, as the current sample was only obtained from one specific region.

Based on the findings derived from the AMOS analysis, the finalized multidimensional ISRS model effectively mirrors the data in this article. The Rasch analysis was employed to confirm the multi-dimensionality of the ISRS, revealing separation indices exceeding 3.5 for both people and items, with item characteristic curves supporting the validity of the revised rating scale. The results from the MnSq and Zstd values indicate ISRS alignment with the Rasch model, which remains within established limits. This pioneering study shows the effectiveness of the ISRS as an assessment tool for ISR, affirming its four elements as a valid and reliable measurement technique applicable across various quantifiable sustainability domains. Future research could explore cross-cultural applications, longitudinal studies for stability assessment, organizational implementations for cultivating social responsibility, and further refinement through replication studies, fostering a deeper understanding and practical utilization of the ISRS in promoting socially responsible behaviors and attitudes across diverse contexts.

Ethics approval and consent to participate

All participants provided informed consent prior to participation in the study. The study received approval from the research ethics committee of the King Saud University and was conducted in accordance with the Helsinki Declaration. All information provided by the participants was collected in a deidentifable form.

Consent for publication

Not applicable.

Availability of data and material

The datasets utilized and/or analyzed in the current investigation can be acquired from the corresponding author upon a reasonable inquiry.

Competing interests

The author affirms that he does not have any conflicting interests.

Funding

The author extends appreciation to the Deanship of Scientific Research at King Saud University for funding this work.

Authors' contributions

AA played a vital role in this research by contributing to its conception, study design, data acquisition, analysis, and interpretation. They were involved in drafting, revising, and critically reviewing the article, ultimately providing approval for publication in a chosen journal. AA also agreed to take responsibility for all aspects of this work.

Acknowledgements

NIL.

Orlitzky M, Schmidt FL, Rynes SL. Corporate social and financial performance: A meta-analysis. Organ Stud. 2003;24(3):403–41.
Cho K, Han SH, Kim HJ, Kim S. The valuation effects of corporate social responsibility on mergers and acquisitions: Evidence from US target firms. Corp Soc Responsib Environ Manag. 2021;28(1):378–88.
Su L, Pan Y, Chen X. Corporate social responsibility: Findings from the Chinese hospitality industry. J retailing consumer Serv. 2017;34:240–7.
Ghosh A. Corporate governance and corporate social responsibility. Essays on Sustainability and Management: Emerging Perspectives. 2017:153 – 75.
Ahmed RI, Zhao G, Ahmad N, Habiba U. A nexus between corporate social responsibility disclosure and its determinants in energy enterprises. J Bus Industrial Mark. 2022;37(6):1255–68.
Jones DA, Rupp DE. Social responsibility in and of organizations: The psychology of corporate social responsibility among organizational members. The SAGE Handbook of Industrial, Work, and Organizational Psychology. 2017;3:333 – 50.
Tamvada M. Corporate social responsibility and accountability: a new theoretical foundation for regulating CSR. Int J Corp Social Responsib. 2020;5(1):2.
Ogola FO, Mària JF. Mechanisms for development in corporate citizenship: a multi-level review. Int J Corp Social Responsib. 2020;5(1):7.
Carroll AB. Corporate social responsibility: Perspectives on the CSR construct’s development and future. Bus Soc. 2021;60(6):1258–78.
Boyd NM, Nowell B. Psychological sense of community: A new construct for the field of management. J Manage Inq. 2014;23(2):107–22.
Mosca O, Milani A, Fornara F, Manunza A, Krys K, Maricchiolo F. Basic psychological needs, good societal development and satisfaction with life: The mediating role of the environment. Sustainability. 2023;15(18):13794.
Öberseder M, Schlegelmilch BB, Murphy PE, Gruber V. Consumers’ perceptions of corporate social responsibility: Scale development and validation. J Bus Ethics. 2014;124:101–15.
El Akremi A, Gond J-P, Swaen V, De Roeck K, Igalens J. How do employees perceive corporate responsibility? Development and validation of a multidimensional corporate stakeholder responsibility scale. J Manag. 2018;44(2):619–57.
Topal HF, Hunt DV, Rogers CD. Exploring urban sustainability understanding and behaviour: A systematic review towards a conceptual framework. Sustainability. 2021;13(3):1139.
Koo B, Curtis C, Ryan B, Chung Y, Khojasteh J. Psychometric approaches to exploring the characteristics of smart hotel brand experiences: Scale development and validation. J Hospitality Tourism Manage. 2023;56:385–95.
Türkmen H, Ekşioğlu A, Tuna Oran N, Erdoğan A. Readiness for Weaning Scale: Development and psychometric measurement study. Infant Child Dev. 2024;33(3):e2471.
Davis SL, Rives LM, De Maya SR. Introducing personal social responsibility as a key element to upgrade CSR. Span J Marketing-ESIC. 2017;21(2):146–63.
Yamashita T. Analyzing Likert scale surveys with Rasch models. Res methods Appl linguistics. 2022;1(3):100022.
Chen WH, Lenderking W, Jin Y, Wyrwich KW, Gelhorn H, Revicki DA. Is Rasch model analysis applicable in small sample size pilot studies for assessing item characteristics? An example using PROMIS pain behavior item bank data. Qual life research: Int J Qual life aspects Treat care rehabilitation. 2014;23(2):485–93.
Muthén LK, Muthén BO. How to use a Monte Carlo study to decide on sample size and determine power. Struct Equ Model. 2002;9(4):599–620.
Kariri HDH, Somaili HE, Mansour ME-SI, Omair AH, Radwan OA. Rasch and Confirmatory Factor Analyses of the Arabic Version of the Psychological Empowerment Scale (PsyES): A Multicultural Approach for Measurable Sustainability. Sustainability. 2023;15(7):5928.
Seamon BA, Kautz SA, Velozo CA. Rasch analysis of the Activities-Specific Balance Confidence Scale in individuals poststroke. Archives rehabilitation Res Clin translation. 2019;1(3–4):100028.
Eklund M, Erlandsson L-K, Hagell P. Psychometric properties of a Swedish version of the Pearlin Mastery Scale in people with mental illness and healthy people. Nord J Psychiatry. 2012;66(6):380–8.
Tan Ş, Pektaş S. Examining the invariance of a measurement model by using the covariance structure approach. Int J Contemp Educational Res. 2020;7(2):27–39.
Rupp DE, Mallory DB. Corporate social responsibility: Psychological, person-centric, and progressing. Annu Rev Organ Psychol Organ Behav. 2015;2(1):211–36.
Hsiao Y-Y, Qi CH, Dale PS, Bulotsky-Shearer R, Wang Q. Measuring Behavior Problems in Children from Low-Income Families: A Rasch Analysis of the Child Behavior Checklist for Ages 1½–5. J Psychoeducational Assess. 2023;41(5):526–41.
Muniandy UCKA, Zulnaidi H, Halili SH. Validity and reliability of the Situational Motivational Scale (SIMS) instrument: Using Rasch model and Amos. MOJES: Malaysian Online J Educational Sci. 2023;11(1):34–46.
Yusuf M, Andariana A, Abustang PB, Mannan A, Tabbu MAS, Qaiyimah D, et al. editors. Construction Validity Testing on Blended Learning Implementation Evaluation Instruments. E3S Web of Conferences; 2023: EDP Sciences.
Rosli MS, Saleh NS, Alshammari SH, Ibrahim MM, Atan AS, Atan NA. Improving Questionnaire Reliability using Construct Reliability for Researches in Educational Technology. Int J Interact Mob Technol. 2021;15(4):109–16.
Rogozińska-Pawełczyk A. The role of innovative human resource management practices, organizational support and knowledge worker effort in counteracting job burnout in the Polish business services sector. Int J Occup Med Environ Health. 2024;37(2):220.
Ayele D, Ram T. The effects of information sharing and logistics practices on supply chain performances. Int J Supply Chain Oper Resil. 2023;6(1):26–52.
Hox JJ. Confirmatory factor analysis. The encyclopedia of research methods in criminology and criminal justice. 2021;2:830-2.
Griffiths MD, Pakpour AH, Mamun MA. Correction to: psychometric validation of the bangla fear of covid-19 scale: confirmatory factor analysis and rasch analysis. Int J mental health Addict. 2022;20(4):2520–2.
López-Lemus JA. Development of a social commerce scale: exploratory and confirmatory factor analysis using structural equation modelling. Int J Organizational Anal. 2024.

No competing interests reported.

SupplementaryTable1.docx

Development, Rasch Validation, and Confirmatory Factor Analysis of the Individual's Social Responsibility Scale

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and Methods

3. Results

4. Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1