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      Critical Thinking in Higher E ducation and Labour Market 

      Critical Thinking Competence in Study Process and Labour Market: A Quantitative Study

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          There is no author summary for this book yet. Authors can add summaries to their books on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract:

          Critical thinking is considered to be one of the most important competences that determine well-being of the individual and society. In the rapidly changing world of information flow, critical thinking is often identified as the goal of higher education. In the modern labour market, employers are increasingly emphasising the importance of critical thinking skills when making decisions in challenging conditions.

          The purpose of this chapter is to reveal how teachers, students, employers and employees define critical thinking, and what their attitude is towards the development of critical thinking skills and dispositions and their importance in the modern labour market. This chapter consists of four sections. The first section discusses the quantitative research methodology. The construction and validation of the research instrument, the methods of data analysis, the sampling and characteristics of the respondents, the research ethics, and limitations are presented in detail. The second section presents the attitude of the teachers and students towards the manifestation and development of critical thinking skills and dispositions. The evaluation of higher education participants – teachers and students – of the importance of critical thinking skills and dispositions in the modern labour market is presented. The teachers’ need to improve critical thinking skills is revealed. The third section introduces the attitude of employers and employees towards the manifestation and development of critical thinking skills and dispositions. The evaluation of labour market participants – employers and employees – of the importance of critical thinking skills and dispositions in the modern labour market is presented. The manifestation of critical thinking in employees’ professional activities is presented, and the need to improve critical thinking skills in labour market participants is revealed. The fourth section presents a comparison of the manifestation of critical thinking in higher education and in labour market, from the point of view of higher education and labour market participants.

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          Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance

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            Principles and Practice of Structural Equation Modeling, Fourth Edition

            Emphasizing concepts and rationale over mathematical minutiae, this is the most widely used, complete, and accessible structural equation modeling (SEM) text. Continuing the tradition of using real data examples from a variety of disciplines, the significantly revised fourth edition incorporates recent developments such as Pearl's graphing theory and the structural causal model (SCM), measurement invariance, and more. Readers gain a comprehensive understanding of all phases of SEM, from data collection and screening to the interpretation and reporting of the results. Learning is enhanced by exercises with answers, rules to remember, and topic boxes. The companion website supplies data, syntax, and output for the book's examples--now including files for Amos, EQS, LISREL, Mplus, Stata, and R (lavaan).<br><br> New to This Edition<br> *Extensively revised to cover important new topics: Pearl's graphing theory and the SCM, causal inference frameworks, conditional process modeling, path models for longitudinal data, item response theory, and more.<br> *Chapters on best practices in all stages of SEM, measurement invariance in confirmatory factor analysis, and significance testing issues and bootstrapping.<br> *Expanded coverage of psychometrics.<br> *Additional computer tools: online files for all detailed examples, previously provided in EQS, LISREL, and Mplus, are now also given in Amos, Stata, and R (lavaan).<br> *Reorganized to cover the specification, identification, and analysis of observed variable models separately from latent variable models.<br><br> Pedagogical Features<br> *Exercises with answers, plus end-of-chapter annotated lists of further reading.<br> *Real examples of troublesome data, demonstrating how to handle typical problems in analyses.<br> *Topic boxes on specialized issues, such as causes of nonpositive definite correlations.<br> *Boxed rules to remember.<br> *Website promoting a learn-by-doing approach, including syntax and data files for six widely used SEM computer tools.
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              When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions.

              A simulation study compared the performance of robust normal theory maximum likelihood (ML) and robust categorical least squares (cat-LS) methodology for estimating confirmatory factor analysis models with ordinal variables. Data were generated from 2 models with 2-7 categories, 4 sample sizes, 2 latent distributions, and 5 patterns of category thresholds. Results revealed that factor loadings and robust standard errors were generally most accurately estimated using cat-LS, especially with fewer than 5 categories; however, factor correlations and model fit were assessed equally well with ML. Cat-LS was found to be more sensitive to sample size and to violations of the assumption of normality of the underlying continuous variables. Normal theory ML was found to be more sensitive to asymmetric category thresholds and was especially biased when estimating large factor loadings. Accordingly, we recommend cat-LS for data sets containing variables with fewer than 5 categories and ML when there are 5 or more categories, sample size is small, and category thresholds are approximately symmetric. With 6-7 categories, results were similar across methods for many conditions; in these cases, either method is acceptable.
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                : 273
                10.3726/9783631861479.003.0005
                612bbf2d-6adf-4e0f-8f3b-7550291ac27b
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