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The overall purpose of this text is to introduce beginning researchers to the study of educational and social policy, how it has been examined from a scholarly perspective, and the salient issues to consider in conceptualizing and conducting policy research. The emphasis is on ""introduce,"" as the various policy fields within the public sector (for example, education, energy, health, labor) are much too diverse to include in depth in a single volume on theoretical concepts and research methods. The focus is not so much on the substance of policymaking as on understanding the interplay between

Policy sciences. --- Policy-making --- Policymaking --- Public policy management --- Policy sciences --- 371.014 --- 371.014 Onderwijspolitiek --- Onderwijspolitiek

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Quantitative methods in social research --- Mathematical statistics --- Social sciences --- -Social sciences --- -#SBIB:303H61 --- 519.2 --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Mathematical models --- Research --- -Mathematical models --- Wiskundige methoden en technieken --- Probability. Mathematical statistics --- 519.2 Probability. Mathematical statistics --- #SBIB:303H61 --- Research&delete&

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Multilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives.New to this edition:An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals;Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches;Expanded coverage illustrating different model-building sequences and how to use results to identify possible model improvements;An expanded set of applied examples used throughout the text;Use of four different software packages (i.e., Mplus, R, SPSS, Stata), with selected examples of model-building input files included in the chapter appendices and a more complete set of files available online.This is an ideal text for graduate courses on multilevel, longitudinal, latent variable modelling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology. Recommended prerequisites are introductory univariate and multivariate statistics.

Social sciences --- Social sciences --- Mathematical models --- Research --- Mathematical models

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"Preface Multilevel modeling has become a mainstream data analysis tool over the past decade, now figuring prominently in a range of social and behavioral science disciplines. Where it originally required specialized software, mainstream statistics packages such as IBM SPSS, SAS, and Stata all have included routines for multilevel modeling in their programs. Although some devotees of these statistical packages have been making good use of the relatively new multilevel modeling functionality, progress has been slower in carefully documenting these routines to facilitate meaningful access to the average user. Two years ago we developed Multilevel and Longitudinal Modeling with IBM SPSS to demonstrate how to use these techniques in IBM SPSS Version 18. Our focus was on developing a set of concepts and programming skills within the IBM SPSS environment that could be used to develop, specify, and test a variety of multilevel models with continuous outcomes, since IBM SPSS is a standard analytic tool used in many graduate programs and organizations globally. Our intent was to help readers gain facility in using the IBM SPSS linear-mixed models routine for continuous outcomes. We offered multiple examples of several different types of multilevel models, focusing on how to set up each model and how to interpret the output. At the time, mixed modeling for categorical outcomes was not available in the IBM SPSS software program. Over the past year or so, however, the generalized linear mixed model (GLMM) has been added to the mixed modeling analytic routine in IBM SPSS starting with Version 19. This addition prompted us to create this companion workbook that would focus on introducing readers to the multilevel approach to modeling with categorical outcomes"--

Quantitative methods in social research --- Psychometrics. --- Psychometrics --- Social sciences --- PSYCHOLOGY / Statistics. --- EDUCATION / Statistics. --- SOCIAL SCIENCE / Statistics. --- #SBIB:303H4 --- #SBIB:303H520 --- Measurement, Mental --- Measurement, Psychological --- Psychological measurement --- Psychological scaling --- Psychological statistics --- Psychology --- Psychometry (Psychophysics) --- Scaling, Psychological --- Psychological tests --- Scaling (Social sciences) --- Computer programs. --- Informatica in de sociale wetenschappen --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Measurement --- Scaling --- Methodology --- SPSS (Computer file) --- SPSS for Windows. --- Statistical product and service solutions --- Statistical package for the social sciences --- SPSS (Computer file). --- EDUCATION / Statistics --- PSYCHOLOGY / Statistics --- SOCIAL SCIENCE / Statistics --- Computer programs

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"This text demonstrates how to use multilevel and longitudinal modeling techniques available in the IBM SPSS (Version 25). Annotated screen shots provide readers with a step-by-step understanding of each technique. Readers learn how to set up, run, and interpret a variety of models. Diagnostic tools, data management issues, and related graphics are introduced throughout. Extended examples illustrate the logic of model development to show the rationale of the research questions and the steps around which the analyses are structured. New to this edition is coverage of generalized linear mixed-effects models, making longitudinal plots and unbalanced longitudinal data and write-up examples"--

Social sciences --- Longitudinal studies --- Statistical methods --- PASW (Computer file) --- SPSS (Computer file)