Measurement invariance conventions and reporting: The state of the art and future directions for psychological research

I. INTRODUCTION TO EVALUATION. 1. Evaluation's Basic Purpose, Uses, and Conceptual Distinctions. 2. Origins of Modern Program Evaluation. 3. Recent Developments and Trends in Evaluation. II. ALTERNATIVE APPROACHES TO PROGRAM EVALUATION. 4. Alternative Views of Evaluation. 5. Objectives-Oriented Evaluation Approaches. 6. Management-Oriented Evaluation Approaches. 7. Consumer-Oriented Evaluation Approaches. 8. Expertise-Oriented Evaluation Approaches. 9. Adversary-Oriented Evaluation Approaches. 10. Participant-Oriented Evaluation Approaches. 11. Alternative Evaluation Approaches: A Summary and Comparative Analysis. III. PRACTICAL GUIDELINES FOR PLANNING EVALUATION. 12. Clarifying the Evaluation Request and Responsibilities. 13. Setting Boundaries and Analyzing the Evaluation Context. 14. Identifying and Selecting the Evaluative Questions and Criteria. 15. Planning How to Conduct the Evaluation. IV. PRACTICAL GUIDELINES FOR CONDUCTING AND USING EVALUATIONS. 16. Dealing with Political, Ethical, and Interpersonal Aspects of Evaluation. 17. Collecting, Analyzing, and Interpreting Quantitative Information. 18. Collecting, Analyzing, and Interpreting Qualitative Information. 19. Reporting and Using Evaluation Information. 20. Evaluating Evaluations. V. EMERGING AND FUTURE SETTINGS FOR PROGRAM EVALUATION. 21. Conducting Multiple-Site Evaluation Studies. 22. Conducting Evaluations of Organizations Renewal and Training in Corporate and Nonprofit Settings. 23. The Future of Evaluation. Appendix: Some General Areas of Competence Important in Education Evaluation.

http://platform.almanhal.com/Files/?ID=T2-41793-MLA0011322.pdf

تقويم البرنامج : طرق بديلة و إرشادات عملية = Program evaluation Alternative Approaches and Practical Guidelines

EDITORIAL BOARD Robert Davison Aviles, Bradley University Harley E. Baker, California State University–Channel Islands Jean-Guy Blais, Universite de Montreal, Canada Catherine Y. Chang, Georgia State University Robert C. Chope, San Francisco State University Kevin O. Cokley, University of Missouri, Columbia Patricia B. Elmore, Southern Illinois University Shawn Fitzgerald, Kent State University John J. Fremer, Educational Testing Service Vicente Ponsoda Gil, Universidad Autonoma de Madrid, Spain Jo-Ida C. Hansen, University of Minnesota Charles C. Healy, University of California at Los Angeles Robin K. Henson, University of North Texas Flaviu Adrian Hodis, Victoria University of Wellington, New Zealand Janet K. Holt, Northern Illinois University David A. Jepsen, The University of Iowa Gregory Arief D. Liem, National Institute of Education, Nanyang Technological University Wei-Cheng J. Mau, Wichita State University Larry Maucieri, Governors State College Patricia Jo McDivitt, Data Recognition Corporation Peter F. Merenda, University of Rhode Island Matthew J. Miller, University of Maryland Ralph O. Mueller, University of Hartford Jane E. Myers, The University of North Carolina at Greensboro Philip D. Parker, University of Western Sydney Ralph L. Piedmont, Loyola College in Maryland Alex L. Pieterse, University at Albany, SUNY Nicholas J. Ruiz, Winona State University James P. Sampson, Jr., Florida State University William D. Schafer, University of Maryland, College Park William E. Sedlacek, University of Maryland, College Park Marie F. Shoffner, University of Virginia Len Sperry, Florida Atlantic University Kevin Stoltz, University of Mississippi Jody L. Swartz-Kulstad, Seton Hall University Bruce Thompson, Texas A&M University Timothy R. Vansickle, Minnesota Department of Education Steve Vensel, Palm Beach Atlantic University Dan Williamson, Lindsey Wilson College F. Robert Wilson, University of Cincinnati

measurement and evaluation in counseling and Development

European Organization for Research and Treatment of Cancer

Since the assessment program began in 1997, over 70 countries have participated at least once. Every three years, a randomly selected group of 15-year-old students take the test, which primarily focuses on one key subject.

/pdf/programme-for-international-student-assessment-pisa-49fgenp9zi.pdf

Programme for International Student Assessment (PISA)

The technical complexities and sheer size of international large-scale assessment (LSA) databases often cause hesitation on the part of the applied researcher interested in analyzing them. Further, inappropriate choice or application of statistical methods is a common problem in applied research using these databases. This article serves as a primer for researchers on the issues and methods necessary for obtaining unbiased results from LSA data. The authors outline the issues surrounding the analysis and reporting of LSA data, with a particular focus on three prominent international surveys. In addition, they make recommendations targeted at applied researchers regarding best analysis and reporting practices when using these databases.

https://www.jstor.org/stable/info/27764566

International Large-Scale Assessment Data: Issues in Secondary Analysis and Reporting

In the field of international educational surveys, equivalence of achievement scale scores across countries has received substantial attention in the academic literature; however, only a relatively recent emphasis on scale score equivalence in nonachievement education surveys has emerged. Given the current state of research in multiple-group models, findings regarding these recent measurement invariance investigations were supported with research that was limited in scope to few groups and relatively small sample sizes. To that end, this study uses data from one large-scale survey as a basis for examining the extent to which typical fit measures used in multiple-group confirmatory factor analysis are suitable for detecting measurement invariance in a large-scale survey context. Using measures validated in a smaller scale context and an empirically grounded simulation study, our findings indicate that many typical measures and associated criteria are either unsuitable in a large group and varied sample-siz...

Assessing the Hypothesis of Measurement Invariance in the Context of Large-Scale International Surveys.

Handbook of International Large-Scale Assessment : Background, Technical Issues, and Methods of Data Analysis

Meaningful comparisons of means or relationships between latent constructs across groups require evidence that measurement is equivalent across the studied groups– a property known as measurement e...

Multiple-Group Invariance with Categorical Outcomes Using Updated Guidelines: An Illustration Using Mplus and the lavaan/semTools Packages

As part of its flagship educational study – the Programme for International Student Assessment (PISA) – the Organisation for Economic Co-operation and Development (OECD) has undertaken extensive work to create an internationally relevant composite indicator aimed at measuring socioeconomic background. However, the degree to which a single measure of socioeconomic background is reliable and valid for all participating countries is not widely discussed. To fill this gap, the authors examine the home possessions index, which is a key component of PISA's socioeconomic indicator, and highlight a number of issues surrounding this index. In particular, they take a psychometric approach to investigating the reliability and some facets of the validity of the home possessions index in a number of participating PISA countries. Their findings suggest that there are notable concerns with the current index, including highly variable reliability by country, poor model-to-data consistency on a number of subscales, and ev...

https://journals.sagepub.com/doi/pdf/10.2304/rcie.2013.8.3.259

Leslie Rutkowski

Papers

International Large-Scale Assessment Data: Issues in Secondary Analysis and Reporting

Assessing the Hypothesis of Measurement Invariance in the Context of Large-Scale International Surveys.

Handbook of International Large-Scale Assessment : Background, Technical Issues, and Methods of Data Analysis

Multiple-Group Invariance with Categorical Outcomes Using Updated Guidelines: An Illustration Using Mplus and the lavaan/semTools Packages

Measuring Socioeconomic Background in PISA: One Size Might not Fit all: