Pedagosko i psiholosko testiranje i procjenjivanje spadaju među najvažnije doprinose znanosti o ponasanju nasem drustvu i pružaju temeljna i znacajna poboljsanja u odnosu na ranije postupke. Iako se ne može ustvrditi da su svi testovi dovoljno usavrseni niti da su sva testiranja razborita i korisna, postoji velika kolicina informacija koje ukazuju na ucinkovitost kvalitetnih instrumenata u onim situacijama u kojima je njihovo koristenje potkrijepljeno validacijskim podacima. Pravilna upotreba testova može dovesti do boljih odluka o pojedincima i programima nego sto bi to bio slucaj bez njihovog koristenja, a također i ukazati na put za siri i pravedniji pristup obrazovanju i zaposljavanju. Međutim, losa upotreba testova može dovesti do zamjetne stete nanesene ispitanicima i drugim sudionicima u procesu donosenja odluka na temelju testovnih podataka. Cilj Standarda je promoviranje kvalitetne i eticne upotrebe testova te uspostavljanje osnovice za ocjenu kvalitete postupaka testiranja. Svrha objavljivanja Standarda je uspostavljanje kriterija za evaluaciju testova, provedbe testiranja i posljedica upotrebe testova. Iako bi evaluacija prikladnosti testa ili njegove primjene trebala ovisiti prvenstveno o strucnim misljenjima, Standardi pružaju okvir koji osigurava obuhvacanje svih relevantnih pitanja. Bilo bi poželjno da svi autori, sponzori, nakladnici i korisnici profesionalnih testova usvoje Standarde te da poticu druge da ih također prihvate.

Standards for educational and psychological testing

Statistical Analysis with Missing Data

Period Notes No. of Units FY 1999 FY 2000 FY 2001 FY 2002 FY 2003 FY 2004 FY 2005 FY 2006 7/1/06-12/31/06 FY 2007 FY 2008 FY 2009 FY 2010 FY 2011 FY 2012 FY 2013 FY 2014 Total Units Sold est. est. est. est. est. est. Actual Actual Actual Actual Actual Actual Actual Actual Actual Actual Actual 1,768 3,797 3,755 5,592 3,310 3,218 3,803 3,888 2,144 3,077 3,358 2,590 3,043 2,132 1,649 1,732 855 49,710

The standards for educational and psychological testing.

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Applied Missing Data Analysis

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

The article examines issues involved in comparing groups and measuring change with pretest and posttest data. Different pretest-posttest designs are presented in a manner that can help rehabilitation professionals to better understand and determine effects resulting from selected interventions. The reliability of gain scores in pretest-posttest measurement is also discussed in the context of rehabilitation research and practice.

/pdf/pretest-posttest-designs-and-measurement-of-change-45oespj154.pdf

Pretest-posttest designs and measurement of change

This article describes the logic and procedures behind testing for factorial invariance across groups in the context of construct validation. The procedures include testing for configural, measurement, and structural invariance in the framework of multiple-group confirmatory factor analysis (CFA). The forward (sequential constraint imposition) approach to testing for factorial invariance is described and illustrated for the cases of first-order and second-order CFA models; computer codes in Mplus are provided. Computations of the Satorra—Bentler scaled chi-square difference, used in testing for factorial invariance under lack of multivariate normality, are also described. Some points of caution related to testing and interpreting measurement invariance are provided as well.

Testing for Factorial Invariance in the Context of Construct Validation

Structural equation modeling (SEM) provides a dependable framework for testing differences among groups on latent variables (constructs, factors). The purpose of this article is to illustrate SEM-based testing for group mean differences on latent variables. Related procedures of confirmatory factor analysis and testing for measurement invariance across compared groups are also presented in the context of rehabilitation research.

/pdf/comparing-groups-on-latent-variables-a-structural-equation-2ug2vcc14y.pdf

Comparing groups on latent variables: A structural equation modeling approach

Statistical Methods for Validation of Assessment Scale Data in Counseling and Related Fields

A method for interval estimation of scale reliability with discrete data is outlined. The approach is applicable with multi-item instruments consisting of binary measures, and is developed within the latent variable modeling methodology. The procedure is useful for evaluation of consistency of single measures and of sum scores from item sets following the 2-parameter logistic model or the 1-parameter logistic model. An extension of the method is described for constructing confidence intervals of change in reliability due to instrument revision. The proposed procedure is illustrated with an example.

Dimiter M. Dimitrov

Papers

Pretest-posttest designs and measurement of change

Testing for Factorial Invariance in the Context of Construct Validation

Comparing groups on latent variables: A structural equation modeling approach

Statistical Methods for Validation of Assessment Scale Data in Counseling and Related Fields

Evaluation of Scale Reliability With Binary Measures Using Latent Variable Modeling