During the past 30 years, meta-analysis has been an indispensable tool for revealing the hidden meaning of our research literatures. The four articles in this special section on meta-analysis illus...

Meta-Analysis: A Constantly Evolving Research Integration Tool

This paper provides a framework for developing sampling designs in mixed methods research. First, we present sampling schemes that have been associated with quantitative and qualitative research. Second, we discuss sample size considerations and provide sample size recommendations for each of the major research designs for quantitative and qualitative approaches. Third, we provide a sampling design typology and we demonstrate how sampling designs can be classified according to time orientation of the components and relationship of the qualitative and quantitative sample. Fourth, we present four major crises to mixed methods research and indicate how each crisis may be used to guide sampling design considerations. Finally, we emphasize how sampling design impacts the extent to which researchers can generalize their findings. Key Words: Sampling Schemes, Qualitative Research, Generalization, Parallel Sampling Designs, Pairwise Sampling Designs, Subgroup Sampling Designs, Nested Sampling Designs, and Multilevel Sampling Designs

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A Typology of Mixed Methods Sampling Designs in Social Science Research

A justice model of applicants' reactions to employment-selection systems is proposed as a basis for organizing previous findings and guiding future research. Organizational justice literature is briefly reviewed, and key findings are used to provide a framework for the proposed model and to support hypotheses. The procedural justice of selection systems is examined in terms of 10 procedural rules, wherein the satisfaction and violation of these rules provide the basis for fairness reactions. Distributive justice of hiring decisions is examined with respect to equity, equality, and needs. The model also includes the interaction of procedural and distributive justice and the relationship of fairness reactions to individual and organizational outcomes.

https://www.jstor.org/stable/pdfplus/258595.pdf

The Perceived Fairness of Selection Systems: An Organizational Justice Perspective

Data analysis methods in psychology still emphasize statistical significance testing, despite numerous articles demonstrating its severe deficiencies. It is now possible to use meta-analysis to show that reliance on significance testing retards the development of cumulative knowledge. But reform of teaching and practice will also require that researchers learn that the benefits that they believe flow from use of significance testing are illusory. Teachers must revamp their courses to bring students to understand that (a) reliance on significance testing retards the growth of cumulative research knowledge; (b) benefits widely believed to flow from significance testing do not in fact exist; and (c) significance testing methods must be replaced with point estimates and confidence intervals in individual studies and with meta-analyses in the integration of multiple studies. This reform is essential to the future progress of cumulative knowledge in psychological research.

/pdf/statistical-significance-testing-and-cumulative-knowledge-in-4xpcuhs8wp.pdf

Statistical Significance Testing and Cumulative Knowledge in Psychology: Implications for Training of Researchers

There has been conspicuously little research concerning missing data problems in the applied psychology literature. Fortunately, other fields have begun to investigate this issue. These include survey research, marketing, statistics, economics, and biometrics. A review of this literature suggests several trends for applied psychologists. For example, listwise deletion of data is often the least accurate technique to deal with missing data. Other methods for estimating missing data scores may be more accurate and preserve more data for investigators to analyze. Further, the literature reveals that the amount of missing data and the reasons for deletion of data impact how investigators should handle the problem. Finally, there is a great need for more investigation of strategies for dealing with missing data, especially when data are missing in nonrandom or systematic patterns.

Missing data: a conceptual review for applied psychologists

Statistical power in criterion-related validation studies.

Tailored testing represents a remarkably effective application of latent trait theory. It has been brought about through recent theoretical developments and through steady advances in computer technology. Tailored testing, the reality, is the fruition of the prediction made by Green (1970), i.e., "the inevitable computer conquest of testing" (p. 194). Because tailored testing is a new, highly technical method, it is known and practiced by very few. But a wider audience of test practitioners must become familiar with it, so that its considerable potential for improved measurement in many settings may be realized. The purpose of the present paper is to introduce tailored testing to that wider audience. Discussions of technical developments will thus be limited. However, references will be provided for technical discussions of important practical developments.

https://www.jstor.org/stable/info/1434014

Tailored testing: a successful application of latent trait theory*

In the last few years, computer assisted tailored testing has been shown to be technically feasible; the question of its acceptability to examinees therefore assumes increased importance. This report presents the reactions and opinions of 163 examinees who participated in a tailored testing pilot study conducted at the U. S. Civil Service Commission during the fall of 1975. The reactions of the examinees can accurately be summarized as overwhelmingly positive, thus disposing of yet another potential obstacle to the advance of this innovative technique.

Vern W. Urry

Papers

Statistical power in criterion-related validation studies.

Tailored testing: a successful application of latent trait theory*

Computer Assisted Tailored Testing: Examinee Reactions and Evaluations:

Correction to Schmidt, Hunter, and Urry.