Multivariate, Replicated, Single-Subject, Repeated Measures Designs and P-Technique Factor Analysis A Review of Intraindividual Change Studies

To facilitate innovation in applied psychology research, investigators need to be well-informed about available research designs. The purpose of this article is to provide an overview of repeated measures research designs (e.g., participants exposed to more than one treatment or measured on more than one occasion). My intent is twofold. First, I underscore the wide range of repeated measures research designs available to researchers in applied psychology. Second, I argue that the differentiation and polarity of group and single-subject research designs is largely arbitrary. I use examples to illustrate each repeated measures design and present its strengths and limitations.

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Testing all six person-oriented principles in dynamic factor analysis

Development and Psychopathology ◽

10.1017/s0954579410000027 ◽

2010 ◽

Vol 22 (2) ◽

pp. 255-259 ◽

Cited By ~ 16

Author(s):

Peter C. M. Molenaar

Keyword(s):

Factor Analysis ◽

Developmental Psychopathology ◽

Dynamic Factor Analysis ◽

Computational Techniques ◽

Single Subject ◽

Dynamic Factor ◽

Current Form ◽

Interindividual Differences ◽

Intraindividual Change ◽

Complex Interactions

AbstractAll six person-oriented principles identified by Sterba and Bauer's Keynote Article can be tested by means of dynamic factor analysis in its current form. In particular, it is shown how complex interactions and interindividual differences/intraindividual change can be tested in this way. In addition, the necessity to use single-subject methods in the analysis of developmental processes is emphasized, and attention is drawn to the possibility to optimally treat developmental psychopathology by means of new computational techniques that can be integrated with dynamic factor analysis.

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Significance of single‐subject studies (and repeated‐measures designs)

Educational Psychologist ◽

10.1080/00461528209529243 ◽

1982 ◽

Vol 17 (1) ◽

pp. 54-60 ◽

Cited By ~ 5

Author(s):

J. Ronald Gentile

Keyword(s):

Repeated Measures ◽

Single Subject ◽

Repeated Measures Designs

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Probability-Based and Measurement-Related Hypotheses With Full Restriction for Investigations by Means of Confirmatory Factor Analysis

Methodology ◽

10.1027/1614-2241/a000033 ◽

2011 ◽

Vol 7 (4) ◽

pp. 157-164

Author(s):

Karl Schweizer

Keyword(s):

Factor Analysis ◽

Confirmatory Factor Analysis ◽

Cognitive Processing ◽

Latent Variables ◽

Repeated Measures ◽

Latent Variable ◽

Model Fit ◽

Repeated Measures Data ◽

Confirmatory Factor ◽

And Performance

Probability-based and measurement-related hypotheses for confirmatory factor analysis of repeated-measures data are investigated. Such hypotheses comprise precise assumptions concerning the relationships among the true components associated with the levels of the design or the items of the measure. Measurement-related hypotheses concentrate on the assumed processes, as, for example, transformation and memory processes, and represent treatment-dependent differences in processing. In contrast, probability-based hypotheses provide the opportunity to consider probabilities as outcome predictions that summarize the effects of various influences. The prediction of performance guided by inexact cues serves as an example. In the empirical part of this paper probability-based and measurement-related hypotheses are applied to working-memory data. Latent variables according to both hypotheses contribute to a good model fit. The best model fit is achieved for the model including latent variables that represented serial cognitive processing and performance according to inexact cues in combination with a latent variable for subsidiary processes.

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Increasing Precision without Altering Treatment Effects: Repeated Measures Designs in Survey Experiments

American Political Science Review ◽

10.1017/s0003055421000241 ◽

2021 ◽

pp. 1-18

Author(s):

SCOTT CLIFFORD ◽

GEOFFREY SHEAGLEY ◽

SPENCER PISTON

Keyword(s):

Treatment Effect ◽

Repeated Measures ◽

Treatment Effects ◽

Repeated Measurements ◽

Experimental Designs ◽

Treatment Effect Size ◽

Standard Design ◽

Survey Experiments ◽

Repeated Measures Designs ◽

Alternative Designs

The use of survey experiments has surged in political science. The most common design is the between-subjects design in which the outcome is only measured posttreatment. This design relies heavily on recruiting a large number of subjects to precisely estimate treatment effects. Alternative designs that involve repeated measurements of the dependent variable promise greater precision, but they are rarely used out of fears that these designs will yield different results than a standard design (e.g., due to consistency pressures). Across six studies, we assess this conventional wisdom by testing experimental designs against each other. Contrary to common fears, repeated measures designs tend to yield the same results as more common designs while substantially increasing precision. These designs also offer new insights into treatment effect size and heterogeneity. We conclude by encouraging researchers to adopt repeated measures designs and providing guidelines for when and how to use them.

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