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Repeated measures design is a research design that involves multiple measures of the same variable taken on the same or matched subjects either under different conditions or over two or more time periods. [1] For instance, repeated measurements are collected in a longitudinal study in which change over time is assessed.
In particular cases, simpler tests like paired difference test, McNemar test and Cochran–Mantel–Haenszel test are available. When the outcome of interest is continuous, estimation of the average treatment effect is performed. Matching can also be used to "pre-process" a sample before analysis via another technique, such as regression ...
Quantitative psychological research findings result from mathematical modeling and statistical estimation or statistical inference. The two types of research differ in the methods employed, rather than the topics they focus on. There are three main types of psychological research: Correlational research; Descriptive research; Experimental research
Statistical tests are used to test the fit between a hypothesis and the data. [1] [2] Choosing the right statistical test is not a trivial task. [1]The choice of the test depends on many properties of the research question.
Radius matching: all matches within a particular radius are used -- and reused between treatment units. Kernel matching: same as radius matching, except control observations are weighted as a function of the distance between the treatment observation's propensity score and control match propensity score. One example is the Epanechnikov kernel.
In medicine, a crossover study or crossover trial is a longitudinal study in which subjects receive a sequence of different treatments (or exposures). While crossover studies can be observational studies, many important crossover studies are controlled experiments, which are discussed in this article.
Statistical methods for psychology include development and application statistical theory and methods for modeling psychological data. These methods include psychometrics, factor analysis, experimental designs, and Bayesian statistics. The article also discusses journals in the same field. [1]
A way to design psychological experiments using both designs exists and is sometimes known as "mixed factorial design". [3] In this design setup, there are multiple variables, some classified as within-subject variables, and some classified as between-group variables. [3] One example study combined both variables.