RCT study, which checking the pre intervention and post intervention effects on motor capacity and motor performance in the same population or same group. which statistical test best analyze this research?
Pre-test and post-test research is one of many forms of quasi-experimental design. The appropriate Statistical Test choice depends on the design (field of study dependent; randomized trials survey) and type of response variable.
1) You may dichotomize the response or DV, and use logistic regression. 2) You may use the difference (post-pre) and regression approach.
And, 3) you can compare the means and for comparing parametric variables with normal distribution paired t-test would be appropriate. For parametric variables without normal distribution, Kruskal Wallis test would be appropriate. Continuous data are often summarised by giving their average and standard deviation (SD), and the paired t-test is used to compare the means of the two samples of related data, Pre-Post points.
4) One-way ANCOVA would be best if you take the Intervention type as the factor (between-subjects variable), and the post-intervention scores as the dependent variables. Pre-intervention scores could make good covariates too.
5) if you take it as repeated measure, the repeated measure ANOVA if the assumptions are met.
Specific Design, the research question, number of participants, type of response variable cumulatively inform the analysis( given the assumptions are met) and also Statistical Test to be used.
The statistical test to use for a randomized controlled trial (RCT) depends on the type of outcome variable and the design of the study. Some common statistical tests used in RCTs include:
T-test: This test is used to compare the means of two groups (such as a treatment group and a control group) and is appropriate for continuous outcome variables.
ANOVA (Analysis of Variance): This test compares means across multiple groups and is also appropriate for continuous outcome variables.
Chi-square test: This test compares the proportions of categorical outcome variables between two or more groups.
Logistic Regression: This test is used for binary outcome variables, and it's helpful in analyzing the relationship between multiple independent variables and a binary outcome.
Cox Regression: This test is used for time-to-event outcome variables, and it's helpful in analyzing the relationship between multiple independent variables and the time to an event.
It's important to note that the choice of statistical test depends on the assumptions of the test and the data you have. It's also important to consult your supervisor or a statistician to ensure that the appropriate test is used.
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