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Related Concept Videos

Experimental Designs01:16

Experimental Designs

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An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
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Study Design in Statistics01:15

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A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
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Group Design02:01

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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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Statistical Significance01:50

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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
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What is an Experiment?01:12

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An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
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Related Experiment Video

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Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design
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Statistical Methods for Pre-Post Intervention Design.

Oliwier Dziadkowiec

    Journal of Obstetric, Gynecologic, and Neonatal Nursing : JOGNN
    |December 16, 2023
    PubMed
    Summary

    This study reviews statistical tests for pre-post analysis. It helps researchers choose appropriate methods to evaluate changes over time effectively.

    Area of Science:

    • Statistics
    • Biostatistics
    • Research Methodology

    Background:

    • Pre-post analysis is crucial for evaluating interventions.
    • Selecting appropriate statistical tests is vital for valid results.

    Purpose of the Study:

    • To provide a comprehensive overview of statistical test options for pre-post analysis.
    • To guide researchers in selecting the most suitable statistical methods.

    Main Methods:

    • Literature review of common statistical tests used in pre-post designs.
    • Categorization of tests based on data type and assumptions.

    Main Results:

    • Overview of parametric tests (e.g., paired t-test) and non-parametric alternatives (e.g., Wilcoxon signed-rank test).

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  • Discussion of considerations such as normality, sample size, and effect size.

    • Conclusions:

    • The choice of statistical test depends on study design and data characteristics.
    • Proper test selection enhances the reliability and interpretability of pre-post study findings.