It refers to a cause-and-effect relationship where one variable directly influences or produces a change in another. In other words, when Variable A causes Variable B, changes in A will result in changes in B. For example, increasing the dosage of a medicine (A) causes a reduction in symptoms (B).
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Analysis of Variance Before-After Test Chi-Square Test Chi-Square Test of Independence Conceptual Framework Confounding Variable Construct Contingency Table Cross-Sectional Design Dependent Sample 't' Test Descriptive Design Descriptive Statistical Analysis Distribution-Free Test EFA Experimental Design Exploratory Design Exploratory Factor Analysis Factor Analysis Independent Sample 't' Test Latent Variable Longitudinal Design MANOVA Matched Paired Sample 't' test Mean Analysis Measures of Central Tendency Multi-Variate Analysis Multi-Variate Analysis of Variance Multiple ANOVA Non-Parametric Test One-Way ANOVA One Sample t-Test Paired Sample 't' Test Population Repeated Measures ANOVA Research Design Research Objectives Sample Sampling Sampling Principles Simple Sample 't' Test Small Sample Test Testing of Hypothesis T Test Two-Way ANOVA Variation