Selecting Appropriate Test in Quantitative Analysis || Quantitative Research


Selecting Appropriate Test in Quantitative Analysis

A. Tests for Comparing Means (Differences)

Independent Samples T-Test

•         Purpose: Compares the means of two independent groups.

•         Data: Continuous outcome, 2 groups.  (If normality assumption met).

Paired T-Test

•         Purpose: Compares the means of two related groups (before and after).

•         Data: Continuous outcome, same group measured twice.  (If data Is normally distributed).

ANOVA (Analysis of Variance)

•         Purpose: Compares means across three or more groups.

•         Data: Continuous outcome, multiple groups. (Normal data)

•         One-Way ANOVA: Compares one independent variable across multiple groups.

•         Two-Way ANOVA: Examines the effect of two independent variables on a dependent variable.


B. Non-Parametric Tests (for non-normal data)  

Mann-Whitney U Test

•         Purpose: Compare two independent groups (Alternative to the independent t-test)

•         Data: Non-normal continuous or ordinal data.

Wilcoxon Signed-Rank Test

•         Purpose: Compares two related groups (before-and-after). (Alternative of Paired T-test)

•         Data: Paired, non-normal continuous data.

Kruskal-Wallis Test

•         Purpose: Compare three or more groups (Alternative to one-way ANOVA).

•         Data: Non-normal continuous data.


C. Tests for Categorical Data (Proportions)

Chi-Square Test

•         Purpose: Tests the association between two categorical variables.

•         Data: Categorical variables. (If large sample sizes)

Fisher’s Exact Test

•         Purpose: Used (If small sample size) for comparing two categorical variables.

•         Data: Categorical data. 



D. Tests for Relationships (Correlations/Associations)

Pearson's Correlation coefficient (Pearson’s r)

•         Purpose: Measures the strength of the linear relationship between two continuous variables.

•         Data: Continuous, normally distributed variables. (Linear relationship)

Spearman's Rank Correlation (Spearman's rho.)

•         Purpose: Measures the strength of the monotonic relationship between two variables when data is not normally distributed.

•         Data: Ordinal or non-normal continuous variables. (non-linear but still monotonic relationship)



E. Regression Analysis (Predictive Models)

Simple Linear Regression

•         Purpose: Predicts the relationship between a continuous dependent variable and one continuous independent variable.

•         Data: Continuous variables (DV and IV).

Multiple Linear Regression

•         Purpose: Examines the effect of multiple continuous or categorical independent variables On a Continuous outcome.

•         Data: Continuous dependent variable, multiple independent variables.

Logistic Regression

•         Purpose: Predicts the probability of a binary outcome (yes/no).

•         Data: Categorical dependent variable (binary: infected/not infected). 

 


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