Psychological Statistics

basta psychstats

Measures of Central Tendency

Type of Problem: N/A

Level of Measurement: Nominal, Ordinal, Interval or ratio

Number of Sample: Any

Type of Sample: Any

Parametric Distribution Assumption: No specific assumption about the distribution of the variable

Measures of Variability

Type of Problem: N/A

Level of Measurement: Interval or ratio

Number of Sample: Any

Type of Sample: Any

Parametric Distribution Assumption: No specific assumption about the distribution of the variable

T- test for Independent Samples

Type of Problem: Comparison of means between two independent groups

Level of Measurement: Interval or ratio

Number of Sample: Two independent samples

Type of Sample: Random or independent

Parametric Distribution Assumption: Normally distributed populations

T-test for Related/Dependent Samples

Type of Problem: Comparison of means for related samples

Level of Measurement: Interval or ratio

Number of Sample: Paired samples

Type of Sample: Matched pairs or repeated measures

Parametric Distribution Assumption: Normally distributed population of differences (or sufficiently large sample size for the Central Limit Theorem to apply)

One-Way Analysis of Variance (ANOVA) F Test

Type of Problem: Comparison of means among three or more independent groups

Level of Measurement: Interval or ratio

Number of Sample: Three or more independent samples

Type of Sample: Random or independent

Parametric Distribution Assumption: Normally distributed populations within each group

Two-Way ANOVA F Test

Type of Problem: Comparison of means considering two independent variables

Level of Measurement: Interval or ratio

Number of Sample: Multiple samples in each combination of independent variables

Type of Sample: Random or independent

Parametric Distribution Assumption: Normally distributed populations within each combination of independent variables

F-Test for Repeated Treatment/Dependent Samples

Type of Problem: Comparison of means for repeated measures under different conditions

Level of Measurement: Interval or ratio

Number of Sample: Two or more related samples

Type of Sample: Matched pairs or repeated measures

Parametric Distribution Assumption: Normally distributed population of differences (or sufficiently large sample size for the Central Limit Theorem to apply)

Analysis of Covariance

Type of Problem: Comparison of means adjusting for a covariate

Level of Measurement: Interval or ratio

Number of Sample: Two or more independent samples

Type of Sample: Random or independent

Parametric Distribution Assumption: Normally distributed populations within each group for the covariate

Chi-square Test of Independence

Type of Problem: Association between two categorical variables

Level of Measurement: Nominal

Number of Sample: Two categorical variables

Type of Sample: Random or independent

Parametric Distribution Assumption: N/A (non-parametric test)

McNemar's Test:

Type of Problem: Comparison of proportions in a 2x2 table

Level of Measurement: Nominal

Number of Sample: Two related samples

Type of Sample: Matched pairs or repeated measures

Parametric Distribution Assumption: N/A (non-parametric test)

Fisher's Exact Test

Type of Problem: Comparison of proportions in a 2x2 table (small sample sizes)

Level of Measurement: Nominal

Number of Sample: Two independent samples

Type of Sample: Random or independent

Parametric Distribution Assumption: N/A (non-parametric test)

Pearson Product Moment Correlation Coefficient

Type of Problem: Relationship between two continuous variables

Level of Measurement: Interval or ratio

Number of Sample: Any

Type of Sample: Any

Parametric Distribution Assumption: Bivariate normality (normal distribution of the variables and their joint distribution)

Multiple Correlation

Type of Problem: Relationship between one continuous variable and two or more continuous variables

Level of Measurement: Interval or ratio

Number of Sample: Any

Type of Sample: Any

Parametric Distribution Assumption: Multivariate normality (normal distribution of the variables and their joint distribution)

Partial Correlation

Type of Problem: Relationship between two continuous variables while controlling for a third variable

Level of Measurement: Interval or ratio

Number of Sample: Any

Type of Sample: Any

Parametric Distribution Assumption: Bivariate normality for each pair of variables involved in the partial correlation.

Spearman Rank Order Correlation

Type of Problem: Relationship between two ordinal variables

Level of Measurement: Ordinal

Number of Sample: Any

Type of Sample: Any

Parametric Distribution Assumption: No specific assumption about the distribution of the variables

Kendall's Coefficient of Concordance

Type of Problem: Agreement among multiple raters or judges

Level of Measurement: Ordinal

Number of Sample: Any

Type of Sample: Any

Parametric Distribution Assumption: No specific assumption about the distribution of the variables

Phi Coefficient

Type of Problem: Association between two binary variables

Level of Measurement: Nominal

Number of Sample: Any

Type of Sample: Any

Parametric Distribution Assumption: No specific assumption about the distribution of the variables

Somer's D

Type of Problem: Association between two ordinal variables

Level of Measurement: Ordinal

Number of Sample: Any

Type of Sample: Any

Parametric Distribution Assumption: No specific assumption about the distribution of the variables

Risk Ratio

Type of Problem: Comparison of the risk of an event between two groups

Level of Measurement: Nominal

Number of Sample: Two independent samples

Type of Sample: Random or independent

Parametric Distribution Assumption: No specific assumption about the distribution of the variables

Regression Analysis

Type of Problem: Prediction of a dependent variable based on one or more independent variables

Level of Measurement: Interval or ratio

Number of Sample: Any

Type of Sample: Any

Parametric Distribution Assumption: Residuals (errors) are normally distributed

Item Analysis

Type of Problem: Assessment of the quality of individual test items

Level of Measurement: Ordinal or interval

Number of Sample: Any

Type of Sample: Any

Parametric Distribution Assumption: No specific assumption about the distribution of the variables

Cronbach's Alpha

Type of Problem: Assessment of internal consistency in a scale or test

Level of Measurement: Ordinal or interval

Number of Sample: Any

Type of Sample: Any

Parametric Distribution Assumption: No specific assumption about the distribution of the variables

Exploratory Factor Analysis

Type of Problem: Identification of underlying factors in a set of observed variables

Level of Measurement: Ordinal or interval

Number of Sample: Any

Type of Sample: Any

Parametric Distribution Assumption: Multivariate normality for the observed variables