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Quantitative Results

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The current analyses available in Intellectus Statistics are:

**Descriptive Statistics-**Calculates frequencies and percentages for selected nominal and ordinal variables. Calculates means and standard deviations fro selected scale variables.

**Cronbach’s Alpha-**Examines the extent to which a set of scale level variables are consistently scored.

**Chi-Square Test of Independence-**Compares the observed frequencies to expected frequencies of two nominal level variables.

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**Pearson Correlation-**Examines the relationship between two or more scale level variables (e.g., math scores related to study time).

**Spearman Correlation-**Examines the relationship between two ordinal or scale level variables (e.g., study time in minutes related to grade letter A-F).

**Kendall Correlation-**Examines the relationship between two or more ordinal or scale-level variables.

**Partial Correlation-**Conducts a partial correlation between two scale-level variables while controlling for scale-level covariates.

**One Sample t Test–**Used to assess if the values of a single variable ar significantly different from a tested value.

**Paired Samples t Test-**Examines the mean difference between two paired scale level variables (e.g., differences between math scores pretest vs. posttest).

**One-Way ANOVA-**Used to examine differences in a scale-level variable between two or more categories.

**Repeated Measures ANOVA-**Used to examine differences in a scale-level variable measured under two or more conditions.

**One-Between One-Within ANOVA-**Used to examine differences in a scale-level variable measured under two or more conditions and between two or more categories.

**(Multiple) Linear Regression–**Examines if one or more scale, ordinal, or nominal level independent variables predict a scale level dependent variable.

**Binary Logistic Regression–**Examines if one or more scale, ordinal, or nominal level independent variables predict a nominal level dependent variable with two levels.

**Chi-Square Goodness of Fit-**Examines if a nominal variable is equally distributed across all groups (e.g., testing the distribution of favorite colors).

**McNemar’s Test-**Examines the relationship between two dichotomous variables that can be matched together (such as by time). Both variables must have the same groups for the analysis to run properly.

**Wilcoxon Signed Rank-**Examines the difference in two paired ordinal or scale level dependent variables (e.g., letter grade pretest vs. posttest).

**Friedman Test-**Examines differences among two or more scale or ordinal level variables (e.g., differences among pretest vs. posttest vs. follow-up letter grade).

**Man-Whitney U-**Examines the difference in an ordinal level dependent variable by a dichotomous nominal level variable (e.g., differences on letter grade A-F by gender).

**Kruskal Wallis-**Examines the difference in a n ordinal or scale level dependent variable by a nominal level variable (e.g., differences in letter grade A-F by ethnicity).