Intellectus Statistics: Available Analyses


Posted June 7, 2013

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.

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).

 


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