Prepare Questionnaire Data for Analysis
Add Significance Symbols to a Correlation Matrix
Add Significance Symbols to a (Atomic) Vector, Matrix, or Array
Data Information by Group
Aggregate an Atomic Vector by Group
Aggregate Data by Group
Amount of Missing Data - Bivariate (Pairwise Deletion)
Amount of Missing Data - Multivariate (Listwise Deletion)
Amount of Missing Data - Univariate
Autoregressive Coefficient by Group
Repeated Group Statistics for a Data-Frame
Bootstrapped Confidence Intervals from a Matrix of Coefficients
Column Means Conditional on Frequency of Observed Values
Generalizability Theory Reliability of a Multilevel Score
Generalizability Theory Reliability of a Score
Generalizability Theory Reliability of Multiple Multilevel Scores
Generalizability Theory Reliability of Multiple Scores
Intraclass Correlation for Multilevel Analysis: ICC(1,1)
All Six Intraclass Correlations by Group
Number of Cases in Data
Number of Groups in Data
Test for Sample Mean Against Mu (one-sample t-test)
Mean Changes Across Two Timepoints For Multiple PrePost Pairs of Varia...
Mean differences for multiple variables across 3+ independent groups (...
Mean differences across two independent groups (independent two-sample...
Test for Multiple Sample Means Against Mu (one-sample t-tests)
Recode Unique Values in a Character Vector to 0ther (or NA)
Apply a Function to Data by Group
Centering and/or Standardizing a Numeric Vector by Group
Centering and/or Standardizing a Numeric Vector
Centering and/or Standardizing Numeric Data by Group
Centering and/or Standardizing Numeric Data
Change Scores from a Numeric Vector by Group
Change Score from a Numeric Vector
Change Scores from Numeric Data by Group
Change Scores from Numeric Data
Frequency of Missing Values by Column
Column Sums Conditional on Frequency of Observed Values
Composite Reliability of a Score
Composite Reliability of Multiple Scores
Bootstrapped Confidence Intervals from a boot Object
Confidence Intervals from Parameter Estimates and Standard Errors
Confidence Intervals from Statistical Information
Correlation Matrix by Group
Point-biserial Correlations of Missingness
Multilevel Correlation Matrices
Bivariate Correlations with Significant Symbols by Group
Point-biserial Correlations of Missingness With Significant Symbols
Multilevel Correlation Matrices with Significant Symbols
Bivariate Correlations with Significant Symbols
Covariances Test of Significance
Cronbach's Alpha of a Set of Variables/Items
Cronbach's Alpha for Multiple Sets of Variables/Items
Decompose a Numeric Vector by Group
Decompose Numeric Data by Group
Design Effect from Multilevel Numeric Vector
Design Effects from Multilevel Numeric Data
Multilevel Descriptive Statistics
Bootstrap Function for cronbach() Function
Bootstrap Function for cronbachs() Function
Bootstrap Function for gtheory() Function
Bootstrap Function for gtheorys() Function
Dummy Variables to a Nominal Variable
Univariate Frequency Table By Group
Univariate Frequency Table
Multiple Univariate Frequency Tables
Multiple Univariate Frequency Tables
Intraclass Correlation for Multiple Variables for Multilevel Analysis:...
Length of a (Atomic) Vector by Group
Length of Data Columns by Group
Reshape Multiple Scores From Long to Wide
Make Dummy Columns
Mean Conditional on Minimum Frequency of Observed Values
Make Dummy Columns For Missing Data.
Make a Function Conditional on Frequency of Observed Values
Make Model Syntax for a Latent Factor in Lavaan
Make Product Terms (e.g., interactions)
Mean Change Across Two Timepoints (dependent two-samples t-test)
Mean differences for a single variable across 3+ independent groups (o...
Mean difference across two independent groups (independent two-samples...
Statistical Mode of a Numeric Vector
Test for Equal Frequency of Values (chi-square test of goodness of fit...
Number of Cases in Data by Group
Describe Number of Cases in Data by Group
Multilevel Number of Cases
Null Hypothesis Significance Testing
Nominal Variable to Dummy Variables
Number of Rows in Data by Group
Multilevel Number of Rows
Find Partial Cases
Recode a Numeric Vector to Percentage of Maximum Possible (POMP) Units
Recode Numeric Data to Percentage of Maximum Possible (POMP) Units
Proportion Comparisons for a Single Variable across 3+ Independent Gro...
Proportion Difference for a Single Variable across Two Independent Gro...
Test for Sample Proportion Against Pi (chi-square test of goodness of ...
Proportion Comparisons for Multiple Variables across 3+ Independent Gr...
Proportion Difference of Multiple Variables Across Two Independent Gro...
Test for Multiple Sample Proportion Against Pi (Chi-square Tests of Go...
Pre-processing Questionnaire Data
Recode Data
Rename Data Columns from a Codebook
Reorder Levels of Factor Data
Recode Invalid Values from a Vector
Recode Invalid Values from Data
Reverse Code a Numeric Vector
Reverse Code Numeric Data
Row Means Conditional on Frequency of Observed Values
Winsorize a Numeric Vector
Frequency of Missing Values by Row
Frequency of Multiple Sets of Missing Values by Row
Row Sums Conditional on Frequency of Observed Values
Observed Unweighted Scoring of a Set of Variables/Items
Observed Unweighted Scoring of Multiple Sets of Variables/Items
Shift a Vector (i.e., lag/lead) by Group
Shift a Vector (i.e., lag/lead)
Shift Data (i.e., lag/lead) by Group
Shift Data (i.e., lag/lead)
Sum Conditional on Minimum Frequency of Observed Values
Summary of a Unidimensional Confirmatory Factor Analysis
Apply a Function to a (Atomic) Vector by Group
Unidimensional Confirmatory Factor Analysis
Test for Invalid Elements in a Vector
Test for Invalid Elements in Data
Frequency of Missing Values in a Vector
Reshape Multiple Sets of Variables From Wide to Long
Winsorize Numeric Data
Offers a suite of functions to prepare questionnaire data for analysis (perhaps other types of data as well). By data preparation, I mean data analytic tasks to get your raw data ready for statistical modeling (e.g., regression). There are functions to investigate missing data, reshape data, validate responses, recode variables, score questionnaires, center variables, aggregate by groups, shift scores (i.e., leads or lags), etc. It provides functions for both single level and multilevel (i.e., grouped) data. With a few exceptions (e.g., ncases()), functions without an "s" at the end of their primary word (e.g., center_by()) act on atomic vectors, while functions with an "s" at the end of their primary word (e.g., centers_by()) act on multiple columns of a data.frame.