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EFA.dimensions - Exploratory Factor Analysis Functions for Assessing Dimensionality

Functions for an assortment of factor analysis-related procedures, including eleven procedures for determining the number of factors; for factor analysis with multiple options for methods of extraction and rotation; for bi-factor analysis; for extension factor analysis; options for running the analyses using either raw data or correlation matrices as input and with options for conducting the analyses using Pearson correlations, Kendall correlations, Spearman correlations, gamma correlations, or polychoric correlations; wrapper 'lavaan'-based functions for factorial invariance and exploratory structural equation modeling; functions for the factor-ability of a correlation matrix, for the congruence between factors from different datasets, for the assessment of local independence, for the assessment of factor solution complexity, for internal consistency, and for correcting Pearson correlation coefficients for attenuation due to unreliability. Auerswald & Moshagen (2019, <doi:10.1037/met0000200>); Field, Miles, & Field (2012, ISBN:978-1-4462-0045-2); Mulaik (2010, ISBN:978-1-4200-9981-2); O'Connor (2000, <doi:10.3758/bf03200807>).

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2.73 score 54 scripts 766 downloads

DFA.CANCOR - Linear Discriminant Function and Canonical Correlation Analysis

Produces SPSS- and SAS-like output for linear discriminant function analysis and canonical correlation analysis. The methods are described in Manly & Alberto (2017, ISBN:9781498728966), Rencher (2002, ISBN:0-471-41889-7), and Tabachnik & Fidell (2019, ISBN:9780134790541).

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2.00 score 2 scripts 566 downloads

SIMPLE.REGRESSION - OLS, Moderated, Logistic, and Count Regressions Made Simple

Provides SPSS- and SAS-like output for least squares multiple regression, logistic regression, and count variable regressions. Detailed output is also provided for OLS moderated regression, interaction plots, and Johnson-Neyman regions of significance. The output includes standardized coefficients, partial and semi-partial correlations, collinearity diagnostics, plots of residuals, and detailed information about simple slopes for interactions. The output for some functions includes Bayes Factors and, if requested, regression coefficients from Bayesian Markov Chain Monte Carlo analyses. There are numerous options for model plots. The REGIONS_OF_SIGNIFICANCE function also provides Johnson-Neyman regions of significance and plots of interactions for both lm and lme models. There is also a function for partial and semipartial correlations and a function for conducting Cohen's set correlation analyses.

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1.48 score 242 downloads

NO.PING.PONG - Incorporating Previous Findings When Evaluating New Data

Functions for revealing what happens when effect size estimates from previous studies are taken into account when evaluating each new dataset in a study sequence. The analyses can be conducted for cumulative meta-analyses and for Bayesian data analyses. The package contains sample data for a wide selection of research topics. Jointly considering previous findings along with new data is more likely to result in correct conclusions than does the traditional practice of not incorporating previous findings, which often results in a back and forth ping-pong of conclusions when evaluating a sequence of studies. O'Connor & Ermacora (2021, <doi:10.1037/cbs0000259>).

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1.00 score 384 downloads

Crosstabs.Loglinear - Cross Tabulation and Loglinear Analyses of Categorical Data

Provides 'SPSS'- and 'SAS'-like output for cross tabulations of two categorical variables (CROSSTABS) and for hierarchical loglinear analyses of two or more categorical variables (LOGLINEAR). The methods are described in Agresti (2013, ISBN:978-0-470-46363-5), Ajzen & Walker (2021, ISBN:9780429330308), Field (2018, ISBN:9781526440273), Norusis (2012, ISBN:978-0-321-74843-0), Nussbaum (2015, ISBN:978-1-84872-603-1), Stevens (2009, ISBN:978-0-8058-5903-4), Tabachnik & Fidell (2019, ISBN:9780134790541), and von Eye & Mun (2013, ISBN:978-1-118-14640-8).

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1.00 score 1 scripts 177 downloads

LagSequential - Lag-Sequential Categorical Data Analysis

Lag-sequential analysis is a method of assessing of patterns (what tends to follow what?) in sequences of codes. The codes are typically for discrete behaviors or states. The functions in this package read a stream of codes, or a frequency transition matrix, and produce a variety of lag sequential statistics, including transitional frequencies, expected transitional frequencies, transitional probabilities, z values, adjusted residuals, Yule's Q values, likelihood ratio tests of stationarity across time and homogeneity across groups or segments, transformed kappas for unidirectional dependence, bidirectional dependence, parallel and nonparallel dominance, and significance levels based on both parametric and randomization tests. The methods are described in Bakeman & Quera (2011) <doi:10.1017/CBO9781139017343>, O'Connor (1999) <doi:10.3758/BF03200753>, Wampold & Margolin (1982) <doi:10.1037/0033-2909.92.3.755>, and Wampold (1995, ISBN:0-89391-919-5).

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1.00 score 244 downloads