CROSSTABS {Crosstabs.Loglinear}

Cross tabulations of two categorical variables

Description

Provides 'SPSS'- and 'SAS'-like output for cross tabulations of two categorical variables. The input can be raw data, a contingency table, or a dataframe with cell frequency counts. The output includes the contingency table, expected frequencies, Pearson's' Chi-Square, Yates's Chi-Square (continuity correction), the Likelihood Ratio, Fisher's Exact p, the Linear-by-Linear Association.', the McNemar Test, the contingency coefficient C, phi, Cramer's V, Cohen's W, the residuals, standardized residuals, and adjusted residuals. Additional output for 2-by-2 tables includes the risk difference, the risk ratio, the odds ratio, and Yule's Q.

Usage

CROSSTABS(data, data_type = 'raw', variables=NULL, Freq = NULL, verbose=TRUE) 

Arguments

data

The input data, which can be raw data, a contingency table, or a dataframe with cell frequency counts (see the Examples below).

data_type

The kind of input data. The options are 'raw' (for raw data), 'cont.table' (for a two-dimensional contingency table), or 'counts' (for a dataframe with the cell frequency counts).

variables

(optional) The two variable names, which is required if data_type = 'raw' or 'counts', e.g., variables=c('varA','varB'). Not required if data_type = 'cont.table'.

Freq

(optional) If data_type = 'counts', then Freq is the name of the column in data that has the frequency counts. If unspecified, it will be assumed that the column is named 'Freq'.

verbose

(optional) Should detailed results be displayed in console?
The options are: TRUE (default) or FALSE.

Value

A list with the following possible elements:

obsFreqs

The observed frequencies.

expFreqs

The expected frequencies.

modEStab

Model test and effect size coefficients.

residuals

The residuals.

stdresiduals

The standardized residuals.

adjresiduals

The adjusted residuals.

EStab2x2

For a 2-by-2 contingency table, a list with the risk difference, the risk ratio, the odds ratio, and Yule's Q values.

Author(s)

Brian P. O'Connor

References

Agresti, A. (2013). Categorical data analysis (3rd ed). Hobokon, NJ: John Wiley & Sons.

Ajzen, R., & Walker, C. M. (2021). Categorical data analysis for the behavioral and social sciences (2nd ed.). New York, NY: Routledge.

Field, A. (2018). Chapter 18: Categorical data. Discovering statistics using SPSS (5th ed.). Los Angeles, CA: Sage.

Noursis, M. J. (2012). Chapter 1: Model selection loglinear analysis. IBM SPSS statistics 19: Advanced statistical procedures Companion. Upper Saddle River, NJ: Prentice Hall.

Stevens, J. P. (2009). Chapter 14: Categorical data analysis: The log linear model. Applied multivariate statistics for the social sciences (5th ed.). New York, NY: Routledge.

Tabachnick, B. G., & Fidell, L. S. (2019). Chapter 16: Multiway frequency analysis. Using multivariate statistics. New York, NY: Pearson.

Examples

# when 'data' is a raw data file (rather than counts/frequencies)
# Field (2018). Chapter 18: Categorical data -- cats only
CROSSTABS(data = subset(datasets$Field_2018_raw, Animal=='Cat'), 
          data_type = 'raw', 
          variables=c('Training','Dance') )


# when 'data' is a file with the counts/frequencies (rather than raw data points)
# Field (2018). Chapter 18: Categorical data -- cats only
CROSSTABS(data = subset(datasets$Field_2018, Animal=='Cat'), 
          data_type = 'counts', 
          variables=c('Training','Dance') )


# create and enter a two-dimensional contingency table for 'data'
# Field (2018). Chapter 18: Categorical data -- cats only
food <- c(28, 10)
affection <- c(48, 114)
Field_2018_cats_conTable <- rbind(food, affection) 
colnames(Field_2018_cats_conTable) <- c('danced', 'did not dance')
names(attributes(Field_2018_cats_conTable)$dimnames) <- c('Training','Dance') 
CROSSTABS(data = Field_2018_cats_conTable, data_type = 'cont.table')


# another way of creating the same two-dimensional contingency table for 'data'
# Field (2018). Chapter 18: Categorical data -- cats only
Field_2018_cats_conTable_2 <- matrix( c(28, 48, 10, 114), nrow = 2, ncol = 2)
colnames(Field_2018_cats_conTable_2) <- c('danced', 'did not dance')
rownames(Field_2018_cats_conTable_2) <- c('food', 'affection')
CROSSTABS(data = Field_2018_cats_conTable_2, data_type = 'cont.table')


# go to this web page to see many more examples of the CROSSTABS function analyses:
# https://oconnor-psych.ok.ubc.ca/loglinear/CROSSTABS_vignettes.html