Package {mariposa}

mariposa: 'SPSS'-Compatible Statistical Tools for Survey Data

Description

Statistical analysis of survey data with full support for survey weights, grouped operations, and 'tidyverse' integration. Provides 80 functions for data import/export ('SPSS', 'Stata', 'SAS', 'Excel') with label roundtripping and tagged NA preservation, label management (variable labels, value labels, type conversions, missing value declaration), data transformation (recoding, dummy coding, standardization, centering), descriptive statistics, codebook generation, hypothesis testing, correlation analysis, post-hoc comparisons, weighted statistics, scale analysis, regression, non-parametric tests, exact tests, factorial ANOVA, and ANCOVA. Every analysis offers compact print() and detailed summary() output with toggleable sections. Statistical results are validated against 'SPSS' version 29 within documented per-tier tolerances (see the compatibility vignette for per-function status). Methods follow the published algorithms of IBM Corp. (2023, "IBM SPSS Statistics Algorithms"), the Lilliefors-corrected normality test of Dallal and Wilkinson (1986) doi:10.1080/00031305.1986.10475419, and the adjusted standardized residuals of Haberman (1973) doi:10.2307/2529686. Designed for survey researchers, social scientists, and students working with complex survey designs.

Author(s)

Maintainer: Yannick Diehl yannick.diehl@gmail.com

Authors:

• Yannick Diehl yannick.diehl@gmail.com

See Also

Useful links:

• https://YannickDiehl.github.io/mariposa/

• https://github.com/YannickDiehl/mariposa

• Report bugs at https://github.com/YannickDiehl/mariposa/issues

Analysis of Covariance: ANCOVA

Description

ancova() tests whether group means differ after controlling for one or more continuous covariates. It performs a factorial ANCOVA using Type III Sum of Squares, matching SPSS UNIANOVA output with the WITH keyword.

Think of it as:

• An ANOVA that removes the effect of confounding variables first

• Testing group differences on "adjusted" means

• Combining regression (covariates) and ANOVA (factors) in one model

The test tells you:

• Whether each factor has a significant effect AFTER controlling for covariates

• The relationship between each covariate and the outcome

• Effect sizes for factors and covariates (partial eta squared)

• Estimated marginal means (group means adjusted for covariates)

Usage

ancova(data, dv, between, covariate, weights = NULL, ss_type = 3)

Arguments

Details

Understanding the Results

Adjusted Means (Estimated Marginal Means): These are the group means after statistically removing the effect of the covariate(s). They answer: "What would the group means be if all groups had the same covariate values?"

Covariate Effects: The covariate row in the ANOVA table shows whether the covariate significantly predicts the DV after adjusting for the factors.

Factor Effects: These show whether the factor affects the DV after controlling for the covariate. This is the primary test of interest.

Partial Eta Squared (Effect Size):

• Less than 0.01: Negligible

• 0.01 to 0.06: Small

• 0.06 to 0.14: Medium

• 0.14 or greater: Large

When to Use This

Use ANCOVA when:

• You have group comparisons (ANOVA) but want to control for a confound

• Your covariate is continuous and linearly related to the DV

• You want to increase statistical power by removing known variance sources

• The covariate's relationship with the DV is the same across groups (homogeneity of regression slopes assumption)

Value

An object of class "ancova" containing:

anova_table

Tibble with Source, SS, df, MS, F, p, Partial Eta Squared

parameter_estimates

Tibble with regression coefficients (B, SE, t, p)

descriptives

Tibble with unadjusted cell means, SDs, and Ns

estimated_marginal_means

Tibble with adjusted means (covariates at grand mean)

levene_test

Tibble with Levene's test results

r_squared

R-squared and Adjusted R-squared

model

The underlying lm model object

call_info

List with metadata (dv, factors, covariates, weighted, etc.)

Use summary() for the full SPSS-style output with toggleable sections.

References

Huitema, B. E. (2011). The Analysis of Covariance and Alternatives (2nd ed.). Wiley.

IBM Corp. (2023). IBM SPSS Statistics 29 Algorithms. IBM Corporation.

See Also

factorial_anova for ANOVA without covariates.

linear_regression for regression analysis.

oneway_anova for single-factor ANOVA.

summary.ancova for detailed output with toggleable sections.

Other hypothesis_tests: binomial_test(), chi_square(), chisq_gof(), factorial_anova(), fisher_test(), friedman_test(), kruskal_wallis(), mann_whitney(), mcnemar_test(), oneway_anova(), t_test(), wilcoxon_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# One-way ANCOVA: income by education, controlling for age
survey_data %>%
  ancova(dv = income, between = c(education), covariate = c(age))

# Two-way ANCOVA with weights
survey_data %>%
  ancova(dv = income, between = c(gender, education),
         covariate = c(age), weights = sampling_weight)

# Multiple covariates
survey_data %>%
  ancova(dv = income, between = c(education),
         covariate = c(age, political_orientation))

# --- Three-layer output ---
result <- ancova(survey_data, dv = income, between = c(education),
                 covariate = c(age))
result              # compact overview
summary(result)     # full detailed output with all sections
summary(result, marginal_means = FALSE)  # hide estimated marginal means

ANOVA for a linear_regression model

Description

For listwise + ungrouped results, dispatches to stats::anova.lm (sequential Type-I sum of squares per term). For the SPSS-style overall-model ANOVA table, use object$anova_table.

Usage

## S3 method for class 'linear_regression'
anova(object, ...)

Arguments

Value

An anova table (sequential Type-I sums of squares), as returned by stats::anova.lm.

ANOVA for a logistic_regression model

Description

For ungrouped results, dispatches to stats::anova.glm (sequential deviance per term). For the SPSS-style omnibus chi-square test, use object$omnibus_test.

Usage

## S3 method for class 'logistic_regression'
anova(object, ...)

Arguments

Value

An anova table (sequential analysis of deviance per term), as returned by stats::anova.glm.

Test Whether a Proportion Matches an Expected Value

Description

binomial_test() tests whether the observed proportion of a binary variable differs from a hypothesized proportion. It uses the exact binomial test, making it valid for any sample size.

Think of it as:

• Testing whether a coin is fair (proportion of heads = 50 percent)

• Checking if your sample's gender ratio matches the population

• Verifying if satisfaction rates meet a target proportion

The test tells you:

• Whether the observed proportion differs significantly from the expected

• The exact p-value (based on the binomial distribution)

• A confidence interval for the true proportion

Usage

binomial_test(data, ..., p = 0.5, weights = NULL, conf.level = 0.95)

Arguments

Details

Understanding the Results

P-value: If p < 0.05, the proportion differs significantly from the test proportion

• p < 0.001: Very strong evidence the proportion differs

• p < 0.01: Strong evidence the proportion differs

• p < 0.05: Moderate evidence the proportion differs

• p > 0.05: No significant difference from expected proportion

Observed Proportion: The actual proportion in your data. Compare this with the test proportion to see the direction of any difference.

Confidence Interval: The range likely to contain the true population proportion. If the test proportion falls outside this range, the result is significant.

When to Use This

Use the binomial test when:

• You want to compare an observed proportion to a known value

• Your variable has exactly 2 categories

• You need an exact test (not relying on normal approximation)

• Sample size is small (where chi-square may not be reliable)

Relationship to Other Tests

• For testing association between two categorical variables: Use chi_square() instead

• For comparing proportions between groups: Use chi-square or z-test for proportions

• For larger samples with normal approximation:

Weighted variants

SPSS NPAR TESTS ignores WEIGHT BY, so weighted results have no SPSS reference. The weighted variant is an R-only frequency-weight extension that reduces exactly to the unweighted test when all weights equal 1 (enforced by an internal invariance suite); see vignette("spss-compatibility") for validation status.

Results will be very similar to a one-sample z-test for proportions

Value

Test results showing whether the proportion differs from expected, including:

• Category counts and observed proportions

• Test proportion (null hypothesis)

• Exact p-value (two-sided)

• Confidence interval for the true proportion

References

Conover, W. J. (1999). Practical nonparametric statistics (3rd ed.). John Wiley & Sons.

See Also

binom.test for the base R exact binomial test.

chi_square for testing associations between categorical variables.

Other hypothesis_tests: ancova(), chi_square(), chisq_gof(), factorial_anova(), fisher_test(), friedman_test(), kruskal_wallis(), mann_whitney(), mcnemar_test(), oneway_anova(), t_test(), wilcoxon_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Test whether gender split is 50/50
survey_data %>%
  binomial_test(gender, p = 0.50)

# Test whether East region proportion is 50%
survey_data %>%
  binomial_test(region, p = 0.50)

# Multiple variables at once
survey_data %>%
  binomial_test(gender, region, p = 0.50)

# Weighted analysis
survey_data %>%
  binomial_test(gender, p = 0.50, weights = sampling_weight)

# Grouped analysis (separate test per region)
survey_data %>%
  group_by(region) %>%
  binomial_test(gender, p = 0.50)

Center Variables (Mean Centering)

Description

Centers variables by subtracting the mean. When used on a grouped data frame (via dplyr::group_by()), this becomes group-mean centering — the R equivalent of separate centering within each group.

Usage

center(data, ..., weights = NULL, suffix = NULL, na.rm = TRUE)

Arguments

Details

Grand-Mean vs. Group-Mean Centering

• Grand-mean centering (ungrouped): Subtracts the overall mean. A centered value of 0 means the respondent is at the sample average.

• Group-mean centering (grouped): Subtracts the group mean. Useful in multilevel models to separate within-group and between-group effects. This replaces sjmisc's separate de_mean() function.

Weighted Centering

When weights is provided, the weighted mean is used for centering. This accounts for survey design in the centering computation.

Value

If data is a vector, a centered numeric vector. If data is a data frame, the modified data frame (invisibly).

See Also

std() for full standardization (centering + scaling)

Other transform: std()

Examples

library(dplyr)
data(survey_data)

# Grand-mean centering
data <- center(survey_data, income, age, suffix = "_c")

# Weighted centering
data <- center(survey_data, income, age,
               weights = sampling_weight, suffix = "_c")

# Group-mean centering (replaces sjmisc::de_mean)
data <- survey_data %>%
  group_by(region) %>%
  center(income, age, suffix = "_gmc")

Test If Two Categories Are Related

Description

chi_square() helps you discover if two categorical variables are related or independent. For example, is education level related to voting preference? Or are they independent of each other?

The test tells you:

• Whether the relationship is statistically significant

• How strong the relationship is (effect sizes)

• What patterns exist in your data

Usage

chi_square(data, ..., weights = NULL, correct = FALSE)

Arguments

Details

Understanding the Results

P-value: If p < 0.05, the variables are likely related (not independent)

• p < 0.001: Very strong evidence of relationship

• p < 0.01: Strong evidence of relationship

• p < 0.05: Moderate evidence of relationship

• p >= 0.05: No significant relationship found

Effect Sizes (How strong is the relationship?):

• Cramer's V: Works for any table size (0 = no relationship, 1 = perfect relationship)

  • < 0.1: Negligible relationship

  • 0.1-0.3: Small relationship

  • 0.3-0.5: Medium relationship

  • 0.5 or higher: Large relationship

• Phi: Only for 2x2 tables (similar interpretation as Cramer's V)

• Gamma: For ordinal data (-1 to +1, shows direction of relationship)

When to Use This

Use chi-squared test when:

• Both variables are categorical (gender, region, education level, etc.)

• You want to know if they're related or independent

• You have at least 5 observations in most cells

Reading the Frequency Tables

• Observed: What you actually found in your data

• Expected: What you'd expect if variables were independent

• Large differences suggest a relationship exists

Tips for Success

• Check that most cells have at least 5 observations

• Use weights for population estimates

• Look at both significance (p-value) and strength (effect sizes)

• Consider using crosstab() for detailed percentage breakdowns

Value

Test results showing whether the variables are related, including:

• Chi-squared statistic and p-value

• Observed vs expected frequencies

• Effect sizes to measure relationship strength Use summary() for the full SPSS-style output with toggleable sections.

References

Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302), 157–175.

Cramer, H. (1946). Mathematical Methods of Statistics. Princeton University Press.

IBM Corp. (2023). IBM SPSS Statistics 29 Algorithms. IBM Corporation.

See Also

chisq.test for the base R chi-squared test.

crosstab for detailed cross-tabulation tables.

frequency for single-variable frequency tables.

summary.chi_square for detailed output with toggleable sections.

Other hypothesis_tests: ancova(), binomial_test(), chisq_gof(), factorial_anova(), fisher_test(), friedman_test(), kruskal_wallis(), mann_whitney(), mcnemar_test(), oneway_anova(), t_test(), wilcoxon_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Basic chi-squared test for independence
survey_data %>% chi_square(gender, region)

# With weights
survey_data %>% chi_square(gender, education, weights = sampling_weight)

# Grouped analysis
survey_data %>% 
  group_by(region) %>% 
  chi_square(gender, employment)

# With continuity correction
survey_data %>% chi_square(gender, region, correct = TRUE)

# --- Three-layer output ---
result <- chi_square(survey_data, gender, education)
result              # compact one-line overview
summary(result)     # full detailed output with all sections
summary(result, cross_tabulation = FALSE)  # hide cross-tabulation

Chi-Square Goodness-of-Fit Test

Description

chisq_gof() tests whether observed frequencies of a categorical variable match expected frequencies. By default, it tests against equal proportions (uniform distribution). You can also specify custom expected proportions.

Think of it as:

• Testing whether categories are equally distributed

• Comparing observed distribution to a theoretical distribution

• The one-sample version of the chi-square test

The test tells you:

• Whether observed frequencies differ from expected frequencies

• How strong the deviation is (chi-square statistic)

• A frequency table with observed, expected, and residual counts

Usage

chisq_gof(data, ..., expected = NULL, weights = NULL)

Arguments

Details

Understanding the Results

P-value: If p < 0.05, the distribution differs from expected

• p < 0.001: Very strong evidence the distribution differs

• p < 0.01: Strong evidence the distribution differs

• p < 0.05: Moderate evidence the distribution differs

• p >= 0.05: No significant deviation from expected distribution

Residuals: The difference between observed and expected counts. Large positive residuals indicate a category has more cases than expected; large negative residuals indicate fewer cases than expected.

When to Use This

Use this test when:

• You want to check whether a categorical variable follows a specific distribution

• You want to test if categories are equally distributed (uniform)

• You have a single categorical variable and a hypothesised distribution

The Chi-Square Goodness-of-Fit Statistic

\chi^2 = \sum \frac{(O_i - E_i)^2}{E_i}

where O_i = observed frequency, E_i = expected frequency.

Degrees of freedom = number of categories - 1.

Relationship to Other Tests

• For testing association between two categorical variables: Use chi_square() instead

• For testing a single binary proportion: Use binomial_test() instead

• For small samples where expected frequencies are below 5: Use fisher_test() instead

SPSS Equivalent

SPSS: NPAR TESTS /CHISQUARE=variable /EXPECTED=EQUAL or: NPAR TESTS /CHISQUARE=variable /EXPECTED=50 30 20

Value

Test results showing whether observed frequencies match expected, including:

• Chi-square statistic (chi_squared) and p-value for each variable

• Degrees of freedom

• Frequency table with observed, expected, and residual counts

• Sample size (N)

References

Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302), 157-175.

See Also

chi_square for chi-square test of independence (two variables).

binomial_test for testing a single proportion.

Other hypothesis_tests: ancova(), binomial_test(), chi_square(), factorial_anova(), fisher_test(), friedman_test(), kruskal_wallis(), mann_whitney(), mcnemar_test(), oneway_anova(), t_test(), wilcoxon_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Test whether gender is equally distributed
survey_data %>%
  chisq_gof(gender)

# Test multiple variables at once
survey_data %>%
  chisq_gof(gender, region, education)

# Custom expected proportions
survey_data %>%
  chisq_gof(interview_mode, expected = c(0.5, 0.3, 0.2))

# With weights
survey_data %>%
  chisq_gof(gender, weights = sampling_weight)

# Grouped analysis
survey_data %>%
  group_by(region) %>%
  chisq_gof(education)

Create a Codebook for Your Data

Description

codebook() creates an interactive HTML data dictionary that opens in the RStudio Viewer. It shows you everything about your dataset at a glance: variable names, types, labels, the empirical values found in the data, value labels, and frequency counts.

Think of it as a professional "cheat sheet" for your dataset – especially useful when working with labelled survey data imported from SPSS, Stata, or SAS.

Usage

codebook(
  data,
  ...,
  weights = NULL,
  show_id = TRUE,
  show_type = TRUE,
  show_labels = TRUE,
  show_values = TRUE,
  show_freq = TRUE,
  show_na = TRUE,
  show_unused = FALSE,
  max_values = 10,
  max_len = 50,
  sort_by_name = FALSE,
  file = NULL,
  view = interactive()
)

Arguments

Details

What the Codebook Shows

For each variable, the codebook displays (depending on options):

• ID: Column position in the dataset

• Name: Variable name (in monospace font)

• Type: Data type (numeric, factor, ordered factor, haven_labelled, etc.)

• Label: Variable label (from SPSS/Stata/SAS imports or manual assignment)

• Values: The empirical values found in the data

• Value Labels: Labels assigned to those values (if any)

• Freq.: Frequency count for each value

When to Use This

Use codebook() when you:

• First receive a new dataset and want to understand its structure

• Work with labelled data (SPSS, Stata, SAS) and need to see all value labels

• Want to document your dataset for colleagues or publications

• Need to quickly see value distributions across variables

Value

Invisibly returns a list of class "codebook" containing:

codebook

Tibble with one row per variable and all metadata

data_info

List with dataset-level information (name, nrow, ncol, etc.)

html

The generated HTML as an htmltools object

weights

Name of the weight variable, or NULL

options

List of all display options

frequencies

Named list of frequency tables per variable

See Also

describe() for detailed numeric summaries, frequency() for detailed frequency tables, read_spss(), read_por(), read_stata(), read_sas(), read_xpt() for importing data with tagged NAs

Examples

data(survey_data)

# Compact console overview (no Viewer)
cb <- codebook(survey_data)
print(cb)

# Detailed console output
summary(cb)


# Full codebook (opens in RStudio Viewer when interactive)
codebook(survey_data)

# Only trust-related variables
codebook(survey_data, starts_with("trust"))

# Save to file for sharing
codebook(survey_data, file = tempfile(fileext = ".html"))

Copy Labels from One Data Frame to Another

Description

Copies variable labels, value labels, and tagged NA metadata from a source data frame to a target data frame. This is essential after dplyr operations like dplyr::filter(), dplyr::select(), or dplyr::mutate() which can strip label attributes.

Usage

copy_labels(data, source)

Arguments

Details

The following attributes are copied for each shared column:

• "label" — variable label

• "labels" — value labels

• "na_tag_map" — tagged NA mapping

• "na_tag_format" — tagged NA format (spss/stata/sas)

• "class" — vector class (e.g., haven_labelled)

Value

The target data frame with labels copied from the source. Only columns present in both data frames are affected. Columns only in the target are left unchanged.

See Also

var_label(), val_labels()

Other labels: drop_labels(), find_var(), set_na(), to_character(), to_label(), to_labelled(), to_numeric(), unlabel(), val_labels(), var_label()

Examples

# Labels are lost after dplyr operations
data_subset <- dplyr::filter(survey_data, age >= 18)

# Restore them
data_subset <- copy_labels(data_subset, survey_data)

Compare Two Categories: See How They Relate

Description

crosstab() shows you how two categorical variables relate to each other. It creates a table that reveals patterns - like whether education level differs by region, or if gender influences product preferences.

Think of it as a two-way frequency table that shows:

• How many people fall into each combination of categories

• What percentage each cell represents

• Whether there are patterns or associations

Usage

crosstab(
  data,
  row,
  col,
  weights = NULL,
  percentages = c("row", "none", "col", "total", "all"),
  na.rm = TRUE,
  digits = 1
)

Arguments

Details

Understanding the Results

The crosstab table shows:

• Cell counts: Number of people in each combination

• Row %: Distribution within each row (e.g., "Among those with high school education, X% live in the East")

• Column %: Distribution within each column (e.g., "Among those in the East, X% have high school education")

• Total %: Percentage of the entire sample (e.g., "X% of all respondents have high school education AND live in the East")

• Adjusted residuals (via summary(result, residuals = TRUE), SPSS /CELLS=ASRESID): which cells deviate from independence. After a significant chi_square test, cells with |adj. residual| > 2 are the ones driving the association. For weighted tables the residuals are computed on the unrounded weighted cell counts; an SPSS v29 reference run for the residuals is pending, so they are currently verified against the Haberman formula (chisq.test()$stdres) rather than SPSS output.

When to Use This

Use crosstab when you want to:

• See if two categorical variables are related

• Compare distributions across groups

• Find patterns in survey responses

• Create demographic breakdowns

Choosing Percentages

• Row %: Use when your row variable is the grouping factor (e.g., "How does region vary BY education level?")

• Column %: Use when your column variable is the grouping factor (e.g., "How does education vary BY region?")

• Total %: Use to understand the overall sample composition

Tips for Success

• Start with row or column percentages, not both at once

• Use chi-squared test to check if the relationship is statistically significant

• Watch for small cell counts (< 5) which may be unreliable

• Consider combining sparse categories if many cells are empty

Value

A cross-tabulation table showing the relationship between two variables. The result also carries the expected cell counts ($expected) and adjusted standardized residuals ($adj_residuals); display the residuals with summary(result, residuals = TRUE).

See Also

table for base R contingency tables.

frequency for single-variable frequency tables.

chi_square for testing if the cross-tabulated variables are related.

Other descriptive: describe(), frequency(), multiple_response(), normality_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Basic crosstab
survey_data %>% crosstab(gender, region)

# With weights and all percentages
survey_data %>% crosstab(gender, education,
                         weights = sampling_weight,
                         percentages = "all")

# Grouped analysis
survey_data %>%
  group_by(employment) %>%
  crosstab(gender, region, weights = sampling_weight)

# Column percentages only
survey_data %>% crosstab(education, employment, percentages = "col")

Get to Know Your Numeric Data

Description

describe() gives you a complete summary of numeric variables - like age, income, or satisfaction scores. It's your first step in any analysis, helping you understand what's typical, what's unusual, and how spread out your data is.

Think of it as a health check for your data that reveals:

• What's the average value?

• What's the middle value?

• How spread out are the responses?

• Are there unusual patterns or outliers?

Usage

describe(
  data,
  ...,
  weights = NULL,
  show = "short",
  probs = c(0.25, 0.5, 0.75),
  na.rm = TRUE,
  excess = TRUE
)

Arguments

Details

Understanding the Results

Key statistics and what they tell you:

• n: How many valid responses (watch for too many missing)

• Mean: The average value

• Median: The middle value (half above, half below)

• SD: Standard deviation - how spread out values are

• Range: The minimum and maximum values

• IQR: Interquartile range - the middle 50% of values

• Skewness: Whether data leans left (negative) or right (positive)

• Kurtosis: Whether you have unusual outliers

When to Use This

Always start here! Use describe() to:

• Check data quality (impossible values?)

• Understand distributions before testing

• Spot outliers that might affect analyses

• Compare groups side by side

Interpreting Patterns

• Mean close to Median: Data is roughly symmetric

• Mean > Median: Right-skewed (tail extends right)

• Mean < Median: Left-skewed (tail extends left)

• Large SD: Responses vary widely

• Small SD: Responses are similar

Value

A summary table with descriptive statistics for each variable

See Also

summary for base R summary statistics.

frequency for categorical variable summaries.

w_mean, w_sd, w_median for individual weighted statistics.

t_test and oneway_anova for group comparisons.

Other descriptive: crosstab(), frequency(), multiple_response(), normality_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Basic unweighted analysis
survey_data %>% describe(age)

# Weighted analysis
survey_data %>% describe(age, weights = sampling_weight)

# Multiple variables with custom statistics
survey_data %>% describe(age, income, life_satisfaction, 
                        weights = sampling_weight, 
                        show = c("mean", "sd", "skew"))

# Grouped analysis
survey_data %>% 
  group_by(region) %>% 
  describe(age, weights = sampling_weight)

Remove Unused Value Labels

Description

Removes value labels for values that are not present in the data. This is useful after filtering or subsetting, when some categories may no longer exist but their labels remain attached.

Usage

drop_labels(data, ..., drop_na = FALSE)

Arguments

Details

This is useful after subsetting data (e.g., filtering out a category). The removed category's label still exists on the variable, which can cause confusing output in frequency() or codebook(). drop_labels() cleans this up by keeping only labels for values actually present in the data.

By default, tagged NA labels are preserved (drop_na = FALSE) because they represent missing value types, not substantive categories.

Value

The data with unused labels removed.

See Also

val_labels(), copy_labels()

Other labels: copy_labels(), find_var(), set_na(), to_character(), to_label(), to_labelled(), to_numeric(), unlabel(), val_labels(), var_label()

Examples

# After filtering, region == 4 no longer exists but its label remains
data_subset <- dplyr::filter(survey_data, region != 4)
data_clean <- drop_labels(data_subset)

Find Which Specific Groups Differ After Kruskal-Wallis

Description

dunn_test() tells you exactly which groups are different from each other after Kruskal-Wallis finds overall differences. It's the non-parametric equivalent of a Tukey or Scheffe post-hoc test.

Think of it as:

• Kruskal-Wallis says "there are differences somewhere"

• Dunn test says "specifically, Group A differs from Group C"

• A way to make all possible pairwise comparisons using rank-based statistics

Usage

dunn_test(x, ...)

## Default S3 method:
dunn_test(x, ...)

Arguments

Details

Understanding the Results

Z-Statistics: Based on differences in mean ranks between groups

• Large absolute Z values indicate big rank differences

• Positive Z: First group has higher mean rank than second

• Negative Z: Second group has higher mean rank than first

Adjusted P-values: Control for multiple comparisons

• p < 0.05: Groups are significantly different

• p >= 0.05: No significant difference between these groups

The Dunn Test Formula

For each pair of groups (i, j):

Z_{ij} = \frac{\bar{R}_i - \bar{R}_j}{\sqrt{\frac{N(N+1)}{12} \left(\frac{1}{n_i} + \frac{1}{n_j}\right)}}

where \bar{R}_i is the mean rank for group i, N is the total sample size, and n_i is the size of group i.

P-Value Adjustment Methods

• Bonferroni (default): Most conservative, multiplies p by number of comparisons

• Holm: Step-down method, less conservative than Bonferroni

• BH: Controls false discovery rate, good for many comparisons

When to Use This

Use Dunn test when:

• Your Kruskal-Wallis test shows significant differences (p < 0.05)

• You want to know which specific groups differ

• Your data violates normality assumptions

• You have ordinal data or skewed distributions

Relationship to Other Tests

• Non-parametric equivalent of tukey_test() and scheffe_test()

• Follow-up to kruskal_wallis(), just like Tukey follows ANOVA

Weighted variants

When the parent kruskal_wallis() result is weighted, the pairwise z statistics use the same frequency-weighted midranks. SPSS NPAR TESTS ignores WEIGHT BY, so weighted results have no SPSS reference (R-only, guarded by an internal invariance suite); see vignette("spss-compatibility") for validation status.

• Uses ranks instead of raw values, making it robust to outliers

Value

Pairwise comparison results showing:

• Which group pairs are significantly different

• Z-statistics based on rank differences

• Adjusted p-values (controlling for multiple comparisons)

References

Dunn, O. J. (1964). Multiple comparisons using rank sums. Technometrics, 6(3), 241-252.

See Also

kruskal_wallis for performing Kruskal-Wallis tests.

tukey_test for parametric post-hoc comparisons after ANOVA.

scheffe_test for conservative parametric post-hoc comparisons.

Other posthoc: levene_test(), pairwise_wilcoxon(), scheffe_test(), tukey_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Perform Kruskal-Wallis followed by Dunn post-hoc test
kw_result <- survey_data %>%
  kruskal_wallis(life_satisfaction, group = education)

# Dunn post-hoc comparisons (default: Bonferroni)
kw_result %>% dunn_test()

# With Holm correction (less conservative)
kw_result %>% dunn_test(p_adjust = "holm")

# With Benjamini-Hochberg (controls false discovery rate)
kw_result %>% dunn_test(p_adjust = "BH")

# With weights
kw_weighted <- survey_data %>%
  kruskal_wallis(life_satisfaction, group = education,
                 weights = sampling_weight)

kw_weighted %>% dunn_test()

# Multiple variables
kw_multi <- survey_data %>%
  kruskal_wallis(life_satisfaction, trust_government,
                 group = education)

kw_multi %>% dunn_test()

# Grouped analysis
kw_grouped <- survey_data %>%
  group_by(region) %>%
  kruskal_wallis(life_satisfaction, group = education)

kw_grouped %>% dunn_test()

Explore the Structure Behind Your Survey Items

Description

efa() performs Exploratory Factor Analysis (EFA) to discover underlying patterns in your survey items. Supports both Principal Component Analysis (PCA) and Maximum Likelihood (ML) extraction. This is the R equivalent of SPSS's FACTOR procedure.

For example, if you have 6 items measuring different attitudes, EFA can reveal whether they group into 2-3 underlying dimensions (factors or components).

Usage

efa(
  data,
  ...,
  n_factors = NULL,
  rotation = "varimax",
  extraction = "pca",
  weights = NULL,
  use = "pairwise",
  sort = TRUE,
  blank = 0.4,
  na.rm = TRUE
)

Arguments

Details

Understanding the Results

KMO (Kaiser-Meyer-Olkin) measures sampling adequacy:

• KMO > 0.90: Marvelous

• KMO 0.80 - 0.90: Meritorious

• KMO 0.70 - 0.80: Middling

• KMO 0.60 - 0.70: Mediocre

• KMO 0.50 - 0.60: Miserable

• KMO < 0.50: Unacceptable - don't use factor analysis

Bartlett's Test of Sphericity tests whether correlations are significantly different from zero. A significant result (p < .05) means the correlation matrix is suitable for factor analysis.

Eigenvalues indicate how much variance each component explains. The Kaiser criterion retains components with eigenvalue > 1.

Factor Loadings show how strongly each item relates to each component:

• |loading| > 0.70: Strong association

• |loading| 0.40 - 0.70: Moderate association

• |loading| < 0.40: Weak (suppressed by default)

Communalities show how much of each item's variance is explained by the extracted components. Low communalities (< 0.40) suggest the item doesn't fit well with the others.

Choosing an Extraction Method

• PCA (default): Extracts components explaining maximum total variance. Simple and robust. Does not assume normality.

• ML: Extracts factors explaining shared variance only. Assumes multivariate normality. Provides a goodness-of-fit test to evaluate model fit. A non-significant chi-square (p > .05) suggests adequate fit.

Choosing a Rotation

• Varimax (default): Assumes factors are uncorrelated. Produces simpler, easier-to-interpret results.

• Oblimin: Allows factors to be correlated. More realistic for social science data. Produces both a Pattern Matrix (unique contributions) and Structure Matrix (total correlations).

• Promax: Oblique rotation based on a power transformation of Varimax results. Like Oblimin, produces Pattern and Structure matrices. Common alternative to Oblimin in SPSS.

• None: No rotation. Rarely useful for interpretation.

Value

An efa result object containing:

loadings

Component loading matrix (rotated if rotation applied)

unrotated_loadings

Unrotated component matrix

eigenvalues

All eigenvalues from the correlation matrix

variance_explained

Tibble with Total, % of Variance, Cumulative %

rotation_variance

Tibble with rotation sums of squared loadings

communalities

Extraction communalities for each variable

kmo

List with overall KMO and per-item MSA values

bartlett

List with chi_sq, df, and p_value

rotation

Rotation method used

extraction

Extraction method used

n_factors

Number of components extracted

correlation_matrix

Correlation matrix used for analysis

initial_communalities

Initial communalities (1.0 for PCA, SMC for ML)

goodness_of_fit

Goodness-of-fit test (ML only): chi_sq, df, p_value. NULL for PCA.

uniquenesses

Unique variances per variable (ML only, NULL for PCA)

pattern_matrix

Pattern matrix (oblimin/promax only, NULL otherwise)

structure_matrix

Structure matrix (oblimin/promax only, NULL otherwise)

factor_correlations

Factor correlation matrix (oblimin/promax only, NULL otherwise)

variables

Character vector of variable names

weights

Weights variable name or NULL

n

Sample size

col_prefix

Column name prefix: "PC" for PCA, "Factor" for ML

sort

Whether loadings are sorted

blank

Suppression threshold

Use summary() for the full SPSS-style output with toggleable sections.

See Also

reliability for checking scale reliability before creating indices.

row_means for creating mean indices after identifying factors.

summary.efa for detailed output with toggleable sections.

Other scale: pomps(), reliability(), row_count(), row_means(), row_sums()

Examples

library(dplyr)
data(survey_data)

# Basic EFA with Varimax rotation
efa(survey_data,
    political_orientation, environmental_concern, life_satisfaction,
    trust_government, trust_media, trust_science)

# With Oblimin rotation (requires GPArotation package)

if (requireNamespace("GPArotation", quietly = TRUE)) {
  efa(survey_data,
      political_orientation, environmental_concern, life_satisfaction,
      trust_government, trust_media, trust_science,
      rotation = "oblimin")
}


# Maximum Likelihood extraction
efa(survey_data,
    political_orientation, environmental_concern, life_satisfaction,
    trust_government, trust_media, trust_science,
    extraction = "ml")

# Promax rotation (oblique)
efa(survey_data,
    political_orientation, environmental_concern, life_satisfaction,
    trust_government, trust_media, trust_science,
    rotation = "promax")

# Fix number of factors
efa(survey_data,
    political_orientation, environmental_concern, life_satisfaction,
    trust_government, trust_media, trust_science,
    n_factors = 2)

# With survey weights
efa(survey_data,
    political_orientation, environmental_concern, life_satisfaction,
    trust_government, trust_media, trust_science,
    weights = sampling_weight)

# Grouped by region
survey_data %>%
  group_by(region) %>%
  efa(political_orientation, environmental_concern, life_satisfaction,
      trust_government, trust_media, trust_science)

# --- Three-layer output ---
result <- efa(survey_data, political_orientation, environmental_concern,
              life_satisfaction, trust_government, trust_media, trust_science)
result              # compact overview
summary(result)     # full detailed output with all sections
summary(result, communalities = FALSE)  # hide communalities table

Compare Groups Across Multiple Factors: Factorial ANOVA

Description

factorial_anova() tests whether group means differ across two or more factors simultaneously, including their interactions. It performs a factorial (two-way, three-way) ANOVA using Type III Sum of Squares, matching SPSS UNIANOVA output.

Think of it as:

• Testing multiple grouping variables at once

• Detecting interaction effects (do factor combinations matter?)

• An extension of one-way ANOVA to multiple factors

The test tells you:

• Whether each factor has a main effect on the outcome

• Whether factors interact (the effect of one depends on the other)

• How much variance each factor explains (partial eta squared)

Usage

factorial_anova(data, dv, between, weights = NULL, ss_type = 3)

Arguments

Details

Understanding the Results

Main Effects: Does each factor independently affect the outcome?

• Significant main effect = group means differ for that factor

• Example: Education affects income regardless of gender

Interaction Effects: Does the effect of one factor depend on another?

• Significant interaction = the pattern differs across factor combinations

• Example: The gender gap in income varies by education level

Partial Eta Squared (Effect Size):

• Less than 0.01: Negligible

• 0.01 to 0.06: Small

• 0.06 to 0.14: Medium

• 0.14 or greater: Large

Type III Sum of Squares

Type III SS tests each effect after adjusting for all other effects. This is the standard in SPSS and recommended for unbalanced designs (unequal cell sizes). It uses orthogonal contrasts (contr.sum) internally.

When to Use This

Use factorial ANOVA when:

• You have one numeric outcome variable

• You have 2-3 categorical grouping factors

• You want to test main effects AND interactions

• Your data is approximately normally distributed within cells

What Comes Next?

If the ANOVA is significant:

1. Check which effects are significant (main effects vs. interactions)

2. Use tukey_test() for post-hoc comparisons on main effects

3. Examine cell means to interpret interaction patterns

4. Consider effect sizes for practical significance

Value

An object of class "factorial_anova" containing:

anova_table

Tibble with Source, SS, df, MS, F, p, Partial Eta Squared

descriptives

Tibble with cell means, SDs, and Ns for each factor combination

levene_test

Tibble with Levene's test results (F, df1, df2, p)

r_squared

R-squared and Adjusted R-squared

model

The underlying model object for S3 dispatch

call_info

List with metadata (dv, factors, weighted, n_total, n_missing)

Use summary() for the full SPSS-style output with toggleable sections.

References

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.

IBM Corp. (2023). IBM SPSS Statistics 29 Algorithms. IBM Corporation.

Maxwell, S. E., & Delaney, H. D. (2004). Designing Experiments and Analyzing Data (2nd ed.). Lawrence Erlbaum Associates.

See Also

oneway_anova for single-factor ANOVA.

tukey_test for post-hoc pairwise comparisons.

levene_test for testing homogeneity of variances.

ancova for ANOVA with covariates.

summary.factorial_anova for detailed output with toggleable sections.

Other hypothesis_tests: ancova(), binomial_test(), chi_square(), chisq_gof(), fisher_test(), friedman_test(), kruskal_wallis(), mann_whitney(), mcnemar_test(), oneway_anova(), t_test(), wilcoxon_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Two-way ANOVA: income by gender and education
survey_data %>%
  factorial_anova(dv = income, between = c(gender, education))

# Two-way ANOVA with weights
survey_data %>%
  factorial_anova(dv = life_satisfaction, between = c(gender, region),
                  weights = sampling_weight)

# Three-way ANOVA
survey_data %>%
  factorial_anova(dv = income, between = c(gender, region, education))

# Follow up with post-hoc tests
result <- survey_data %>%
  factorial_anova(dv = income, between = c(gender, education))
result %>% tukey_test()
result %>% levene_test()

# --- Three-layer output ---
result              # compact overview
summary(result)     # full detailed output with all sections
summary(result, marginal_means = FALSE)  # hide estimated marginal means

Find Variables by Name or Label

Description

Searches for variables in your data by matching a pattern against variable names, variable labels, or both. This is especially useful for SPSS datasets where variable names are often cryptic codes (e.g., v104, q23a_1) and the actual meaning is stored in variable labels.

Usage

find_var(data, pattern, search = c("name_label", "name", "label"))

Arguments

Details

When to Use This

• You imported an SPSS file and need to find which variable contains "trust" or "satisfaction"

• You want to quickly identify all variables related to a topic

• You know the German/English label text but not the variable code

Pattern Matching

The pattern argument supports regular expressions. Matching is case-insensitive. Some examples:

• "trust" — matches "trust", "Trust", "distrust", "trustworthy"

• "^trust" — matches only names/labels starting with "trust"

• "^q[0-9]+" — matches variable names like q1, q23, q104

• "zufried" — finds German labels containing "Zufriedenheit"

Value

A data frame with columns:

col

Column position in the data

name

Variable name

label

Variable label (or "" if none)

See Also

var_label() for getting/setting variable labels, val_labels() for value labels

Other labels: copy_labels(), drop_labels(), set_na(), to_character(), to_label(), to_labelled(), to_numeric(), unlabel(), val_labels(), var_label()

Examples

library(dplyr)
data(survey_data)

# Find all variables related to "trust"
find_var(survey_data, "trust")

# Search only in variable labels
find_var(survey_data, "satisfaction", search = "label")

# Use regex to find numbered items
find_var(survey_data, "^q[0-9]+", search = "name")

Fisher's Exact Test for Small Samples

Description

fisher_test() performs Fisher's exact test for independence in a contingency table. Use this instead of chi_square() when your sample is small or when expected cell frequencies are below 5.

Think of it as:

• An exact alternative to the chi-square test of independence

• Best for small samples where the chi-square approximation may be inaccurate

• SPSS automatically reports Fisher's Exact Test for 2x2 tables

The test tells you:

• Whether two categorical variables are related (exact p-value)

• The contingency table of observed frequencies

• Results that are valid even with very small samples

Usage

fisher_test(data, row, col, weights = NULL, ...)

Arguments

Details

Understanding the Results

P-value: If p < 0.05, the variables are likely related (not independent)

• p < 0.001: Very strong evidence of relationship

• p < 0.01: Strong evidence of relationship

• p < 0.05: Moderate evidence of relationship

• p >= 0.05: No significant relationship found

Unlike the chi-square test which uses a large-sample approximation, Fisher's exact test computes the exact probability of observing the given table (or a more extreme one) under the null hypothesis of independence.

For tables larger than 2x2, the Fisher-Freeman-Halton extension is used.

When to Use This

Use Fisher's exact test when:

• Any expected cell frequency is less than 5

• Your total sample size is small (typically N < 30)

• You have a 2x2 table (Fisher's exact is standard here)

• You want an exact p-value rather than an approximation

Relationship to Other Tests

• For large samples with all expected frequencies >= 5: Use chi_square() instead

• For paired binary data (before/after): Use mcnemar_test() instead

• For testing a single proportion: Use binomial_test() instead

SPSS Equivalent

SPSS: CROSSTABS /STATISTICS=CHISQ (Fisher's exact test is automatically reported for 2x2 tables)

Value

Test results showing whether two categorical variables are related, including:

• Exact p-value (two-sided)

• Contingency table of observed frequencies

• Sample size (N)

References

Fisher, R. A. (1922). On the interpretation of chi-square from contingency tables, and the calculation of P. Journal of the Royal Statistical Society, 85(1), 87-94.

Freeman, G. H., & Halton, J. H. (1951). Note on an exact treatment of contingency, goodness of fit and other problems of significance. Biometrika, 38(1/2), 141-149.

See Also

chi_square for chi-square test of independence (large samples).

mcnemar_test for paired proportions.

Other hypothesis_tests: ancova(), binomial_test(), chi_square(), chisq_gof(), factorial_anova(), friedman_test(), kruskal_wallis(), mann_whitney(), mcnemar_test(), oneway_anova(), t_test(), wilcoxon_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Basic 2x2 Fisher test
survey_data %>%
  fisher_test(row = gender, col = region)

# Fisher test for larger table
survey_data %>%
  fisher_test(row = gender, col = interview_mode)

# With weights
survey_data %>%
  fisher_test(row = gender, col = region, weights = sampling_weight)

# Grouped analysis
survey_data %>%
  group_by(education) %>%
  fisher_test(row = gender, col = region)

Count How Many People Chose Each Option

Description

frequency() helps you understand categorical data by showing how many people chose each option. It's perfect for survey questions with fixed choices like education level, yes/no questions, or rating scales.

Think of it as creating a summary table that shows:

• How many people chose each option

• What percentage that represents

• Running totals to see cumulative patterns

Usage

frequency(
  data,
  ...,
  weights = NULL,
  sort_frq = "none",
  show_na = TRUE,
  show_prc = TRUE,
  show_valid = TRUE,
  show_sum = TRUE,
  show_labels = "auto",
  show_unused = FALSE,
  sort.frq = NULL,
  show.na = NULL,
  show.prc = NULL,
  show.valid = NULL,
  show.sum = NULL,
  show.labels = NULL,
  show.unused = NULL
)

fre(data, ..., weights = NULL, sort_frq = "none", show_na = TRUE,
  show_prc = TRUE, show_valid = TRUE, show_sum = TRUE, show_labels = "auto",
  show_unused = FALSE, sort.frq = NULL, show.na = NULL, show.prc = NULL,
  show.valid = NULL, show.sum = NULL, show.labels = NULL, show.unused = NULL)

Arguments

Details

Understanding the Results

The frequency table shows:

• Freq: Number of responses in each category

• %: Percentage including missing values (use for "response rate")

• Valid %: Percentage excluding missing values (use for "among those who answered")

• Cum %: Running total percentage (helps identify cutoff points)

When to Use This

Use frequency() when you have:

• Categorical variables (gender, region, education level)

• Yes/No questions

• Rating scales (satisfied/neutral/dissatisfied)

• Any question with a fixed set of options

Weights Make a Difference

Without weights, you're describing your sample. With weights, you're estimating population values. Always use weights for population inference.

Tagged Missing Values

When data is imported with tagged NAs (e.g., via read_spss() with tag_na = TRUE, or read_stata(), read_sas(), read_xpt() with the tag_na parameter), frequency() automatically expands the missing value section to show each missing type individually (with its original missing value code and label), plus summary rows for Total Valid and Total Missing.

Value

A frequency table showing counts and percentages for each category

See Also

table for base R frequency tables.

crosstab for cross-tabulation of two variables.

chi_square for testing relationships between categories.

describe for numeric variable summaries.

Other descriptive: crosstab(), describe(), multiple_response(), normality_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Basic categorical analysis
survey_data %>% frequency(gender)

# Multiple variables with weights
survey_data %>% frequency(gender, region, weights = sampling_weight)

# Grouped analysis by region
survey_data %>% 
  group_by(region) %>% 
  frequency(gender, weights = sampling_weight)

# Education levels with sorting
survey_data %>% frequency(education, sort_frq = "desc")

# Employment status with custom display options
survey_data %>% frequency(employment, weights = sampling_weight, 
                         show_na = TRUE, show_sum = TRUE)

Compare Three or More Related Measurements Without Assuming Normality

Description

friedman_test() compares three or more related measurements from the same subjects when your data isn't normally distributed. It's the non-parametric alternative to repeated-measures ANOVA.

Think of it as:

• Comparing ratings of multiple items by the same respondents

• Testing whether scores change across three or more time points

• A robust repeated-measures comparison that works with any data shape

The test tells you:

• Whether at least one measurement is significantly different from the others

• Which measurements tend to be rated higher or lower (via mean ranks)

• The strength of the overall effect (Kendall's W)

Usage

friedman_test(data, ..., weights = NULL, conf.level = 0.95)

Arguments

Details

Understanding the Results

P-value: If p < 0.05, at least one measurement is significantly different

• p < 0.001: Very strong evidence of differences

• p < 0.01: Strong evidence of differences

• p < 0.05: Moderate evidence of differences

• p > 0.05: No significant differences found

Kendall's W (Effect size: How consistent is the pattern?):

• < 0.1: Negligible agreement/effect

• 0.1 - 0.3: Weak agreement

• 0.3 - 0.5: Moderate agreement

• 0.5 or higher: Strong agreement

Mean Ranks:

• Higher mean rank = measurement tends to have higher values

• Lower mean rank = measurement tends to have lower values

• Compare mean ranks to see which measurements stand out

When to Use This

Use the Friedman test when:

• You have 3 or more related measurements from the same subjects

• Your data is not normally distributed

• You have ordinal data (ratings, rankings)

• You want a robust alternative to repeated-measures ANOVA

• You're comparing multiple ratings by the same respondents

Relationship to Other Tests

• For 2 related measurements: Use wilcoxon_test() instead

• For normally distributed repeated measures: Use repeated-measures ANOVA

Weighted variants

SPSS NPAR TESTS ignores WEIGHT BY, so weighted results have no SPSS reference. The weighted variant is an R-only frequency-weight extension that reduces exactly to the unweighted test when all weights equal 1 (enforced by an internal invariance suite); see vignette("spss-compatibility") for validation status.

• For independent groups: Use kruskal_wallis() instead

Value

Test results showing whether the measurements differ, including:

• Chi-Square statistic (chi_squared, the Friedman test statistic)

• Degrees of freedom (number of measurements minus 1)

• P-value (are measurements different?)

• Kendall's W (effect size: how strong is the overall pattern?)

• Mean rank for each measurement (which measurements are higher/lower?)

References

Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200), 675-701.

Kendall, M. G., & Babington Smith, B. (1939). The problem of m rankings. The Annals of Mathematical Statistics, 10(3), 275-287.

See Also

friedman.test for the base R Friedman test.

wilcoxon_test for comparing two related measurements.

kruskal_wallis for comparing independent groups.

Other hypothesis_tests: ancova(), binomial_test(), chi_square(), chisq_gof(), factorial_anova(), fisher_test(), kruskal_wallis(), mann_whitney(), mcnemar_test(), oneway_anova(), t_test(), wilcoxon_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Compare three trust items (rated by same respondents)
survey_data %>%
  friedman_test(trust_government, trust_media, trust_science)

# Using tidyselect helpers
survey_data %>%
  friedman_test(starts_with("trust_"))

# Weighted analysis
survey_data %>%
  friedman_test(trust_government, trust_media, trust_science,
                weights = sampling_weight)

# Grouped analysis (separate test per region)
survey_data %>%
  group_by(region) %>%
  friedman_test(trust_government, trust_media, trust_science)

Kendall's Tau Correlation Analysis

Description

Calculates Kendall's tau-b rank correlation coefficients between variables with support for weighted correlations, grouped data, and multiple variable pairs. Provides significance testing and SPSS-compatible output formatting.

The function computes tau-b, which is adjusted for ties and is particularly suitable for ordinal data or when the assumptions of Pearson correlation are not met. For weighted correlations, it uses survey-weighted rank calculations.

Usage

kendall_tau(
  data,
  ...,
  weights = NULL,
  alternative = c("two.sided", "less", "greater"),
  use = c("pairwise", "listwise"),
  na.rm = NULL
)

Arguments

Details

Understanding the Results

Kendall's tau measures how often pairs of observations are in the same order (concordant) versus different order (discordant). It is particularly useful for ordinal data, data with outliers, small sample sizes, and non-linear but monotonic relationships.

The tau value ranges from -1 to +1:

• Strong positive (0.5 to 1.0): High values of one variable tend to go with high values of the other

• Moderate positive (0.3 to 0.5): Some tendency for values to increase together

• Weak positive (0 to 0.3): Slight tendency for values to increase together

• No correlation (near 0): No relationship between the variables

• Negative values: As one variable increases, the other tends to decrease

The output also provides:

• p-value: Probability of seeing this correlation by chance (smaller = stronger evidence)

• n: Number of observations used

• significance stars: Quick visual indicator of statistical significance

When to Use This

Choose Kendall's tau when:

• Your data is ordinal (ranked categories)

• You have a small sample size (< 30 observations)

• Your data has outliers that might affect Pearson correlation

Weighted variants

The weighted tau-b is an exact frequency-weighted computation (it reproduces the unweighted tau when all weights equal 1, enforced by an internal invariance suite), but its z statistic and p-value use a no-ties normal approximation. SPSS NONPAR CORR ignores WEIGHT BY, so weighted results have no SPSS reference; see vignette("spss-compatibility") for validation status.

• You want a more conservative measure than Spearman's rho

Value

Correlation results showing rank-based relationships between variables, including the tau-b coefficient, p-value, z-score, and sample size for each pair. For multiple variables, correlation, significance, and sample size matrices are also provided. Use summary() for the full SPSS-style output with toggleable sections.

References

Kendall, M. G. (1938). A new measure of rank correlation. Biometrika, 30(1/2), 81–93.

Kendall, M. G. (1945). The treatment of ties in ranking problems. Biometrika, 33(3), 239–251.

Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). John Wiley & Sons.

See Also

cor with method = "kendall" for the base R implementation.

spearman_rho for Spearman's rank correlation.

pearson_cor for Pearson correlation analysis.

summary.kendall_tau for detailed output with toggleable sections.

Other correlation: partial_cor(), pearson_cor(), spearman_rho()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Kendall's tau is O(n^2); the examples run on a subset for speed.
# On your own data, simply drop the subsetting.
mini_survey <- survey_data[1:300, ]

# Basic correlation between two variables
mini_survey %>%
  kendall_tau(life_satisfaction, political_orientation)

# Correlation matrix for multiple variables
mini_survey %>%
  kendall_tau(life_satisfaction, political_orientation, trust_media)

# Weighted correlations
mini_survey %>%
  kendall_tau(age, income, weights = sampling_weight)

# Listwise deletion for missing data
mini_survey %>%
  kendall_tau(age, income, use = "listwise")

# One-tailed test
mini_survey %>%
  kendall_tau(age, income, alternative = "greater")


# Grouped correlations
mini_survey %>%
  group_by(region) %>%
  kendall_tau(age, income, life_satisfaction)

# Using tidyselect helpers for ordinal variables
mini_survey %>%
  kendall_tau(starts_with("trust"), weights = sampling_weight)

# --- Three-layer output ---
result <- mini_survey %>%
  kendall_tau(life_satisfaction, political_orientation, trust_media,
              weights = sampling_weight)
result              # compact one-line overview
summary(result)     # full correlation, p-value, and N matrices
summary(result, pvalue_matrix = FALSE)  # hide p-values

Compare Multiple Groups Without Assuming Normal Data

Description

kruskal_wallis() compares three or more groups when your data isn't normally distributed or when you have ordinal data (like ratings or rankings). It's the non-parametric alternative to one-way ANOVA.

Think of it as:

• An extension of the Mann-Whitney test for more than two groups

• A robust way to compare groups that works with any data shape

• Perfect for Likert scales, rankings, or skewed distributions

The test tells you:

• Whether at least one group is different from the others

• How strong the overall group effect is (effect size)

• Which groups tend to have higher or lower values (via mean ranks)

Usage

kruskal_wallis(data, ..., group, weights = NULL, conf.level = 0.95)

Arguments

Details

Understanding the Results

P-value: If p < 0.05, at least one group is significantly different

• p < 0.001: Very strong evidence of group differences

• p < 0.01: Strong evidence of group differences

• p < 0.05: Moderate evidence of group differences

• p > 0.05: No significant group differences found

Effect Size Epsilon-squared (How much do groups matter?):

• < 0.01: Negligible effect

• 0.01-0.06: Small effect

• 0.06-0.14: Medium effect

• 0.14 or higher: Large effect

Mean Ranks:

• Higher mean rank = group tends to have higher values

• Lower mean rank = group tends to have lower values

• Compare mean ranks to see the pattern of group differences

When to Use This

Use Kruskal-Wallis test when:

• Your data is not normally distributed (skewed, outliers)

• You have ordinal data (rankings, Likert scales)

• Sample sizes are small or very unequal across groups

• You want a robust alternative to one-way ANOVA

• You're comparing satisfaction ratings, income, or other skewed variables

What Comes Next?

If the Kruskal-Wallis test is significant:

1. Look at mean ranks to see the pattern

2. Use pairwise Mann-Whitney tests with Bonferroni correction to find which specific groups differ

3. Consider effect sizes to judge practical importance

Relationship to Other Tests

• For 2 groups: Use mann_whitney() instead

• For normally distributed data: Use oneway_anova() instead

Weighted variants

SPSS NPAR TESTS ignores WEIGHT BY, so weighted results have no SPSS reference. The weighted variant is an R-only frequency-weight extension that reduces exactly to the unweighted test when all weights equal 1 (enforced by an internal invariance suite); see vignette("spss-compatibility") for validation status.

• For repeated measures (same subjects): Use friedman_test() instead

Value

Test results showing whether groups differ, including:

• H statistic (Kruskal-Wallis chi-square test statistic)

• Degrees of freedom (number of groups minus 1)

• P-value (are groups different?)

• Effect size epsilon-squared (how big is the group effect?)

• Mean rank for each group (which groups are higher/lower?)

References

Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583-621.

Tomczak, M., & Tomczak, E. (2014). The need to report effect size estimates revisited. An overview of some recommended measures of effect size. Trends in Sport Sciences, 1(21), 19-25.

See Also

kruskal.test for the base R Kruskal-Wallis test.

mann_whitney for comparing exactly two groups.

oneway_anova for parametric one-way ANOVA.

Other hypothesis_tests: ancova(), binomial_test(), chi_square(), chisq_gof(), factorial_anova(), fisher_test(), friedman_test(), mann_whitney(), mcnemar_test(), oneway_anova(), t_test(), wilcoxon_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Basic Kruskal-Wallis test (comparing across education levels)
survey_data %>%
  kruskal_wallis(life_satisfaction, group = education)

# Multiple variables
survey_data %>%
  kruskal_wallis(life_satisfaction, income, trust_government,
                 group = education)

# Using tidyselect helpers
survey_data %>%
  kruskal_wallis(starts_with("trust_"), group = education)

# Weighted analysis
survey_data %>%
  kruskal_wallis(life_satisfaction, group = education,
                 weights = sampling_weight)

# Grouped analysis (separate test for each region)
survey_data %>%
  group_by(region) %>%
  kruskal_wallis(life_satisfaction, group = education)

# Compare across employment status (5 groups)
survey_data %>%
  kruskal_wallis(income, group = employment)

Test If Groups Vary Similarly

Description

levene_test() checks if different groups have similar amounts of variation. This is an important assumption for many statistical tests - groups should spread out in similar ways.

The test tells you:

• Whether variance is consistent across groups

• If you can trust standard ANOVA and t-test results

• When to use alternative tests that don't assume equal variance

Usage

levene_test(x, ...)

## Default S3 method:
levene_test(x, ...)

## S3 method for class 'data.frame'
levene_test(x, ..., group, weights = NULL, center = c("mean", "median"))

## S3 method for class 'oneway_anova'
levene_test(x, center = c("mean", "median"), ...)

## S3 method for class 't_test'
levene_test(x, center = c("mean", "median"), ...)

## S3 method for class 'mann_whitney'
levene_test(x, ...)

## S3 method for class 'grouped_df'
levene_test(x, variable, group = NULL, weights = NULL, center = "mean", ...)

Arguments

Details

Understanding the Results

P-value interpretation:

• p > 0.05: Good! Groups have similar variance (assumption met)

• p <= 0.05: Problem - groups vary differently (assumption violated)

Think of it like checking if all groups are equally "spread out":

• Similar spread = can use standard tests

• Different spread = need special methods

When to Use This

Check variance equality when:

• Before running t-tests or ANOVA

• Comparing groups with different sizes

• Your statistical test assumes equal variances

• You see very different standard deviations

What If Variances Are Unequal?

If Levene's test is significant (p <= 0.05):

• For t-tests: Use Welch's t-test (var.equal = FALSE)

• For ANOVA: Use Welch's ANOVA

• Consider transforming your data

• Use non-parametric alternatives

• Report that equal variance assumption was violated

Usage Flexibility

You can use this function two ways:

• Standalone: Check any variables for equal variance

• After tests: Pipe after t_test() or oneway_anova() to verify assumptions

Tips for Success

• Always check this assumption for group comparisons

• Visual inspection (boxplots) can supplement the test

• Large samples make the test very sensitive

• Use median-based test for skewed data (center = "median")

• Don't panic if violated - alternatives exist!

Value

Test results showing:

• Whether groups have equal variances (p-value)

• F-statistic measuring variance differences

• Which variables meet the assumption

References

Levene, H. (1960). Robust tests for equality of variances. In I. Olkin (Ed.), Contributions to Probability and Statistics (pp. 278–292). Stanford University Press.

Brown, M. B., & Forsythe, A. B. (1974). Robust tests for the equality of variances. Journal of the American Statistical Association, 69(346), 364–367.

IBM Corp. (2023). IBM SPSS Statistics 29 Algorithms. IBM Corporation.

See Also

oneway_anova for one-way ANOVA (which assumes equal variances).

t_test for group mean comparisons.

var.test for the base R F-test of variance equality.

Other posthoc: dunn_test(), pairwise_wilcoxon(), scheffe_test(), tukey_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Standalone Levene test (test homogeneity of variances)
survey_data %>% levene_test(life_satisfaction, group = region)

# Multiple variables
survey_data %>% levene_test(life_satisfaction, trust_government, group = region)

# Weighted analysis
survey_data %>% levene_test(income, group = education, weights = sampling_weight)

# Piped after ANOVA (common workflow)
result <- survey_data %>%
  oneway_anova(life_satisfaction, group = education)
result %>% levene_test()

# Piped after t-test
survey_data %>%
  t_test(age, group = gender) %>%
  levene_test()

# Using mean instead of median as center
survey_data %>% levene_test(income, group = region, center = "mean")

Run a Linear Regression

Description

linear_regression() performs bivariate or multiple linear regression with SPSS-compatible output. Wraps stats::lm() and adds standardized coefficients (Beta), a formatted ANOVA table, and a model summary matching SPSS REGRESSION output.

Supports two interface styles:

• Formula interface: linear_regression(data, life_satisfaction ~ age + education)

• SPSS-style: linear_regression(data, dependent = life_satisfaction, predictors = c(age, education))

Usage

linear_regression(
  data,
  formula = NULL,
  dependent = NULL,
  predictors = NULL,
  weights = NULL,
  use = c("listwise", "pairwise"),
  standardized = TRUE,
  conf.level = 0.95,
  factors = c("dummy", "numeric")
)

Arguments

Details

Understanding the Results

The output includes four sections matching SPSS REGRESSION output:

• Model Summary: R, R-squared, Adjusted R-squared, and Standard Error of the Estimate. R-squared tells you how much variance in the dependent variable is explained by the predictors.

• ANOVA: Tests whether the overall model is significant. A significant F-test means at least one predictor matters.

• Coefficients: B (unstandardized), Beta (standardized), t-value, p-value, and confidence intervals for each predictor.

• Descriptives: Mean, SD, and N for all variables in the model.

Interpreting coefficients:

• B (unstandardized): For each 1-unit increase in the predictor, the dependent variable changes by B units

• Beta (standardized): Allows comparison across predictors with different scales. Larger absolute Beta = stronger effect

• p-value: Values below 0.05 indicate statistically significant predictors

When to Use This

Use linear_regression() when:

• Your dependent variable is continuous (e.g., income, satisfaction score)

• You want to predict an outcome from one or more predictors

• You need standardized coefficients to compare predictor importance

For binary outcomes (yes/no, 0/1), use logistic_regression instead.

Technical Details

Missing Data: By default, listwise deletion is used (matching SPSS REGRESSION /MISSING LISTWISE). Set use = "pairwise" to match SPSS /MISSING PAIRWISE, which computes the regression from a pairwise covariance matrix. Pairwise deletion retains more cases and produces results closer to SPSS output when data has varying patterns of missingness.

Weights: When weights are specified, they are treated as frequency weights (matching SPSS WEIGHT BY behavior). The model is fitted using weighted least squares via lm(weights = ...).

Standardized Coefficients: Beta = B * (SD_x / SD_y). This matches the SPSS standardized coefficient output. Not available for the intercept. For dummy-coded factor terms (factors = "dummy"), the SD of the contrast column from the design matrix is used.

Factor Predictors: By default (factors = "dummy"), factor predictors are expanded into L - 1 contrasts via R's stats::model.matrix(), matching base R lm(): unordered factors get treatment (dummy) contrasts against the first level, while ordered factors get R's default polynomial contrasts (.L/.Q/.C terms). Pass factors = "numeric" to silently coerce factor levels to their integer codes (SPSS REGRESSION default). The "numeric" mode is required to reproduce SPSS results for ordinal predictors like education or Likert scales that SPSS treats as continuous.

Grouped Analysis: When data is grouped via dplyr::group_by(), a separate regression is run for each group (matching SPSS SPLIT FILE BY).

Value

For ungrouped + listwise data, an object of class c("linear_regression", "lm") — the fitted lm itself, with mariposa-specific slots attached:

coef_table

SPSS-style tibble with B, Std.Error, Beta, t, p, CI_lower, CI_upper. For weighted models, SE / t / p are adjusted to SPSS's frequency-weight df (see Technical Details).

anova_table

SPSS-style overall-model ANOVA tibble (Source × Sum_of_Squares / df / Mean_Square / F_statistic / Sig).

model_summary

List with R, R_squared, adj_R_squared, std_error.

descriptives

Tibble with Mean, Std.Deviation, N for all variables.

n

Sample size (listwise complete cases; weighted N when weighted).

formula, dependent, predictor_names, weighted, weight_name, use, is_grouped, standardized, conf.level

Call metadata.

Because the object inherits from "lm", all standard generics (predict(), anova(), vcov(), confint(), residuals(), fitted(), coef(), model.matrix(), broom::tidy(), broom::glance(), broom::augment()) dispatch natively without unwrapping. summary() returns the SPSS-style mariposa summary; for the raw lm summary use stats::summary.lm() on the same object.

For use = "pairwise" (no single fitted lm available) or for grouped data, returns a list of class "linear_regression". Pairwise results expose the same SPSS-style tables but not the lm generics; grouped results hold one fitted lm-inheriting model per group under $groups.

See Also

logistic_regression for binary outcome variables.

describe for checking variable distributions before regression.

pearson_cor for checking bivariate correlations.

summary.linear_regression for detailed output with toggleable sections.

Other regression: logistic_regression(), marginal_effects()

Examples

library(dplyr)
data(survey_data)

# Bivariate regression
linear_regression(survey_data, life_satisfaction ~ age)

# Multiple regression
linear_regression(survey_data, income ~ age + education + life_satisfaction)

# SPSS-style interface
linear_regression(survey_data,
                  dependent = life_satisfaction,
                  predictors = c(trust_government, trust_media, trust_science))

# Weighted regression
linear_regression(survey_data, life_satisfaction ~ age, weights = sampling_weight)

# Grouped by region
survey_data |>
  dplyr::group_by(region) |>
  linear_regression(life_satisfaction ~ age)

# Factor predictors: dummy-coding (default, matches base R lm())
linear_regression(survey_data, income ~ age + education)

# Factor predictors: SPSS-style ordinal-as-scale
linear_regression(survey_data, income ~ age + education,
                  factors = "numeric")

# --- Three-layer output ---
result <- linear_regression(survey_data, life_satisfaction ~ age + income)
result                                  # compact one-line overview
summary(result)                         # full detailed SPSS-style output
summary(result, descriptives = FALSE)   # hide descriptives section

Run a Logistic Regression

Description

logistic_regression() performs binary logistic regression with SPSS-compatible output. Wraps stats::glm(family = binomial) and adds odds ratios, pseudo R-squared measures, classification table, and model tests matching SPSS LOGISTIC REGRESSION output.

Supports two interface styles:

• Formula interface: logistic_regression(data, high_satisfaction ~ age + income)

• SPSS-style: logistic_regression(data, dependent = high_satisfaction, predictors = c(age, income))

Usage

logistic_regression(
  data,
  formula = NULL,
  dependent = NULL,
  predictors = NULL,
  weights = NULL,
  conf.level = 0.95,
  factors = c("dummy", "numeric")
)

Arguments

Details

Understanding the Results

The output includes five sections matching SPSS LOGISTIC REGRESSION output:

• Omnibus Test: Tests whether the model as a whole is significant. A significant chi-square means the model predicts better than chance.

• Model Summary: -2 Log Likelihood and pseudo R-squared values. Lower -2LL = better fit. Higher R-squared = more variance explained.

• Hosmer-Lemeshow Test: Goodness-of-fit test. A non-significant result (p > 0.05) means the model fits the data well.

• Classification Table: How well the model classifies cases. Shows percentage correctly predicted for each group and overall.

• Coefficients: B, Wald test, odds ratios (Exp(B)), and CIs.

Interpreting odds ratios (Exp(B)):

• Exp(B) > 1: Predictor increases the odds of the outcome

• Exp(B) < 1: Predictor decreases the odds of the outcome

• Exp(B) = 1: Predictor has no effect on the odds

When to Use This

Use logistic_regression() when:

• Your dependent variable is binary (yes/no, 0/1, pass/fail)

• You want to predict group membership from one or more predictors

• You need odds ratios to interpret predictor effects

For continuous outcomes, use linear_regression instead.

Technical Details

Dependent Variable: Must be binary. Factors with exactly 2 levels are automatically converted to 0/1 (first level = 0, second level = 1). Numeric variables must contain only 0 and 1 values.

Missing Data: Listwise deletion is used (matching SPSS LOGISTIC REGRESSION default behavior).

Weights: When weights are specified, they are treated as frequency weights (matching SPSS WEIGHT BY behavior).

Pseudo R-squared: Three measures are reported:

• Cox & Snell R-squared (bounded below 1)

• Nagelkerke R-squared (adjusted to reach 1)

• McFadden R-squared (1 - LL_model/LL_null)

Factor Predictors: By default (factors = "dummy"), factor predictors are expanded into L - 1 contrasts via R's stats::model.matrix(), matching base R glm(): unordered factors get treatment (dummy) contrasts against the first level, while ordered factors get R's default polynomial contrasts (.L/.Q/.C terms). Pass factors = "numeric" to silently coerce factor levels to their integer codes (SPSS LOGISTIC REGRESSION default without an explicit /CATEGORICAL subcommand).

Grouped Analysis: When data is grouped via dplyr::group_by(), a separate regression is run for each group (matching SPSS SPLIT FILE BY).

Value

For ungrouped data, an object of class c("logistic_regression", "glm", "lm") — the fitted glm itself, with mariposa-specific slots attached:

coef_table

Tibble with B, S.E., Wald, df, Sig., Exp(B), CI_lower, CI_upper

model_summary

List with minus2LL, cox_snell_r2, nagelkerke_r2, mcfadden_r2

omnibus_test

List with chi_sq, df, p for overall model test

classification

List with table, overall_pct, pct_correct_0, pct_correct_1

hosmer_lemeshow

List with chi_sq, df, p (goodness-of-fit test)

n

Sample size (listwise complete cases; weighted N when weighted)

formula, dependent, predictor_names, weighted, weight_name, is_grouped, conf.level

Call metadata.

Because the object inherits from "glm", all standard generics (predict(), anova(), vcov(), confint(), residuals(), fitted(), coef(), broom::tidy(), broom::glance(), broom::augment()) dispatch natively without unwrapping. summary() returns the SPSS-style mariposa summary; for the raw glm summary use stats::summary.glm() on the same object.

For grouped data, returns a list of class "logistic_regression" with $groups holding one fitted glm-inheriting result per group.

See Also

linear_regression for continuous outcome variables.

chi_square for testing associations between categorical variables.

summary.logistic_regression for detailed output with toggleable sections.

Other regression: linear_regression(), marginal_effects()

Examples

library(dplyr)
data(survey_data)

# Create binary DV
survey_data$high_satisfaction <- ifelse(survey_data$life_satisfaction >= 4, 1, 0)

# Bivariate logistic regression
logistic_regression(survey_data, high_satisfaction ~ age)

# Multiple logistic regression
logistic_regression(survey_data, high_satisfaction ~ age + income + education)

# SPSS-style interface
logistic_regression(survey_data,
                    dependent = high_satisfaction,
                    predictors = c(age, income))

# Weighted logistic regression
logistic_regression(survey_data, high_satisfaction ~ age,
                    weights = sampling_weight)

# Grouped by region
survey_data |>
  dplyr::group_by(region) |>
  logistic_regression(high_satisfaction ~ age)

# Factor predictors: dummy-coding (default, matches base R glm())
logistic_regression(survey_data, high_satisfaction ~ age + education)

# Factor predictors: SPSS-style ordinal-as-scale
logistic_regression(survey_data, high_satisfaction ~ age + education,
                    factors = "numeric")

# --- Three-layer output ---
result <- logistic_regression(survey_data, high_satisfaction ~ age + income)
result                                    # compact one-line overview
summary(result)                           # full detailed SPSS-style output
summary(result, classification = FALSE)   # hide classification table

Longitudinal Study Data (Synthetic)

Description

A synthetic repeated measures dataset suitable for testing longitudinal analysis functions. Contains realistic treatment effects, missing data patterns, and within-subject correlations.

Usage

longitudinal_data

Format

A data frame with 480 rows and 10 variables:

subject_id

Subject identifier (factor, 1-120)

group

Treatment group (Control, Treatment)

age

Age in years (numeric)

gender

Gender (Male, Female)

time

Time point (T1, T2, T3, T4)

time_numeric

Time point as numeric (1-4)

outcome_score

Primary outcome measure (continuous)

secondary_outcome

Secondary outcome measure (continuous)

physio_measure

Physiological measure (continuous)

questionnaire_score

Questionnaire rating (ordered factor, 1-7)

Details

Study design features:

• 120 subjects (60 per group)

• 4 measurement occasions

• Balanced treatment assignment

• Realistic dropout patterns (0% at T1, 35% by T4)

• Treatment effect grows over time (interaction effect)

• Autoregressive error structure

• Effect size approximately Cohen's d = 0.6

Missing data patterns reflect realistic longitudinal study attrition with higher dropout in later time points. The treatment effect demonstrates a clear Group x Time interaction suitable for repeated measures testing.

Source

Generated synthetically with realistic longitudinal correlation patterns and treatment effects. No real study data was used.

See Also

longitudinal_data_wide for the wide format version.

survey_data for the cross-sectional survey dataset.

Examples

# Load required packages and data
library(dplyr)
data(longitudinal_data)

# Simple example analysis with wide format data
# data(longitudinal_data_wide)
# (Repeated measures functions coming in future version)

# Descriptive statistics by group and time
longitudinal_data %>%
  group_by(group, time) %>%
  describe(outcome_score)

Longitudinal Study Data - Wide Format (Synthetic)

Description

Wide format version of the longitudinal_data dataset with one row per subject and separate columns for each time point. Useful for certain repeated measures analyses and data visualization.

Usage

longitudinal_data_wide

Format

A data frame with 120 rows and 8 variables:

subject_id

Subject identifier (factor, 1-120)

group

Treatment group (Control, Treatment)

age

Age in years (numeric)

gender

Gender (Male, Female)

score_T1

Outcome score at Time 1

score_T2

Outcome score at Time 2

score_T3

Outcome score at Time 3

score_T4

Outcome score at Time 4

Details

This is the wide format version of longitudinal_data with outcome scores spread across columns by time point. Missing values represent subject dropout or missed assessments.

Source

Generated synthetically from the long format longitudinal_data.

See Also

longitudinal_data for the long format version

Examples

# Load required packages and data
library(dplyr)
data(longitudinal_data_wide)

# Analysis of change scores
# (Repeated measures functions coming in future version)

Compare Two Groups Without Assuming Normal Data

Description

mann_whitney() compares two groups when your data isn't normally distributed or when you have ordinal data (like ratings or rankings). It's the go-to alternative when t-tests aren't appropriate.

Think of it as:

• A robust way to compare groups that works with any data shape

• Perfect for Likert scales, ratings, or skewed distributions

• A test that compares the typical values between groups

The test tells you:

• Whether one group tends to have higher values than the other

• How strong the difference is (effect size)

• Which group has higher average ranks

Usage

mann_whitney(
  data,
  ...,
  group,
  weights = NULL,
  mu = 0,
  alternative = c("two.sided", "less", "greater"),
  conf.level = 0.95
)

Arguments

Details

Understanding the Results

P-value: If p < 0.05, the groups are significantly different

• p < 0.001: Very strong evidence of difference

• p < 0.01: Strong evidence of difference

• p < 0.05: Moderate evidence of difference

• p >= 0.05: No significant difference found

Effect Size r (How big is the difference?):

• |r| < 0.1: Negligible difference

• |r| ~ 0.1: Small difference

• |r| ~ 0.3: Medium difference

• |r| ~ 0.5: Large difference

• |r| > 0.5: Very large difference

Rank Mean Difference:

• Positive: Group 1 tends to have higher values

• Negative: Group 2 tends to have higher values

• Zero: Groups have similar distributions

When to Use This

Use Mann-Whitney test when:

• Your data is not normally distributed (skewed, outliers)

• You have ordinal data (rankings, Likert scales)

• Sample sizes are small (< 30 per group)

• You want a robust alternative to the t-test

• You're comparing satisfaction ratings, income, or other skewed variables

Advantages Over t-test

• Works with any data distribution

• Not affected by outliers

• Valid for ordinal data

• No assumptions about variance

• More robust for real-world data

Tips for Success

• Each group should have at least 5 observations

• The test compares distributions, not just means

• Look at both p-values and effect sizes

• Consider plotting the data to see the pattern

Weighted variants

The weighted test is the design-based Lumley-Scott (2013) rank test (Horvitz-Thompson midranks with a sandwich variance), validated against survey::svyranktest() rather than SPSS: SPSS NPAR TESTS ignores WEIGHT BY, so no SPSS reference exists for weighted results. The weighted descriptive U and W are design-based rank quantities and may differ from SPSS's expanded-data U; the Z statistic and p-value are the validated quantities.

• Use this for Likert scales and rating data

Value

Test results showing whether groups differ, including:

• U and W statistics (test statistics)

• Z-score and p-value (are groups different?)

• Effect size r (r_effect, how big is the difference?)

• Rank means for each group (which group is higher?) Use summary() for the full SPSS-style output with toggleable sections.

References

Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 18(1), 50-60.

Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80-83.

Lumley, T., & Scott, A. (2013). Two-sample rank tests under complex sampling. Biometrika, 100(4), 831-842.

See Also

wilcox.test for the base R Wilcoxon test function.

svyranktest for survey-weighted rank tests.

t_test for parametric t-tests.

summary.mann_whitney for detailed output with toggleable sections.

Other hypothesis_tests: ancova(), binomial_test(), chi_square(), chisq_gof(), factorial_anova(), fisher_test(), friedman_test(), kruskal_wallis(), mcnemar_test(), oneway_anova(), t_test(), wilcoxon_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Basic Mann-Whitney test (non-parametric comparison)
survey_data %>%
  mann_whitney(age, group = gender)

# Multiple variables
survey_data %>%
  mann_whitney(age, income, life_satisfaction, group = region)

# Using tidyselect helpers
survey_data %>%
  mann_whitney(starts_with("trust_"), group = gender)

# Weighted analysis
survey_data %>%
  mann_whitney(income, group = region, weights = sampling_weight)

# Grouped analysis (separate tests for each education level)
survey_data %>%
  group_by(education) %>%
  mann_whitney(life_satisfaction, group = gender)

# One-sided test
survey_data %>%
  mann_whitney(life_satisfaction, group = region, alternative = "greater")

# --- Three-layer output ---
result <- survey_data %>%
  mann_whitney(income, group = gender, weights = sampling_weight)
result              # compact one-line overview
summary(result)     # full detailed output with all sections
summary(result, effect_sizes = FALSE)  # hide effect sizes

Average Marginal Effects

Description

marginal_effects() computes average marginal effects (AMEs) for a fitted logistic_regression model — the Stata margins, dydx(*) workhorse. AMEs translate logit coefficients into the language reports actually use: percentage-point changes in the predicted probability.

For each continuous predictor, the AME is the derivative of the predicted probability with respect to that predictor, averaged over the observed sample. For each factor level, it is the average discrete change in predicted probability compared to the reference level.

Usage

marginal_effects(model, conf.level = 0.95, ...)

Arguments

Details

Understanding the Output

An AME of 0.03 for age means: averaged over the sample, one additional year of age increases the predicted probability of the outcome by 3 percentage points. Unlike odds ratios, AMEs are directly comparable across models and across groups, which is why they are the preferred reporting style in much of the social sciences.

When to Use This

• To report logistic regression results on the probability scale instead of (or alongside) odds ratios

• To compare predictor effects across groups (run on a group_by()-fitted model)

Technical Details

Continuous predictors use a centered numerical derivative of the predicted probability; factor predictors use the average discrete change against the reference level. Standard errors come from the delta method with the analytic gradient of the AME with respect to the coefficients and the model's Wald covariance matrix, matching the default in Stata's margins and R's margins package. Weighted models average with their frequency weights (unrounded, Charter §5.1); at weights == 1 the weighted AME reduces exactly to the unweighted one.

Linear regression: AMEs are not needed there — for a linear model without interactions or transformations, the unstandardized coefficient B is the marginal effect, already shown in the linear_regression coefficients table. Calling marginal_effects() on a linear_regression result says so instead of duplicating the table.

Validation: SPSS has no AME procedure, so this function is Tier 4 (Internal) under the Validation Charter — verified against the analytic logit-AME formula and an independent finite-difference delta-method recomputation, not against SPSS output.

Value

An object of class "marginal_effects" whose $results tibble holds one row per predictor term (and group combination) with:

Term

Predictor name; factor rows read "var: level vs. ref"

Type

"dydx" (continuous derivative) or "discrete" (factor-level contrast)

AME

Average marginal effect on the probability scale

SE

Delta-method standard error

z, p_value

Wald test of the AME

CI_lower, CI_upper

Confidence interval at conf.level

See Also

logistic_regression for fitting the model.

summary.marginal_effects for detailed output.

Other regression: linear_regression(), logistic_regression()

Examples

data(survey_data)
survey_data$high_satisfaction <- as.integer(survey_data$life_satisfaction >= 4)

model <- logistic_regression(survey_data,
                             high_satisfaction ~ age + income + education)
marginal_effects(model)

# Weighted model
model_w <- logistic_regression(survey_data, high_satisfaction ~ age + income,
                               weights = sampling_weight)
marginal_effects(model_w)

# --- Three-layer output ---
ame <- marginal_effects(model)
ame                 # compact overview
summary(ame)        # full detailed output

McNemar's Test for Paired Proportions

Description

mcnemar_test() tests whether paired proportions have changed between two dichotomous measurements. Use this for before/after comparisons of categorical outcomes.

Think of it as:

• A paired comparison test for binary (yes/no) data

• The categorical equivalent of a paired t-test

• Tests whether the proportion of "changers" is symmetric

The test tells you:

• Whether paired proportions changed significantly

• Both asymptotic and exact p-values for maximum reliability

• The number of discordant pairs (who actually changed)

Usage

mcnemar_test(data, var1, var2, weights = NULL, correct = TRUE, ...)

Arguments

Details

Understanding the Results

P-value: If p < 0.05, the paired proportions are significantly different

• p < 0.001: Very strong evidence of change

• p < 0.01: Strong evidence of change

• p < 0.05: Moderate evidence of change

• p >= 0.05: No significant change found

Exact vs Asymptotic: The exact binomial p-value is more reliable for small samples. For large samples, both p-values will be very similar.

Discordant Pairs: Only pairs where the two measurements differ (b and c) contribute to the test. If b approximately equals c, there is no evidence of systematic change.

When to Use This

Use McNemar's test when:

• You have paired or matched binary data

• You're comparing before/after proportions

• Both variables must be dichotomous (exactly 2 levels)

The McNemar Statistic

For a 2x2 table with discordant cells b and c:

\chi^2 = \frac{(|b - c| - 1)^2}{b + c}

(with continuity correction)

The exact test uses a binomial test on the discordant pairs.

Relationship to Other Tests

• For unpaired categorical data: Use chi_square() instead

• For paired ordinal/continuous data: Use wilcoxon_test() instead

• For paired data with more than 2 levels: Consider the Bowker test of symmetry

SPSS Equivalent

SPSS: CROSSTABS /STATISTICS=MCNEMAR

Value

Test results showing whether paired proportions changed, including:

• McNemar chi-square statistic (chi_squared, with continuity correction)

• Asymptotic p-value

• Exact binomial p-value (two-sided)

• 2x2 contingency table

• Discordant pair counts (b and c)

References

McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153-157.

See Also

chi_square for independence tests.

wilcoxon_test for paired non-parametric tests on ordinal data.

Other hypothesis_tests: ancova(), binomial_test(), chi_square(), chisq_gof(), factorial_anova(), fisher_test(), friedman_test(), kruskal_wallis(), mann_whitney(), oneway_anova(), t_test(), wilcoxon_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Create dichotomous variables
test_data <- survey_data %>%
  mutate(
    trust_gov_high = as.integer(trust_government >= 4),
    trust_media_high = as.integer(trust_media >= 4)
  )

# McNemar test
test_data %>%
  mcnemar_test(var1 = trust_gov_high, var2 = trust_media_high)

# Grouped analysis
test_data %>%
  group_by(region) %>%
  mcnemar_test(var1 = trust_gov_high, var2 = trust_media_high)

Analyze Multiple Response Sets

Description

multiple_response() analyzes "check all that apply" survey questions — the SPSS MULT RESPONSE procedure. It takes a set of 0/1 indicator variables (one per answer option) and produces the two tables SPSS users know:

• Without by: the frequencies table — number of mentions per option, percent of responses (sums to 100%), and percent of cases (sums above 100%, because respondents can tick several options).

• With by: the crosstab of the set against a categorical variable — mentions and case-based column percentages per group.

Usage

multiple_response(data, ..., by = NULL, counted = 1, weights = NULL)

Arguments

Details

Understanding the Output

The two percentage columns answer different questions:

• Percent of responses: "Of all boxes ticked, how many were this option?" — describes the mix of answers.

• Percent of cases: "What share of respondents ticked this option?" — usually the number reports need. It sums above 100% whenever respondents tick more than one box.

Case handling

Following SPSS MULT RESPONSE, a case is valid if it has at least one non-missing indicator in the set; cases missing on all indicators are excluded and reported as missing. Within a valid case, missing indicators simply contribute no mention. With by, cases missing on the by variable are excluded too.

Technical Details

Weighted analyses count mentions and cases as unrounded sums of weights (Charter §5.1); displayed Ns are rounded. With weights == 1 the weighted table reduces exactly to the unweighted one. Only dichotomy sets (indicator + counted value) are supported; SPSS's category-range sets are not.

An SPSS v29 MULT RESPONSE reference run is pending; until it lands the counts and percentages are verified against direct hand-computation from the indicator matrix (see the SPSS compatibility vignette).

Value

An object of class "multiple_response" whose $results tibble holds one row per answer option (and group combination) with:

Option

Variable name of the option

Label

The option's variable label (falls back to the name)

n

Number of mentions (weighted sum when weighted)

pct_responses

Share of all mentions — sums to 100%

pct_cases

Share of valid cases mentioning the option — can sum above 100%

With by, $by_results additionally holds the long-form crosstab (columns by_level, Option, Label, n, pct_cases). $n_cases is the number of valid cases (at least one non-missing indicator), $n_responses the total number of mentions.

See Also

frequency for single-variable frequency tables.

crosstab for ordinary two-variable crosstabs.

summary.multiple_response for detailed output.

Other descriptive: crosstab(), describe(), frequency(), normality_test()

Examples

library(dplyr)
data(survey_data)

# Build an example set: which institutions does a respondent
# trust highly (rating of 4 or 5)?
trust <- survey_data %>%
  mutate(
    gov     = as.integer(trust_government >= 4),
    media   = as.integer(trust_media >= 4),
    science = as.integer(trust_science >= 4)
  )

# Frequencies table: % of responses vs. % of cases
multiple_response(trust, gov, media, science)

# Crossed against gender, with weights
multiple_response(trust, gov, media, science,
                  by = gender, weights = sampling_weight)

# --- Three-layer output ---
result <- multiple_response(trust, gov, media, science)
result              # compact overview
summary(result)     # full detailed output

Frequency Table of Missing Value Types

Description

Shows a breakdown of the different types of missing values in a variable that was read with read_spss(), read_stata(), read_sas(), or read_xpt() and contains tagged NAs.

Usage

na_frequencies(x)

Arguments

Value

A data frame with columns:

See Also

read_spss(), read_stata(), read_sas(), read_xpt(), untag_na(), strip_tags()

Other data-import: read_por(), read_sas(), read_spss(), read_stata(), read_xlsx(), read_xpt(), strip_tags(), untag_na()

Examples

if (requireNamespace("haven", quietly = TRUE)) {
  # Declare -9/-8 as distinct tagged missing types, then inspect them
  x <- set_na(c(1, 2, -9, 3, -8, -9), -9, -8)
  na_frequencies(x)
  #   tag n code label
  # 1   a 2   -9  <NA>
  # 2   b 1   -8  <NA>
}

Test Variables for Normality

Description

normality_test() checks whether numeric variables follow a normal distribution, producing the two tests SPSS prints in its EXAMINE "Tests of Normality" table:

• Kolmogorov-Smirnov with Lilliefors significance correction (the "Kolmogorov-Smirnov(a)" column in SPSS)

• Shapiro-Wilk (computed for n between 3 and 5000, matching the SPSS convention)

Use it before a t-test, ANOVA, or Pearson correlation to check the normality assumption, together with levene_test for the equal-variances assumption.

Usage

normality_test(data, ...)

Arguments

Details

Understanding the Output

For both tests the null hypothesis is "the variable is normally distributed":

• p >= 0.05: No significant deviation from normality detected.

• p < 0.05: The variable deviates significantly from a normal distribution.

With large survey samples these tests flag even tiny, practically irrelevant deviations. Combine them with the skewness and kurtosis from describe before deciding against a parametric test.

When to Use This

• Before t_test, oneway_anova, or pearson_cor to check the normality assumption

• Grouped (via group_by()) to check normality within each comparison group — the form of the assumption that actually matters for group comparisons

If normality is clearly violated, consider the rank-based alternatives: mann_whitney, kruskal_wallis, or spearman_rho.

Technical Details

The Lilliefors-corrected p-value uses the Dallal-Wilkinson (1986) approximation, the same correction SPSS applies in EXAMINE ("Lilliefors Significance Correction"). Shapiro-Wilk uses stats::shapiro.test() and is reported for 3 <= n <= 5000, as in SPSS. Cases with missing values are excluded per variable.

Weights: normality_test() deliberately takes no weights argument. Normality checks are a diagnostic of the sample distribution, and neither Shapiro-Wilk nor the Lilliefors correction has a well-defined fractional-frequency-weight form. Run the test on the unweighted sample.

An SPSS v29 EXAMINE reference run is pending; until it lands the statistics are verified against independent R implementations of the same published formulas (see the SPSS compatibility vignette).

Value

An object of class "normality_test" whose $results tibble holds one row per variable (and group combination) with:

n

Number of valid (non-missing) cases

ks_statistic, ks_df, ks_p

Kolmogorov-Smirnov statistic with Lilliefors-corrected p-value (Dallal-Wilkinson approximation); df equals n as in SPSS

shapiro_w, shapiro_p

Shapiro-Wilk W and p-value (NA when n < 3 or n > 5000)

References

Dallal, G. E., & Wilkinson, L. (1986). An analytic approximation to the distribution of Lilliefors's test statistic for normality. The American Statistician, 40(4), 294-296.

See Also

levene_test for the equal-variances assumption.

describe for skewness and kurtosis.

summary.normality_test for detailed output.

Other descriptive: crosstab(), describe(), frequency(), multiple_response()

Examples

library(dplyr)
data(survey_data)

# Test a single variable
normality_test(survey_data, age)

# Several variables at once
normality_test(survey_data, age, income, life_satisfaction)

# Within comparison groups (SPSS: EXAMINE ... BY gender)
survey_data %>%
  group_by(gender) %>%
  normality_test(age, income)

# --- Three-layer output ---
result <- normality_test(survey_data, age, income)
result              # compact overview
summary(result)     # full SPSS-style table

Compare Multiple Groups: Are Their Averages Different?

Description

oneway_anova() helps you determine if average values differ across three or more groups. For example, does average income vary by education level? Or is customer satisfaction different across regions?

Think of it as:

• An extension of t-test for more than two groups

• A way to test if group membership affects outcomes

• A tool to identify meaningful group differences

The test tells you:

• Whether at least one group is different from the others

• How much variation is explained by group membership

• Which variance assumption fits your data best

Usage

oneway_anova(
  data,
  ...,
  group,
  weights = NULL,
  var.equal = TRUE,
  conf.level = 0.95
)

Arguments

Details

Understanding the Results

P-value: If p < 0.05, at least one group average is different

• p < 0.001: Very strong evidence of group differences

• p < 0.01: Strong evidence of group differences

• p < 0.05: Moderate evidence of group differences

• p >= 0.05: No significant group differences found

Effect Sizes (How much do groups matter?):

• Eta-squared: Proportion of variance explained by groups

  • < 0.01: Negligible effect

  • 0.01-0.06: Small effect

  • 0.06-0.14: Medium effect

  • 0.14 or higher: Large effect

• Omega-squared: More conservative estimate (usually preferred)

• Omega-squared and epsilon-squared are truncated at 0; a negative raw estimate (which occurs when F < 1) is reported as 0.

When to Use This

Use ANOVA when:

• You have one numeric outcome (income, satisfaction score, etc.)

• You have one categorical grouping variable with 3+ groups

• You want to know if group averages differ

• Your data is roughly normally distributed within groups

Variance Assumptions

Like SPSS ONEWAY, both results are always shown for comparison:

• Standard ANOVA: valid when group variances are similar

• Welch's ANOVA: robust when group variances differ

• Use levene_test to check which situation applies

What Comes Next?

If ANOVA is significant:

1. Look at group means to see the pattern

2. Use tukey_test() to find which specific groups differ

3. Consider effect sizes to judge practical importance

Tips for Success

• Check that each group has sufficient observations (ideally 20+)

• Look at both p-values and effect sizes

• Use Welch's ANOVA if group sizes are very unequal

• Follow up with post-hoc tests to identify specific differences

• Welch's robust test for equality of means

Value

ANOVA results showing whether groups differ, including:

• F-statistic (F_statistic) and p-value (are groups different?)

• Effect sizes (how much do groups matter?)

• Group statistics (means and standard deviations)

• Both standard and Welch ANOVA results Use summary() for the full SPSS-style output with toggleable sections.

References

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.

Welch, B. L. (1951). On the comparison of several mean values: an alternative approach. Biometrika, 38(3/4), 330-336.

Olejnik, S., & Algina, J. (2003). Generalized eta and omega squared statistics: measures of effect size for some common research designs. Psychological Methods, 8(4), 434-447.

See Also

aov for the base R ANOVA function.

oneway.test for Welch's ANOVA.

tukey_test for post-hoc pairwise comparisons.

levene_test for testing homogeneity of variances.

summary.oneway_anova for detailed output with toggleable sections.

Other hypothesis_tests: ancova(), binomial_test(), chi_square(), chisq_gof(), factorial_anova(), fisher_test(), friedman_test(), kruskal_wallis(), mann_whitney(), mcnemar_test(), t_test(), wilcoxon_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Basic one-way ANOVA (comparing across education levels)
survey_data %>%
  oneway_anova(life_satisfaction, group = education)

# Multiple dependent variables
survey_data %>%
  oneway_anova(life_satisfaction, trust_government, group = education)

# Using tidyselect helpers
survey_data %>%
  oneway_anova(starts_with("trust_"), group = education)

# Weighted analysis
survey_data %>%
  oneway_anova(income, group = education, weights = sampling_weight)

# Grouped analysis (separate ANOVA for each region)
survey_data %>%
  group_by(region) %>%
  oneway_anova(life_satisfaction, group = education)

# Store results for post-hoc analysis
result <- survey_data %>%
  oneway_anova(life_satisfaction, group = education)

# Follow up with post-hoc tests
result %>% tukey_test()
result %>% levene_test()  # Check homogeneity of variances

# --- Three-layer output ---
result              # compact one-line overview
summary(result)     # full detailed output with all sections
summary(result, descriptives = FALSE)  # hide group statistics

Find Which Specific Measurements Differ After Friedman Test

Description

pairwise_wilcoxon() tells you exactly which pairs of measurements differ from each other after the Friedman test finds overall differences. It performs all pairwise Wilcoxon signed-rank tests with p-value correction.

Think of it as:

• Friedman says "there are differences somewhere among the measurements"

• Pairwise Wilcoxon says "specifically, Measurement A differs from Measurement C"

• A way to make all possible pairwise comparisons for repeated measures

Usage

pairwise_wilcoxon(x, ...)

## Default S3 method:
pairwise_wilcoxon(x, ...)

Arguments

Details

Understanding the Results

Z-Statistics: Based on the Wilcoxon signed-rank test for each pair

• Large absolute Z values indicate big differences between two measurements

• Positive Z: Values in var1 tend to be higher than var2

• Negative Z: Values in var2 tend to be higher than var1

Adjusted P-values: Control for multiple comparisons

• p < 0.05: Measurements are significantly different

• p >= 0.05: No significant difference between these measurements

The Wilcoxon Signed-Rank Test

For each pair of measurements, the Wilcoxon signed-rank test:

1. Computes differences between the two measurements

2. Ranks the absolute differences

3. Computes a Z-statistic based on the rank sums

4. Uses normal approximation with tie correction

P-Value Adjustment Methods

• Bonferroni (default): Most conservative, multiplies p by number of comparisons

• Holm: Step-down method, less conservative than Bonferroni

• BH: Controls false discovery rate, good for many comparisons

When to Use This

Use pairwise Wilcoxon when:

• Your Friedman test shows significant differences (p < 0.05)

• You want to know which specific measurements differ

• Your data are ordinal or violate normality assumptions

• You have repeated measures or matched groups

Relationship to Other Tests

• Non-parametric post-hoc for repeated measures (like Dunn is for independent groups)

• Follow-up to friedman_test(), like dunn_test() follows kruskal_wallis()

Weighted variants

When the parent friedman_test() result is weighted, each pairwise test uses the same frequency-weighted signed-rank formulas. SPSS NPAR TESTS ignores WEIGHT BY, so weighted results have no SPSS reference (R-only, guarded by an internal invariance suite); see vignette("spss-compatibility") for validation status.

• Each pairwise comparison uses wilcoxon_test() logic internally

Value

Pairwise comparison results showing:

• Which measurement pairs are significantly different

• Z-statistics from Wilcoxon signed-rank tests

• Adjusted p-values (controlling for multiple comparisons)

References

Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80-83.

See Also

friedman_test for performing Friedman tests.

wilcoxon_test for individual paired Wilcoxon tests.

dunn_test for post-hoc comparisons after Kruskal-Wallis.

Other posthoc: dunn_test(), levene_test(), scheffe_test(), tukey_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Perform Friedman followed by pairwise Wilcoxon post-hoc
friedman_result <- survey_data %>%
  friedman_test(trust_government, trust_media, trust_science)

# Pairwise Wilcoxon comparisons (default: Bonferroni)
friedman_result %>% pairwise_wilcoxon()

# With Holm correction (less conservative)
friedman_result %>% pairwise_wilcoxon(p_adjust = "holm")

# With Benjamini-Hochberg (controls false discovery rate)
friedman_result %>% pairwise_wilcoxon(p_adjust = "BH")

# With weights
fw_weighted <- survey_data %>%
  friedman_test(trust_government, trust_media, trust_science,
                weights = sampling_weight)

fw_weighted %>% pairwise_wilcoxon()

# Grouped analysis
fw_grouped <- survey_data %>%
  group_by(region) %>%
  friedman_test(trust_government, trust_media, trust_science)

fw_grouped %>% pairwise_wilcoxon()

Partial Correlation

Description

partial_cor() computes partial correlations between variables while controlling for one or more other variables — the SPSS PARTIAL CORR procedure (Stata: pcorr). It answers the question "how strongly are x and y related once the influence of the control variables is removed?"

Usage

partial_cor(data, ..., controls, weights = NULL)

Arguments

Details

Understanding the Output

Comparing partial_r against zero_order_r tells you what the controls contribute:

• Partial clearly smaller than zero-order: much of the original association runs through the control variables (confounding or mediation).

• Partial similar to zero-order: the association is largely independent of the controls.

• Partial larger than zero-order: a suppressor situation — the controls masked part of the association.

When to Use This

• Check whether a bivariate correlation survives controlling for demographics (age, education, ...)

• Separate the direct association of two attitudes from what a shared cause explains

For full multivariate control with several predictors, use linear_regression instead.

Technical Details

Cases are deleted listwise across the analysis and control variables (SPSS /MISSING=LISTWISE, the PARTIAL CORR default). The partial correlation is computed from the Pearson correlation matrix of all variables via the inverse of the control-variable block; the same Pearson formula as pearson_cor is used throughout, so weighted results follow the SPSS frequency-weight convention with unrounded sum(w) in df and test statistics. Significance is two-tailed (the SPSS default).

An SPSS v29 PARTIAL CORR reference run is pending; until it lands the statistics are verified against the independent residual-of-regressions characterization (see the SPSS compatibility vignette).

Value

An object of class "partial_cor" whose $correlations tibble holds one row per variable pair (and group combination) with:

partial_r

Partial correlation controlling for the controls

zero_order_r

The ordinary (zero-order) Pearson correlation of the pair, for comparison — SPSS /STATISTICS=CORR

df

Degrees of freedom, n - 2 - k with k control variables (non-integer for weighted data, Charter §5.1)

t_stat, p_value

t-test of the partial correlation (two-tailed, the SPSS default)

n

Listwise-complete sample size (rounded weighted N when weighted)

For three or more analysis variables, $matrices additionally holds the full partial-correlation matrix per group.

See Also

pearson_cor for zero-order correlations.

linear_regression for multivariate control.

summary.partial_cor for detailed output.

Other correlation: kendall_tau(), pearson_cor(), spearman_rho()

Examples

library(dplyr)
data(survey_data)

# Does the satisfaction-income correlation survive controlling for age?
partial_cor(survey_data, life_satisfaction, income, controls = age)

# Several variables, several controls
partial_cor(survey_data, trust_government, trust_media, trust_science,
            controls = c(age, political_orientation))

# Weighted (SPSS WEIGHT BY)
partial_cor(survey_data, life_satisfaction, income,
            controls = age, weights = sampling_weight)

# Grouped (SPSS SPLIT FILE)
survey_data %>%
  group_by(gender) %>%
  partial_cor(life_satisfaction, income, controls = age)

# --- Three-layer output ---
result <- partial_cor(survey_data, life_satisfaction, income, controls = age)
result              # compact overview
summary(result)     # full detailed output

Measure How Strongly Variables Are Related

Description

pearson_cor() shows you how strongly numeric variables are related to each other. For example, is age related to income? Does satisfaction increase with experience? This helps you understand patterns in your data.

The correlation tells you:

• Direction: Positive (both increase together) or negative (one increases as other decreases)

• Strength: How closely the variables move together (from 0 = no relationship to 1 = perfect relationship)

• Significance: Whether the relationship is real or could be due to chance

Usage

pearson_cor(
  data,
  ...,
  weights = NULL,
  conf.level = 0.95,
  alternative = c("two.sided", "less", "greater"),
  use = c("pairwise", "listwise"),
  na.rm = NULL
)

Arguments

Details

Understanding the Results

Correlation coefficient (r) ranges from -1 to +1:

• +1: Perfect positive relationship (as one goes up, the other always goes up)

• 0: No linear relationship

• -1: Perfect negative relationship (as one goes up, the other always goes down)

Interpreting strength (absolute value of r):

• 0.00 - 0.10: Negligible relationship

• 0.10 - 0.30: Weak relationship

• 0.30 - 0.50: Moderate relationship

• 0.50 - 0.70: Strong relationship

• 0.70 - 0.90: Very strong relationship

• 0.90 - 1.00: Extremely strong relationship

P-value interpretation:

• p < 0.001: Very strong evidence of a relationship

• p < 0.01: Strong evidence of a relationship

• p < 0.05: Moderate evidence of a relationship

• p >= 0.05: No significant relationship found

A correlation of 0.65 with p < 0.001 means:

• Strong positive relationship (r = 0.65)

• As one variable increases, the other tends to increase

• Very unlikely to be due to chance (p < 0.001)

• About 42% of variation is shared (r-squared = 0.65 squared = 0.42)

When to Use This

Use Pearson correlation when:

• Both variables are numeric and continuous

• You expect a linear relationship

• Data is roughly normally distributed

• You want to measure strength of linear association

Don't use when:

• Data has extreme outliers (consider Spearman instead)

• Relationship is curved/non-linear

• Variables are categorical (use chi-squared test)

• You need to establish causation (correlation does not imply causation)

Tips for Success

• Always plot your data first to check for non-linear patterns

• Consider both statistical significance (p-value) and practical importance (r value)

• Remember: correlation does not imply causation

• Check for outliers that might inflate or deflate correlations

• Use Spearman correlation for ordinal data or non-normal distributions

Value

Correlation results showing relationships between variables, including:

• Correlation coefficient (r): Strength and direction of relationship

• P-value: Whether the relationship is statistically significant

• Confidence interval: Range of plausible correlation values

• Sample size: Number of observations used Use summary() for the full SPSS-style output with toggleable sections.

References

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.

Fisher, R. A. (1915). Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population. Biometrika, 10(4), 507–521.

See Also

cor for the base R correlation function.

cor.test for correlation significance testing.

spearman_rho for rank-based correlation (robust to outliers).

kendall_tau for ordinal correlation.

summary.pearson_cor for detailed output with toggleable sections.

Other correlation: kendall_tau(), partial_cor(), spearman_rho()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Basic correlation between two variables
survey_data %>%
  pearson_cor(age, income)

# Correlation matrix for multiple variables
survey_data %>%
  pearson_cor(age, income, life_satisfaction)

# Weighted correlations
survey_data %>%
  pearson_cor(age, income, weights = sampling_weight)

# Grouped correlations
survey_data %>%
  group_by(region) %>%
  pearson_cor(age, income, life_satisfaction)

# Using tidyselect helpers
survey_data %>%
  pearson_cor(where(is.numeric), weights = sampling_weight)

# Listwise deletion for missing data
survey_data %>%
  pearson_cor(age, income, use = "listwise")

# --- Three-layer output ---
result <- survey_data %>%
  pearson_cor(age, income, life_satisfaction, weights = sampling_weight)
result              # compact one-line overview
summary(result)     # full correlation, p-value, and N matrices
summary(result, pvalue_matrix = FALSE)  # hide p-values

Effect Sizes for Contingency Tables

Description

Convenience helpers that run chi_square and return just the requested effect size as a numeric value (named by group for grouped data): phi() for 2x2 tables, cramers_v() for larger tables, and goodman_gamma() for ordinal variables.

For the full test output (chi-square statistic, p-value, all effect sizes), call chi_square directly.

Usage

phi(data, ..., weights = NULL)

cramers_v(data, ..., weights = NULL)

goodman_gamma(data, ..., weights = NULL)

Arguments

Value

A numeric vector with the effect size (one element per group for grouped data).

See Also

chi_square

Examples

data(survey_data)
phi(survey_data, gender, region)
cramers_v(survey_data, education, region)
goodman_gamma(survey_data, education, life_satisfaction)

Transform Scores to Percent of Maximum Possible (POMPS)

Description

pomps() transforms scores to a 0-100 scale using the Percent of Maximum Possible Scores method. This makes different scales directly comparable regardless of their original range.

This is the R equivalent of the SPSS formula: COMPUTE v81p = ((v81 - 1) / (7 - 1)) * 100.

A score of 0 means the minimum possible score, 100 means the maximum. The transformation preserves all correlations between variables.

Usage

pomps(x, scale_min = NULL, scale_max = NULL)

Arguments

Details

The Formula

POMPS = ((score - scale_min) / (scale_max - scale_min)) * 100

Why Specify scale_min and scale_max?

By default, pomps() uses the observed minimum and maximum of your data. However, for Likert scales you should specify the theoretical range:

• A 1-5 Likert scale: scale_min = 1, scale_max = 5

• A 1-7 Likert scale: scale_min = 1, scale_max = 7

• A 0-10 scale: scale_min = 0, scale_max = 10

Using theoretical values ensures that the transformation is consistent across samples and time points.

When to Use This

• Comparing variables measured on different scales

• Creating profile plots across scales with different ranges

• Reporting scale scores in an intuitive 0-100 format

Value

A numeric vector of the same length as x, with values rescaled to the 0-100 range.

See Also

row_means for creating mean indices across items.

Other scale: efa(), reliability(), row_count(), row_means(), row_sums()

Examples

library(dplyr)
data(survey_data)

# Transform a 1-5 Likert scale to POMPS
survey_data <- survey_data %>%
  mutate(trust_gov_pomps = pomps(trust_government, scale_min = 1, scale_max = 5))

# Transform multiple variables with the same scale
survey_data <- survey_data %>%
  mutate(across(
    c(trust_government, trust_media, trust_science),
    ~ pomps(.x, scale_min = 1, scale_max = 5),
    .names = "{.col}_pomps"
  ))

# Auto-detect range (uses observed min/max)
survey_data <- survey_data %>%
  mutate(age_pomps = pomps(age))

Predict from a linear_regression model

Description

For listwise + ungrouped results, dispatches to stats::predict.lm (the result inherits from "lm"). For grouped or pairwise results, raises an informative error.

Usage

## S3 method for class 'linear_regression'
predict(object, ...)

Arguments

Value

A numeric vector of predictions (or a matrix/list, depending on the arguments), as returned by stats::predict.lm.

Predict from a logistic_regression model

Description

For ungrouped results, dispatches to stats::predict.glm (the result inherits from "glm"). For grouped results, raises an informative error pointing at object$groups.

Usage

## S3 method for class 'logistic_regression'
predict(object, ...)

Arguments

Value

A numeric vector of predictions on the scale requested via type (link scale by default), as returned by stats::predict.glm.

Print ANCOVA results (compact)

Description

Compact print method for objects of class "ancova". Shows factor effects and covariates with F statistics, p-values, and effect sizes.

For the full detailed output, use summary().

Usage

## S3 method for class 'ancova'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- ancova(survey_data, dv = life_satisfaction, between = gender, covariate = age)
result              # compact overview
summary(result)     # full detailed output

Print binomial test results (compact)

Description

Compact print method for objects of class "binomial_test". Shows a one-line summary per variable with the observed vs. test proportion, p-value, and sample size.

For the full detailed output (category table, test statistics with confidence interval), use summary().

Usage

## S3 method for class 'binomial_test'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- binomial_test(survey_data, gender, p = 0.50)
result              # compact one-line overview
summary(result)     # full detailed output

Print chi-squared test results (compact)

Description

Compact print method for objects of class "chi_square". Shows a one-line summary per test with test statistic, p-value, effect size, and sample size.

For the full detailed output, use summary().

Usage

## S3 method for class 'chi_square'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- chi_square(survey_data, gender, education)
result              # compact one-line overview
summary(result)     # full detailed output

Print chi-square goodness-of-fit test results (compact)

Description

Compact print method for objects of class "chisq_gof". Shows a one-line summary per variable with the chi-square statistic, degrees of freedom, p-value, and sample size.

For the full detailed output (frequency tables with observed, expected, and residual counts), use summary().

Usage

## S3 method for class 'chisq_gof'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- chisq_gof(survey_data, gender)
result              # compact one-line overview
summary(result)     # full detailed output

Print a Compact Console Overview of the Codebook

Description

Displays a concise one-line-per-variable table in the console, showing position, name, type, and label. The full HTML codebook with values, value labels, and frequencies is displayed in the RStudio Viewer.

Usage

## S3 method for class 'codebook'
print(x, ...)

Arguments

Value

Invisibly returns the codebook object

Examples

data(survey_data)
cb <- codebook(survey_data)
print(cb)

Print method for crosstab results (compact)

Description

Compact print method for objects of class "crosstab". Shows the table variables, dimensions, and valid N, plus a reminder that no significance test is included.

For the full cross-tabulation table (cell counts and percentages), use summary().

Usage

## S3 method for class 'crosstab'
print(x, digits = 1, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- crosstab(survey_data, gender, region)
result              # compact overview
summary(result)     # full cross-tabulation table

Print method for describe objects

Description

Prints formatted descriptive statistics with professional layout. Automatically adjusts output based on whether analysis was weighted or unweighted. The output of describe() is a summary table by nature, so print() and summary() display the same statistics table; summary() additionally offers a statistics section toggle and a digits option.

Usage

## S3 method for class 'describe'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Print Dunn post-hoc test results (compact)

Description

Compact print method for objects of class "dunn_test". Shows one line per variable (or group combination) with the number of pairwise comparisons and how many are significant at the .05 level.

For the full comparison tables (Z-statistics, adjusted p-values), use summary().

Usage

## S3 method for class 'dunn_test'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- kruskal_wallis(survey_data, life_satisfaction,
                         group = education) |> dunn_test()
result              # compact overview
summary(result)     # full comparison tables

Print EFA results (compact)

Description

Compact print method for objects of class "efa". Shows KMO value, number of factors, total variance explained, extraction method, and rotation in a concise format.

For the full detailed output including communalities, variance explained per factor, and rotated component matrices, use summary().

Usage

## S3 method for class 'efa'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- efa(survey_data, political_orientation, environmental_concern,
              life_satisfaction, trust_government, trust_media, trust_science)
result              # compact overview
summary(result)     # full detailed output

Print factorial ANOVA results (compact)

Description

Compact print method for objects of class "factorial_anova". Shows main effects and interactions with F statistics, p-values, and effect sizes.

For the full detailed output, use summary().

Usage

## S3 method for class 'factorial_anova'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- factorial_anova(survey_data,
                          dv = life_satisfaction,
                          between = c(gender, education))
result              # compact overview
summary(result)     # full detailed output

Print Fisher's exact test results (compact)

Description

Compact print method for objects of class "fisher_test". Shows a one-line summary with the exact p-value, significance stars, and sample size.

For the full detailed output (contingency table, test results), use summary().

Usage

## S3 method for class 'fisher_test'
print(x, digits = 4, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- fisher_test(survey_data, row = gender, col = region)
result              # compact one-line overview
summary(result)     # full detailed output

Print method for frequency objects (compact)

Description

Compact print method for objects of class "frequency". Shows one line per variable (and group combination) with the number of categories, the valid N, and the missing count.

For the full frequency tables (counts, percentages, cumulative percentages), use summary().

Usage

## S3 method for class 'frequency'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- frequency(survey_data, gender)
result              # compact overview
summary(result)     # full frequency tables

Print Friedman test results (compact)

Description

Compact print method for objects of class "friedman_test". Shows a one-line summary with the chi-square statistic, p-value, Kendall's W, and sample size.

For the full detailed output (rank tables, test statistics), use summary().

Usage

## S3 method for class 'friedman_test'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- friedman_test(survey_data, trust_government, trust_media,
                        trust_science)
result              # compact one-line overview
summary(result)     # full detailed output

Print Kendall's tau results (compact)

Description

Compact print method for objects of class "kendall_tau". Shows tau coefficient, p-value, and sample size per pair.

For the full detailed output including matrices, use summary().

Usage

## S3 method for class 'kendall_tau'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- kendall_tau(survey_data[1:300, ], age, life_satisfaction)
result              # compact one-line overview
summary(result)     # full correlation matrices

Print Kruskal-Wallis test results (compact)

Description

Compact print method for objects of class "kruskal_wallis". Shows a one-line summary per variable with the H statistic, p-value, effect size (epsilon-squared), and sample size.

For the full detailed output (rank tables, test statistics), use summary().

Usage

## S3 method for class 'kruskal_wallis'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- kruskal_wallis(survey_data, life_satisfaction, group = education)
result              # compact one-line overview
summary(result)     # full detailed output

Print Levene test results (compact)

Description

Compact print method for objects of class "levene_test". Shows a one-line summary per variable with the F statistic, degrees of freedom, p-value, and the equal/unequal variances conclusion.

For the full detailed output (results tables, interpretation, recommendation), use summary().

Usage

## S3 method for class 'levene_test'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- levene_test(survey_data, life_satisfaction, group = education)
result              # compact one-line overview
summary(result)     # full detailed output

Print linear regression results (compact)

Description

Compact print method for objects of class "linear_regression". Shows R-squared, adjusted R-squared, F statistic, and p-value.

For the full detailed output, use summary().

Usage

## S3 method for class 'linear_regression'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- linear_regression(survey_data, life_satisfaction ~ age + income)
result              # compact one-line overview
summary(result)     # full detailed output

Print logistic regression results (compact)

Description

Compact print method for objects of class "logistic_regression". Shows Nagelkerke R-squared, chi-squared test, and classification accuracy.

For the full detailed output, use summary().

Usage

## S3 method for class 'logistic_regression'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

survey_data$high_satisfaction <- as.integer(survey_data$life_satisfaction > 3)
result <- logistic_regression(survey_data, high_satisfaction ~ age + income)
result              # compact one-line overview
summary(result)     # full detailed output

Print Mann-Whitney test results (compact)

Description

Compact print method for objects of class "mann_whitney". Shows a one-line summary per variable with test statistic, p-value, effect size, and sample size.

For the full detailed output, use summary().

Usage

## S3 method for class 'mann_whitney'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- mann_whitney(survey_data, life_satisfaction, group = gender)
result              # compact one-line overview
summary(result)     # full detailed output

Print average marginal effects (compact)

Description

Compact print method for objects of class "marginal_effects": one line per predictor with the AME on the probability scale.

Usage

## S3 method for class 'marginal_effects'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

survey_data$high_satisfaction <- as.integer(survey_data$life_satisfaction >= 4)
model <- logistic_regression(survey_data, high_satisfaction ~ age + income)
marginal_effects(model)

Print McNemar test results (compact)

Description

Compact print method for objects of class "mcnemar_test". Shows a one-line summary with the McNemar chi-square statistic, the asymptotic and exact p-values, and sample size.

For the full detailed output (2x2 contingency table, test results, discordant pairs), use summary().

Usage

## S3 method for class 'mcnemar_test'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

test_data <- transform(survey_data,
  trust_gov_high = as.integer(trust_government >= 4),
  trust_media_high = as.integer(trust_media >= 4))
result <- mcnemar_test(test_data, var1 = trust_gov_high,
                       var2 = trust_media_high)
result              # compact one-line overview
summary(result)     # full detailed output

Print multiple response results (compact)

Description

Compact print method for objects of class "multiple_response": one line per answer option with mentions and percent of cases.

Usage

## S3 method for class 'multiple_response'
print(x, digits = 1, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

d <- survey_data
d$gov <- as.integer(d$trust_government >= 4)
d$media <- as.integer(d$trust_media >= 4)
multiple_response(d, gov, media)

Print normality test results (compact)

Description

Compact print method for objects of class "normality_test". Shows one line per variable with both test results. For grouped analyses, only the dimensions are shown — use summary() for the per-group tables.

Usage

## S3 method for class 'normality_test'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- normality_test(survey_data, age, income)
result              # compact overview
summary(result)     # full detailed output

Print ANOVA test results (compact)

Description

Compact print method for objects of class "oneway_anova". Shows a one-line summary per variable with F statistic, p-value, effect size, and sample size.

For the full detailed output, use summary().

Usage

## S3 method for class 'oneway_anova'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- oneway_anova(survey_data, life_satisfaction,
                       group = education)
result              # compact one-line overview
summary(result)     # full detailed output

Print pairwise Wilcoxon post-hoc test results (compact)

Description

Compact print method for objects of class "pairwise_wilcoxon". Shows one line per group combination with the number of pairwise comparisons and how many are significant at the .05 level.

For the full comparison tables (Z-statistics, adjusted p-values), use summary().

Usage

## S3 method for class 'pairwise_wilcoxon'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- friedman_test(survey_data, trust_government, trust_media,
                        trust_science) |> pairwise_wilcoxon()
result              # compact overview
summary(result)     # full comparison tables

Print partial correlation results (compact)

Description

Compact print method for objects of class "partial_cor": one line per variable pair with the partial correlation, p-value, and the zero-order correlation for comparison.

Usage

## S3 method for class 'partial_cor'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- partial_cor(survey_data, life_satisfaction, income, controls = age)
result              # compact overview
summary(result)     # full detailed output

Print Pearson correlation results (compact)

Description

Compact print method for objects of class "pearson_cor". Shows correlation coefficient, p-value, and sample size per pair.

For the full detailed output including matrices, use summary().

Usage

## S3 method for class 'pearson_cor'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- pearson_cor(survey_data, age, life_satisfaction)
result              # compact one-line overview
summary(result)     # full correlation matrices

Print reliability results (compact)

Description

Compact print method for objects of class "reliability". Shows Cronbach's Alpha (with quality interpretation), McDonald's Omega, and item count in a single line per group.

For the full detailed output including item statistics, inter-item correlations, and item-total statistics, use summary().

Usage

## S3 method for class 'reliability'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- reliability(survey_data, trust_government, trust_media, trust_science)
result              # compact one-line overview
summary(result)     # full detailed output

Print Scheffe test results (compact)

Description

Compact print method for objects of class "scheffe_test". Shows one line per variable (or factor, or group combination) with the number of pairwise comparisons and how many are significant at the .05 level.

For the full comparison tables (mean differences, confidence intervals, adjusted p-values), use summary().

Usage

## S3 method for class 'scheffe_test'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- oneway_anova(survey_data, life_satisfaction,
                       group = education) |> scheffe_test()
result              # compact overview
summary(result)     # full comparison tables

Print Spearman correlation results (compact)

Description

Compact print method for objects of class "spearman_rho". Shows rank correlation coefficient, p-value, and sample size per pair.

For the full detailed output including matrices, use summary().

Usage

## S3 method for class 'spearman_rho'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- spearman_rho(survey_data, age, life_satisfaction)
result              # compact one-line overview
summary(result)     # full correlation matrices

Print summary of ANCOVA results (detailed output)

Description

Displays the detailed SPSS-style output for an ANCOVA, with sections controlled by the boolean parameters passed to summary.ancova. Sections include the ANCOVA table with Type III sums of squares, effect sizes, estimated marginal means, and Levene's test for homogeneity of variances.

Usage

## S3 method for class 'summary.ancova'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

ancova for the main analysis, summary.ancova for summary options.

Examples

result <- ancova(survey_data, dv = life_satisfaction, between = gender, covariate = age)
summary(result)                          # all sections
summary(result, marginal_means = FALSE)  # hide marginal means

Print summary of binomial test results (detailed output)

Description

Displays the detailed SPSS-style output for a binomial test, with sections controlled by the boolean parameters passed to summary.binomial_test. Sections include the category table and the test statistics table with confidence interval.

Usage

## S3 method for class 'summary.binomial_test'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

binomial_test for the main analysis, summary.binomial_test for summary options.

Examples

result <- binomial_test(survey_data, gender, p = 0.50)
summary(result)                     # all sections
summary(result, categories = FALSE) # hide category tables

Print summary of chi-squared test results (detailed output)

Description

Displays the detailed SPSS-style output for a chi-squared test, with sections controlled by the boolean parameters passed to summary.chi_square. Sections include cross-tabulation, test results, and effect sizes (Cramer's V, Phi).

Usage

## S3 method for class 'summary.chi_square'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

chi_square for the main analysis, summary.chi_square for summary options.

Examples

result <- chi_square(survey_data, gender, education)
summary(result)                          # all sections
summary(result, cross_tabulation = FALSE) # hide crosstab

Print summary of chi-square goodness-of-fit results (detailed output)

Description

Displays the detailed output for a chi-square goodness-of-fit test, with sections controlled by the boolean parameters passed to summary.chisq_gof. Sections include per-variable frequency tables and the test statistics table.

Usage

## S3 method for class 'summary.chisq_gof'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

chisq_gof for the main analysis, summary.chisq_gof for summary options.

Examples

result <- chisq_gof(survey_data, gender)
summary(result)                          # all sections
summary(result, frequency_table = FALSE) # hide frequency tables

Print a Detailed Console Codebook Summary

Description

Renders the verbose codebook summary to the console with boolean-gated sections for overview, variable details, and value labels.

Usage

## S3 method for class 'summary.codebook'
print(x, ...)

Arguments

Value

Invisibly returns the summary object

Examples

data(survey_data)
cb <- codebook(survey_data)
print(summary(cb))

Print summary of crosstab results (detailed output)

Description

Displays the full cross-tabulation table for a crosstab result, with sections controlled by the boolean parameters passed to summary.crosstab. For grouped analyses, a separate table is displayed for each group combination.

Usage

## S3 method for class 'summary.crosstab'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

crosstab for the main analysis, summary.crosstab for summary options.

Examples

result <- crosstab(survey_data, gender, region)
summary(result)                       # full table
summary(result, percentages = FALSE) # counts only

Print summary of describe results (detailed output)

Description

Displays the descriptive statistics table for a describe result, controlled by the boolean parameter passed to summary.describe.

Usage

## S3 method for class 'summary.describe'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

describe for the main analysis, summary.describe for summary options.

Examples

result <- describe(survey_data, age, income)
summary(result)

Print summary of Dunn post-hoc test results (detailed output)

Description

Displays the detailed output for Dunn pairwise comparisons, with sections controlled by the boolean parameters passed to summary.dunn_test. The display includes:

• Pairwise group comparisons with Z-statistics

• Adjusted p-values controlling for multiple comparisons

• Significance indicators (* p < 0.05, ** p < 0.01, *** p < 0.001)

For grouped analyses, results are displayed separately for each group combination.

Usage

## S3 method for class 'summary.dunn_test'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

dunn_test for the main analysis, summary.dunn_test for summary options.

Examples

result <- kruskal_wallis(survey_data, life_satisfaction,
                         group = education) |> dunn_test()
summary(result)                       # all sections
summary(result, comparisons = FALSE)  # hide comparison tables

Print summary of EFA results (detailed output)

Description

Displays the detailed SPSS-style output for an Exploratory Factor Analysis, with sections controlled by the boolean parameters passed to summary.efa. Sections include KMO and Bartlett's test, communalities, variance explained, and rotated component/pattern matrices.

Usage

## S3 method for class 'summary.efa'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

efa for the main analysis, summary.efa for summary options.

Examples

result <- efa(survey_data, political_orientation, environmental_concern,
              life_satisfaction, trust_government, trust_media, trust_science)
summary(result)                        # all sections
summary(result, communalities = FALSE) # hide communalities

Print summary of factorial ANOVA results (detailed output)

Description

Displays the detailed SPSS-style output for a factorial ANOVA, with sections controlled by the boolean parameters passed to summary.factorial_anova. Sections include the ANOVA table with Type III sums of squares, effect sizes, estimated marginal means, and Levene's test for homogeneity of variances.

Usage

## S3 method for class 'summary.factorial_anova'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

factorial_anova for the main analysis, summary.factorial_anova for summary options.

Examples

result <- factorial_anova(survey_data,
                          dv = life_satisfaction,
                          between = c(gender, education))
summary(result)                          # all sections
summary(result, marginal_means = FALSE)  # hide marginal means

Print summary of Fisher's exact test results (detailed output)

Description

Displays the detailed output for Fisher's exact test, with sections controlled by the boolean parameters passed to summary.fisher_test. Sections include the contingency table and the test results table.

Usage

## S3 method for class 'summary.fisher_test'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

fisher_test for the main analysis, summary.fisher_test for summary options.

Examples

result <- fisher_test(survey_data, row = gender, col = region)
summary(result)                            # all sections
summary(result, contingency_table = FALSE) # hide contingency table

Print summary of frequency results (detailed output)

Description

Prints formatted frequency statistics with ASCII tables, with sections controlled by the boolean parameters passed to summary.frequency.

Usage

## S3 method for class 'summary.frequency'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

frequency for the main analysis, summary.frequency for summary options.

Examples

result <- frequency(survey_data, gender)
summary(result)                          # all sections
summary(result, frequency_table = FALSE) # only summary statistics

Print summary of Friedman test results (detailed output)

Description

Displays the detailed SPSS-style output for a Friedman test, with sections controlled by the boolean parameters passed to summary.friedman_test. Sections include the mean rank table and the test statistics table with effect size interpretation.

Usage

## S3 method for class 'summary.friedman_test'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

friedman_test for the main analysis, summary.friedman_test for summary options.

Examples

result <- friedman_test(survey_data, trust_government, trust_media,
                        trust_science)
summary(result)                # all sections
summary(result, ranks = FALSE) # hide rank table

Print summary of Kendall's tau correlation results (detailed output)

Description

Displays the detailed SPSS-style output for a Kendall's tau correlation, with sections controlled by the boolean parameters passed to summary.kendall_tau. Sections include the correlation matrix, p-value matrix, and sample size matrix.

Usage

## S3 method for class 'summary.kendall_tau'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

kendall_tau for the main analysis, summary.kendall_tau for summary options.

Examples

result <- kendall_tau(survey_data[1:300, ], age, life_satisfaction)
summary(result)                             # all matrices
summary(result, pvalue_matrix = FALSE)      # hide p-values

Print summary of Kruskal-Wallis test results (detailed output)

Description

Displays the detailed SPSS-style output for a Kruskal-Wallis test, with sections controlled by the boolean parameters passed to summary.kruskal_wallis. Sections include the per-group rank table and the test statistics table with effect size.

Usage

## S3 method for class 'summary.kruskal_wallis'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

kruskal_wallis for the main analysis, summary.kruskal_wallis for summary options.

Examples

result <- kruskal_wallis(survey_data, life_satisfaction, group = education)
summary(result)                # all sections
summary(result, ranks = FALSE) # hide rank tables

Print summary of Levene test results (detailed output)

Description

Displays the detailed output for Levene's test of homogeneity of variance, with sections controlled by the boolean parameters passed to summary.levene_test. Sections include the results tables, interpretation, and recommendation.

Usage

## S3 method for class 'summary.levene_test'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

levene_test for the main analysis, summary.levene_test for summary options.

Examples

result <- levene_test(survey_data, life_satisfaction, group = education)
summary(result)                          # all sections
summary(result, interpretation = FALSE)  # hide interpretation

Print summary of linear regression results (detailed output)

Description

Displays the detailed SPSS-style output for a linear regression, with sections controlled by the boolean parameters passed to summary.linear_regression. Sections include model summary (R-squared, F-test), coefficients table (B, SE, Beta, t, p), and collinearity diagnostics (Tolerance, VIF).

Usage

## S3 method for class 'summary.linear_regression'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

linear_regression for the main analysis, summary.linear_regression for summary options.

Examples

result <- linear_regression(survey_data, life_satisfaction ~ age + income)
summary(result)                           # all sections
summary(result, collinearity = FALSE)     # hide VIF/Tolerance

Print summary of logistic regression results (detailed output)

Description

Displays the detailed SPSS-style output for a logistic regression, with sections controlled by the boolean parameters passed to summary.logistic_regression. Sections include the omnibus test of model coefficients, model fit statistics (Nagelkerke R-squared, Hosmer-Lemeshow), classification table, and coefficients with odds ratios.

Usage

## S3 method for class 'summary.logistic_regression'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

logistic_regression for the main analysis, summary.logistic_regression for summary options.

Examples

survey_data$high_satisfaction <- as.integer(survey_data$life_satisfaction > 3)
result <- logistic_regression(survey_data, high_satisfaction ~ age + income)
summary(result)                         # all sections
summary(result, classification = FALSE) # hide classification table

Print summary of Mann-Whitney test results (detailed output)

Description

Displays the detailed SPSS-style output for a Mann-Whitney U test, with sections controlled by the boolean parameters passed to summary.mann_whitney. Sections include rank statistics, test results, and effect sizes (rank-biserial correlation).

Usage

## S3 method for class 'summary.mann_whitney'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

mann_whitney for the main analysis, summary.mann_whitney for summary options.

Examples

result <- mann_whitney(survey_data, life_satisfaction, group = gender)
summary(result)                        # all sections
summary(result, effect_sizes = FALSE)  # hide effect sizes

Print summary of average marginal effects (detailed output)

Description

Displays the detailed AME table for a marginal_effects result, per group for grouped models.

Usage

## S3 method for class 'summary.marginal_effects'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

marginal_effects for the main analysis, summary.marginal_effects for summary options.

Examples

survey_data$high_satisfaction <- as.integer(survey_data$life_satisfaction >= 4)
model <- logistic_regression(survey_data, high_satisfaction ~ age + income)
summary(marginal_effects(model))

Print summary of McNemar test results (detailed output)

Description

Displays the detailed output for a McNemar test, with sections controlled by the boolean parameters passed to summary.mcnemar_test. Sections include the 2x2 contingency table, the test results table, and the discordant pairs.

Usage

## S3 method for class 'summary.mcnemar_test'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

mcnemar_test for the main analysis, summary.mcnemar_test for summary options.

Examples

test_data <- transform(survey_data,
  trust_gov_high = as.integer(trust_government >= 4),
  trust_media_high = as.integer(trust_media >= 4))
result <- mcnemar_test(test_data, var1 = trust_gov_high,
                       var2 = trust_media_high)
summary(result)                            # all sections
summary(result, discordant_pairs = FALSE)  # hide discordant pairs

Print summary of multiple response results (detailed output)

Description

Displays the detailed SPSS-style MULT RESPONSE tables for a multiple_response result, per group for grouped data.

Usage

## S3 method for class 'summary.multiple_response'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

multiple_response for the main analysis, summary.multiple_response for summary options.

Examples

d <- survey_data
d$gov <- as.integer(d$trust_government >= 4)
d$media <- as.integer(d$trust_media >= 4)
summary(multiple_response(d, gov, media, by = gender))

Print summary of normality test results (detailed output)

Description

Displays the SPSS-style "Tests of Normality" table (Kolmogorov-Smirnov with Lilliefors correction, Shapiro-Wilk) for a normality_test result, per group for grouped data.

Usage

## S3 method for class 'summary.normality_test'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

normality_test for the main analysis, summary.normality_test for summary options.

Examples

result <- normality_test(survey_data, age, income)
summary(result)

Print summary of one-way ANOVA results (detailed output)

Description

Displays the detailed SPSS-style output for a one-way ANOVA, with sections controlled by the boolean parameters passed to summary.oneway_anova. Sections include group descriptives, ANOVA table, homogeneity of variances (Levene's test), and effect sizes.

Usage

## S3 method for class 'summary.oneway_anova'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

oneway_anova for the main analysis, summary.oneway_anova for summary options.

Examples

result <- oneway_anova(survey_data, life_satisfaction, group = education)
summary(result)                        # all sections
summary(result, descriptives = FALSE)  # hide group statistics

Print summary of pairwise Wilcoxon post-hoc test results (detailed output)

Description

Displays the detailed output for pairwise Wilcoxon comparisons, with sections controlled by the boolean parameters passed to summary.pairwise_wilcoxon. The display includes:

• Pairwise measurement comparisons with Z-statistics

• Adjusted p-values controlling for multiple comparisons

• Significance indicators (* p < 0.05, ** p < 0.01, *** p < 0.001)

For grouped analyses, results are displayed separately for each group.

Usage

## S3 method for class 'summary.pairwise_wilcoxon'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

pairwise_wilcoxon for the main analysis, summary.pairwise_wilcoxon for summary options.

Examples

result <- friedman_test(survey_data, trust_government, trust_media,
                        trust_science) |> pairwise_wilcoxon()
summary(result)                       # all sections
summary(result, comparisons = FALSE)  # hide comparison tables

Print summary of partial correlation results (detailed output)

Description

Displays the detailed SPSS-style output for a partial_cor result, with sections controlled by the boolean parameters passed to summary.partial_cor.

Usage

## S3 method for class 'summary.partial_cor'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

partial_cor for the main analysis, summary.partial_cor for summary options.

Examples

result <- partial_cor(survey_data, life_satisfaction, income, controls = age)
summary(result)

Print summary of Pearson correlation results (detailed output)

Description

Displays the detailed SPSS-style output for a Pearson correlation, with sections controlled by the boolean parameters passed to summary.pearson_cor. Sections include the correlation matrix, p-value matrix, and sample size matrix.

Usage

## S3 method for class 'summary.pearson_cor'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

pearson_cor for the main analysis, summary.pearson_cor for summary options.

Examples

result <- pearson_cor(survey_data, age, life_satisfaction)
summary(result)                             # all matrices
summary(result, pvalue_matrix = FALSE)      # hide p-values

Print summary of reliability analysis results (detailed output)

Description

Displays the detailed SPSS-style output for a reliability analysis, with sections controlled by the boolean parameters passed to summary.reliability. Sections include reliability statistics, item statistics, inter-item correlations, and item-total statistics.

Usage

## S3 method for class 'summary.reliability'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

reliability for the main analysis, summary.reliability for summary options.

Examples

result <- reliability(survey_data, trust_government, trust_media, trust_science)
summary(result)                                  # all sections
summary(result, inter_item_correlations = FALSE)  # hide correlations

Print summary of Scheffe test results (detailed output)

Description

Displays the detailed output for Scheffe post-hoc comparisons, with sections controlled by the boolean parameters passed to summary.scheffe_test. The display includes:

• Pairwise group comparisons with mean differences

• Confidence intervals for differences (widest among all post-hoc tests)

• Scheffe-adjusted p-values controlling family-wise error rate

• Significance indicators (* p < 0.05, ** p < 0.01, *** p < 0.001)

For grouped analyses, results are displayed separately for each group combination. For weighted analyses, effective sample sizes are used in calculations.

Usage

## S3 method for class 'summary.scheffe_test'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

scheffe_test for the main analysis, summary.scheffe_test for summary options.

Examples

result <- oneway_anova(survey_data, life_satisfaction,
                       group = education) |> scheffe_test()
summary(result)                       # all sections
summary(result, comparisons = FALSE)  # hide comparison tables

Print summary of Spearman correlation results (detailed output)

Description

Displays the detailed SPSS-style output for a Spearman rank correlation, with sections controlled by the boolean parameters passed to summary.spearman_rho. Sections include the correlation matrix, p-value matrix, and sample size matrix.

Usage

## S3 method for class 'summary.spearman_rho'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

spearman_rho for the main analysis, summary.spearman_rho for summary options.

Examples

result <- spearman_rho(survey_data, age, life_satisfaction)
summary(result)                             # all matrices
summary(result, pvalue_matrix = FALSE)      # hide p-values

Print summary of t-test results (detailed output)

Description

Displays the detailed SPSS-style output for a t-test, with sections controlled by the boolean parameters passed to summary.t_test. Sections include group descriptives, test results (equal and unequal variances), and effect sizes.

Usage

## S3 method for class 'summary.t_test'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

t_test for the main analysis, summary.t_test for summary options.

Examples

result <- t_test(survey_data, life_satisfaction, group = gender)
summary(result)                        # all sections
summary(result, effect_sizes = FALSE)  # hide effect sizes

Print summary of Tukey HSD test results (detailed output)

Description

Displays the detailed output for Tukey HSD post-hoc comparisons, with sections controlled by the boolean parameters passed to summary.tukey_test. The display includes:

• Pairwise group comparisons with mean differences

• Confidence intervals for differences

• Tukey-adjusted p-values controlling family-wise error rate

• Significance indicators (* p < 0.05, ** p < 0.01, *** p < 0.001)

For grouped analyses, results are displayed separately for each group combination. For weighted analyses, effective sample sizes are used in calculations.

Usage

## S3 method for class 'summary.tukey_test'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

tukey_test for the main analysis, summary.tukey_test for summary options.

Examples

result <- oneway_anova(survey_data, life_satisfaction,
                       group = education) |> tukey_test()
summary(result)                     # all sections
summary(result, comparisons = FALSE)  # hide comparison tables

Print summary of Wilcoxon signed-rank test results (detailed output)

Description

Displays the detailed SPSS-style output for a Wilcoxon signed-rank test, with sections controlled by the boolean parameters passed to summary.wilcoxon_test. Sections include the rank table and the test statistics table with effect size interpretation.

Usage

## S3 method for class 'summary.wilcoxon_test'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

See Also

wilcoxon_test for the main analysis, summary.wilcoxon_test for summary options.

Examples

result <- wilcoxon_test(survey_data, x = trust_government, y = trust_media)
summary(result)                # all sections
summary(result, ranks = FALSE) # hide rank table

Print t-test results (compact)

Description

Compact print method for objects of class "t_test". Shows a one-line summary per variable with test statistic, p-value, effect size, and sample size.

For the full detailed output, use summary().

Usage

## S3 method for class 't_test'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- t_test(survey_data, life_satisfaction, group = gender)
result              # compact one-line overview
summary(result)     # full detailed output

Print Tukey HSD test results (compact)

Description

Compact print method for objects of class "tukey_test". Shows one line per variable (or factor, or group combination) with the number of pairwise comparisons and how many are significant at the .05 level.

For the full comparison tables (mean differences, confidence intervals, adjusted p-values), use summary().

Usage

## S3 method for class 'tukey_test'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- oneway_anova(survey_data, life_satisfaction,
                       group = education) |> tukey_test()
result              # compact overview
summary(result)     # full comparison tables

Print method for w_modus objects

Description

Print method for w_modus objects

Usage

## S3 method for class 'w_modus'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Print method for w_quantile objects

Description

Prints a formatted summary of weighted quantile calculations, including quantiles, sample sizes, and weights (if applicable). For grouped data, results are displayed by group.

Usage

## S3 method for class 'w_quantile'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Print Wilcoxon signed-rank test results (compact)

Description

Compact print method for objects of class "wilcoxon_test". Shows a one-line summary per comparison with the Z statistic, p-value, effect size r, and sample size.

For the full detailed output (rank tables, test statistics), use summary().

Usage

## S3 method for class 'wilcoxon_test'
print(x, digits = 3, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

result <- wilcoxon_test(survey_data, x = trust_government, y = trust_media)
result              # compact one-line overview
summary(result)     # full detailed output

Read SPSS Portable Data with Tagged Missing Values

Description

Reads an SPSS portable .por file and preserves user-defined missing values as tagged NAs. This is the portable format equivalent of read_spss() for .sav files.

Usage

read_por(path, tag_na = TRUE, verbose = FALSE)

Arguments