survey_example: Example Survey Data for Social Science Research

In corrselect: Correlation-Based and Model-Based Predictor Pruning

Example Survey Data for Social Science Research

Description

A simulated questionnaire dataset with 30 Likert-scale items measuring three latent constructs (satisfaction, engagement, loyalty), plus demographic variables and an overall satisfaction score.

Usage

survey_example

Format

A data frame with 200 rows and 35 variables:

respondent_id

Integer. Unique respondent identifier

age

Integer. Respondent age (18-75 years)

gender

Factor. Gender (Male, Female, Other)

education

Ordered factor. Education level (High School, Bachelor, Master, PhD)

overall_satisfaction

Integer. Overall satisfaction score (0-100)

satisfaction_1, satisfaction_2, satisfaction_3, satisfaction_4, satisfaction_5, satisfaction_6, satisfaction_7, satisfaction_8, satisfaction_9, satisfaction_10

Ordered factor. Satisfaction items (1-7 Likert scale)

engagement_1, engagement_2, engagement_3, engagement_4, engagement_5, engagement_6, engagement_7, engagement_8, engagement_9, engagement_10

Ordered factor. Engagement items (1-7 Likert scale)

loyalty_1, loyalty_2, loyalty_3, loyalty_4, loyalty_5, loyalty_6, loyalty_7, loyalty_8, loyalty_9, loyalty_10

Ordered factor. Loyalty items (1-7 Likert scale)

Details

This dataset represents a common scenario in survey research: multiple items measuring similar constructs lead to redundancy and multicollinearity. Items within each construct are correlated (satisfaction, engagement, loyalty), and the constructs themselves are inter-correlated.

Use case: Demonstrating assocSelect() for identifying redundant questionnaire items in mixed-type data (ordered factors + numeric variables).

Source

Simulated data based on typical customer satisfaction survey structures

See Also

assocSelect(), corrPrune()

Examples

data(survey_example)

# This dataset has mixed types: numeric (age, overall_satisfaction),
# factors (gender, education), and ordered factors (Likert items)
str(survey_example[, 1:10])


# Use assocSelect() for mixed-type data pruning
# This may take a few seconds with 34 variables
pruned <- assocSelect(survey_example[, -1],  # Exclude respondent_id
                      threshold = 0.8,
                      method_ord_ord = "spearman")
length(pruned@subset_list[[1]])  # Size of the best (top-ranked) subset

corrselect documentation built on July 19, 2026, 1:06 a.m.