reliability() calculates Cronbach's Alpha and detailed item statistics
to evaluate whether your survey items form a reliable scale. This is the R
equivalent of SPSS's RELIABILITY /MODEL=ALPHA /STATISTICS=DESCRIPTIVE CORR
/SUMMARY=TOTAL.
For example, if you have 3 items measuring "trust", reliability analysis tells you whether these items consistently measure the same concept.
Arguments
- data
Your survey data (a data frame or tibble)
- ...
The items to analyze. Use bare column names separated by commas, or tidyselect helpers like
starts_with("trust").- weights
Optional survey weights for population-representative results
- na.rm
Remove missing values before calculating? (Default: TRUE). Uses listwise deletion (only complete cases across all items).
Value
A reliability result object containing:
- alpha
Cronbach's Alpha (unstandardized)
- alpha_standardized
Cronbach's Alpha based on standardized items
- n_items
Number of items in the scale
- item_statistics
Mean, SD, and N for each item
- item_total
Corrected Item-Total Correlation and Alpha if Item Deleted
- inter_item_cor
Inter-item correlation matrix
- n
Sample size (listwise)
Use summary() for the full SPSS-style output with toggleable sections.
Details
Understanding the Results
Cronbach's Alpha tells you how internally consistent your scale is:
Alpha > 0.90: Excellent reliability
Alpha 0.80 - 0.90: Good reliability
Alpha 0.70 - 0.80: Acceptable reliability
Alpha 0.60 - 0.70: Questionable reliability
Alpha < 0.60: Poor reliability - reconsider items
Item-Total Correlation shows how well each item fits the scale:
Values > 0.40: Item fits well
Values 0.20 - 0.40: Item may need review
Values < 0.20: Consider removing the item
Alpha if Item Deleted shows what happens if you remove an item:
If alpha increases when removing an item, that item hurts reliability
If alpha decreases, the item contributes to the scale
When to Use This
Run reliability() before creating scale scores with
row_means:
Select your items
Check reliability
If acceptable (alpha > .70), create the index
If not, review items and consider removing problematic ones
See also
row_means for creating mean indices after checking reliability.
pearson_cor for bivariate correlations.
summary.reliability for detailed output with toggleable sections.
Other scale:
efa(),
pomps(),
row_count(),
row_means(),
row_sums()
Examples
library(dplyr)
data(survey_data)
# Check reliability of trust items
reliability(survey_data, trust_government, trust_media, trust_science)
#> Reliability Analysis: 3 items
#> Cronbach's Alpha = 0.047 (Poor), N = 2135
# With survey weights
reliability(survey_data, trust_government, trust_media, trust_science,
weights = sampling_weight)
#> Reliability Analysis: 3 items [Weighted]
#> Cronbach's Alpha = 0.052 (Poor), N = 2150
# Using tidyselect helpers
reliability(survey_data, starts_with("trust"))
#> Reliability Analysis: 3 items
#> Cronbach's Alpha = 0.047 (Poor), N = 2135
# Grouped by region
survey_data %>%
group_by(region) %>%
reliability(trust_government, trust_media, trust_science)
#> [region = 1]
#> Reliability Analysis: 3 items
#> Cronbach's Alpha = 0.037 (Poor), N = 422
#> [region = 2]
#> Reliability Analysis: 3 items
#> Cronbach's Alpha = 0.050 (Poor), N = 1713
# --- Three-layer output ---
result <- reliability(survey_data, trust_government, trust_media, trust_science)
result # compact one-line overview
#> Reliability Analysis: 3 items
#> Cronbach's Alpha = 0.047 (Poor), N = 2135
summary(result) # full detailed output with all sections
#>
#> Reliability Analysis Results
#> ----------------------------
#> - Items: trust_government, trust_media, trust_science
#> - N of Items: 3
#>
#> Reliability Statistics
#> ----------------------------------------
#> Cronbach's Alpha: 0.047
#> Alpha (standardized): 0.048
#> N of Items: 3
#> N (listwise): 2135
#>
#> Item Statistics
#> ----------------------------------------
#> item mean sd n
#> trust_government 2.621 1.162 2135
#> trust_media 2.430 1.156 2135
#> trust_science 3.624 1.034 2135
#>
#> Inter-Item Correlation Matrix:
#> ------------------------------
#> trust_government trust_media trust_science
#> trust_government 1.000 0.014 0.020
#> trust_media 0.014 1.000 0.015
#> trust_science 0.020 0.015 1.000
#> ------------------------------
#>
#> Item-Total Statistics
#> ----------------------------------------
#> item scale_mean_deleted scale_var_deleted corrected_r
#> trust_government 6.05 2.440 0.024
#> trust_media 6.25 2.467 0.020
#> trust_science 5.05 2.723 0.025
#> alpha_deleted
#> 0.029
#> 0.040
#> 0.027
summary(result, inter_item_correlations = FALSE) # hide correlations
#>
#> Reliability Analysis Results
#> ----------------------------
#> - Items: trust_government, trust_media, trust_science
#> - N of Items: 3
#>
#> Reliability Statistics
#> ----------------------------------------
#> Cronbach's Alpha: 0.047
#> Alpha (standardized): 0.048
#> N of Items: 3
#> N (listwise): 2135
#>
#> Item Statistics
#> ----------------------------------------
#> item mean sd n
#> trust_government 2.621 1.162 2135
#> trust_media 2.430 1.156 2135
#> trust_science 3.624 1.034 2135
#>
#> Item-Total Statistics
#> ----------------------------------------
#> item scale_mean_deleted scale_var_deleted corrected_r
#> trust_government 6.05 2.440 0.024
#> trust_media 6.25 2.467 0.020
#> trust_science 5.05 2.723 0.025
#> alpha_deleted
#> 0.029
#> 0.040
#> 0.027