Distils a whole survey's sampling precision into a few bullet points – the narrative "Data info" paragraph from the report appendix, generated automatically. It scans the dataset, assesses the numeric (point-scale) and categorical (proportion) variables, and reports the typical sampling margins and an overall reliability verdict.
Arguments
- data
A data frame. If omitted, the session default (
use_dataset()) is used.- ...
Optional columns to restrict the assessment to (tidyselect). If omitted, all suitable columns are used: every numeric column, and every categorical column that is not free text or an identifier.
- z
Confidence multiplier for the margins of error. Default
1.96(95%).
Value
An object of class ezrsurvey_precision (a list with n, the
per-variable table, overall_rse, rating and the bullets). Printing it
shows the bullet points.
Details
Rather than make you choose which variables to quote precision for, this picks
them: numeric columns become point estimates (margins in rating points) and
categorical columns become proportions (margins in percentage points), with
free-text and id-like columns skipped. It then reports the typical and
worst margins across each group, plus an overall verdict from the median
relative standard error via rse_rating(). For proportions it also gives the
worst-case +/- percentage-point margin at a 50/50 split, which is the widest
any percentage in the study can be. See vignette("ezrsurvey") for the
rationale behind the relative-standard-error reliability bands.
See also
Other diagnostics:
diagnose(),
margin_of_error(),
rse(),
rse_rating(),
se_mean(),
se_prop()
Examples
precision_summary(podracing_survey)
#> Survey precision summary
#> - Based on 1,000 total responses (per-question base of 776 to 1,000).
#> - Point ratings carry a sampling margin of about +/-0.40 points (up to +/-0.69) at 95% confidence.
#> - Percentages carry a sampling margin of about +/-3.0 percentage points (worst case +/-3.1 at a 50/50 split).
#> - Overall relative standard error of about 4.2% -- high precision.
#>