Calculates the mean, standard deviation, and variance-covariance matrix for
each term in an individual-level data set. This is useful when a user wants
summary EPA information for a subset of respondents, for example, when comparing
cultural meaning across groups. In this case, a user would first create the
desired individual data subsets using the epa_subset() function, then pass the
resulting data frames to this function to calculate relevant summary statistics.
Value
a summary dataset with one row per term. Includes the evaluation, potency, and activity mean, standard deviation, and variance-covariance matrix entries for each term/component combination. Values are rounded to the nearest .01.
Examples
epa_summary(dplyr::filter(epa_subset(datatype = "individual", dataset = "usfullsurveyor2015"),
gender == "Male"))
#> # A tibble: 2,403 × 20
#> term component n_E n_P n_A mean_E mean_P mean_A sd_E sd_P sd_A
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 abandon behavior 31 31 31 -3.47 0.37 -0.22 1 3.14 2.46
#> 2 abandoned modifier 22 22 22 -3.15 0.42 -1.19 1.14 2.92 1.92
#> 3 abduct behavior 48 48 48 -3.58 1.79 1.65 1.26 2.23 2.22
#> 4 abet behavior 18 18 18 -0.16 0.22 0.08 1.01 1.24 0.92
#> 5 abhor behavior 6 6 6 -2.75 0.76 -0.28 1.36 2.81 2.39
#> 6 able_bodi… modifier 31 31 31 3 2.81 1.22 1.09 1.34 1.81
#> 7 abort behavior 26 26 26 -2.18 0.72 -0.21 1.83 2.94 2.61
#> 8 abortioni… identity 39 39 39 -0.86 1.11 0.03 1.98 2.43 1.98
#> 9 absent_mi… modifier 34 34 34 -1.72 -1.55 -1.47 1.5 1.78 1.92
#> 10 abuse behavior 32 32 32 -3.64 -0.54 1.73 1.22 3.12 1.85
#> # ℹ 2,393 more rows
#> # ℹ 9 more variables: cov_EE <dbl>, cov_EP <dbl>, cov_EA <dbl>, cov_PE <dbl>,
#> # cov_PP <dbl>, cov_PA <dbl>, cov_AE <dbl>, cov_AP <dbl>, cov_AA <dbl>