1 Presentation
This is the data preparation code for the project “Socioeconomic and Gender Disparities: A Multi-Country Study.” The prepared dataset is SOGEDI_dataset_V1.sav
In this repository, data processing and cleaning exclude countries with insufficient sample sizes for robust statistical analysis, retaining only observations from Argentina, Chile, Colombia, Spain, and Mexico. However, for anyone wishing to use all cases and countries from the original dataset, it can be accessed at the following link.
2 Libraries
First, we load the necessary libraries. In this case, we use pacman::p_load to load and call libraries in one move.
3 Data
We load the database from the the Github repository project.
sogedi_db <- haven::read_sav(url("https://github.com/sogedi-project/sogedi-data/raw/refs/heads/main/input/data/original/SOGEDI_dataset_V1.sav"), user_na = T)
glimpse(sogedi_db)Rows: 4,386
Columns: 283
$ ID <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,…
$ StartDate <dttm> 2024-04-28 11:11:20, 2024-04-28 11:12:34,…
$ EndDate <dttm> 2024-04-28 11:30:12, 2024-04-28 11:31:15,…
$ IPAddress <chr> "90.167.243.1", "83.58.124.179", "79.152.1…
$ Duration__in_seconds <dbl> 1132, 1120, 1192, 1410, 1328, 645, 933, 88…
$ RecordedDate <dttm> 2024-04-28 11:30:12, 2024-04-28 11:31:16,…
$ ResponseId <chr> "R_1eqka09S3bZXYTp", "R_42oDc55cfSucfrX", …
$ LocationLatitude <chr> "41.6362", "41.3891", "41.4287", "41.5453"…
$ LocationLongitude <chr> "-4.7435", "2.1606", "2.2164", "2.4414", "…
$ aten_check_1 <dbl+lbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,…
$ aten_check_2 <dbl+lbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,…
$ aten_check_3 <dbl+lbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,…
$ time_class_1_First_Click <dbl> 2.695, 12.941, 11.788, 13.477, 228.254, 8.…
$ time_class_1_Last_Click <dbl> 55.801, 60.736, 38.827, 50.411, 249.534, 2…
$ time_class_1_Page_Submit <dbl> 57.095, 62.226, 40.360, 51.672, 250.460, 2…
$ time_class_1_Click_Count <dbl> 20, 6, 6, 6, 12, 9, 6, 6, 6, 7, 11, 16, 6,…
$ eco_in_1 <dbl+lbl> 6, 6, 7, 6, 6, 4, 4, 3, 6, 3, 7, 7, 5,…
$ eco_in_2 <dbl+lbl> 6, 6, 7, 6, 6, 4, 5, 4, 3, 4, 1, 6, 5,…
$ eco_in_3 <dbl+lbl> 7, 6, 7, 6, 6, 4, 2, 3, 5, 3, 5, 4, 6,…
$ jus_ine <dbl+lbl> 1, 2, 1, 1, 2, 5, 1, 1, 2, 1, 2, 5, 3,…
$ co_eco <dbl+lbl> 7, 7, 6, 4, 5, 3, 6, 6, 3, 2, 1, 4, 5,…
$ time_class_2_First_Click <dbl> 4.004, 11.257, 7.633, 9.522, 6.466, 9.334,…
$ time_class_2_Last_Click <dbl> 88.112, 83.882, 68.033, 82.459, 61.386, 32…
$ time_class_2_Page_Submit <dbl> 89.238, 85.347, 69.004, 83.534, 62.577, 33…
$ time_class_2_Click_Count <dbl> 34, 12, 13, 16, 20, 18, 17, 13, 17, 12, 14…
$ pp_pw_1 <dbl+lbl> 7, 4, 6, 2, 5, 5, 3, 3, 2, 5, 7, 5, 5,…
$ pp_pw_2 <dbl+lbl> 7, 5, 6, 3, 6, 5, 5, 7, 2, 5, 2, 5, 5,…
$ pp_pw_3 <dbl+lbl> 7, 6, 7, 2, 5, 3, 3, 5, 2, 4, 2, 4, 5,…
$ pp_pw_4 <dbl+lbl> 7, 4, 4, 1, 5, 5, 3, 5, 2, 5, 4, 3, 5,…
$ cc_pw_1 <dbl+lbl> 5, 4, 6, 3, 6, 4, 5, 6, 5, 4, 4, 6, 6,…
$ cc_pw_2 <dbl+lbl> 4, 2, 4, 2, 5, 4, 4, 4, 2, 4, 2, 4, 4,…
$ cc_pw_3 <dbl+lbl> 4, 3, 6, 4, 6, 4, 4, 4, 2, 5, 7, 5, 6,…
$ cc_pw_4 <dbl+lbl> 3, 5, 5, 3, 6, 4, 5, 5, 4, 4, 7, 6, 6,…
$ hc_pw_1 <dbl+lbl> 1, 1, 1, 1, 1, 4, 2, 2, 1, 3, 1, 2, 4,…
$ hc_pw_2 <dbl+lbl> 2, 1, 2, 3, 3, 4, 2, 3, 1, 6, 1, 2, 5,…
$ hc_pw_3 <dbl+lbl> 1, 1, 4, 2, 2, 4, 1, 2, 1, 3, 1, 2, 2,…
$ hc_pw_4 <dbl+lbl> 2, 2, 2, 1, 2, 3, 4, 2, 1, 4, 1, 2, 5,…
$ time_class_3_First_Click <dbl> 1.784, 8.404, 5.983, 5.768, 68.795, 4.382,…
$ time_class_3_Last_Click <dbl> 53.633, 61.879, 60.551, 189.575, 121.331, …
$ time_class_3_Page_Submit <dbl> 54.156, 63.590, 61.758, 190.098, 122.122, …
$ time_class_3_Click_Count <dbl> 30, 12, 14, 13, 14, 19, 13, 12, 13, 13, 13…
$ pp_pm_1 <dbl+lbl> 6, 5, 6, 4, 5, 5, 3, 6, 2, 5, 7, 5, 6,…
$ pp_pm_2 <dbl+lbl> 7, 5, 7, 2, 6, 3, 2, 6, 2, 5, 5, 5, 4,…
$ pp_pm_3 <dbl+lbl> 7, 6, 6, 3, 5, 3, 3, 6, 2, 5, 7, 3, 5,…
$ pp_pm_4 <dbl+lbl> 7, 4, 6, 3, 5, 3, 3, 5, 2, 4, 7, 4, 6,…
$ cc_pm_1 <dbl+lbl> 7, 4, 4, 3, 5, 4, 5, 3, 5, 3, 2, 5, 5,…
$ cc_pm_2 <dbl+lbl> 4, 2, 1, 1, 4, 4, 3, 4, 2, 2, 2, 4, 4,…
$ cc_pm_3 <dbl+lbl> 4, 3, 2, 3, 4, 4, 4, 4, 2, 3, 1, 4, 6,…
$ cc_pm_4 <dbl+lbl> 3, 5, 5, 2, 4, 5, 4, 4, 2, 3, 2, 6, 3,…
$ hc_pm_1 <dbl+lbl> 3, 1, 4, 3, 3, 3, 2, 4, 4, 4, 7, 3, 5,…
$ hc_pm_2 <dbl+lbl> 3, 1, 5, 3, 3, 3, 2, 3, 2, 4, 7, 3, 5,…
$ hc_pm_3 <dbl+lbl> 2, 1, 3, 1, 2, 4, 2, 3, 2, 5, 7, 3, 6,…
$ hc_pm_4 <dbl+lbl> 3, 2, 4, 2, 5, 4, 2, 4, 1, 5, 7, 3, 5,…
$ time_gender_1_First_Click <dbl> 1.829, 12.693, 10.808, 18.682, 8.264, 6.60…
$ time_gender_1_Last_Click <dbl> 126.481, 112.822, 147.886, 102.824, 82.228…
$ time_gender_1_Page_Submit <dbl> 127.601, 114.335, 149.828, 104.069, 83.877…
$ time_gender_1_Click_Count <dbl> 49, 20, 28, 23, 20, 31, 25, 24, 20, 23, 27…
$ gen_in_1 <dbl+lbl> 6, 7, 6, 7, 7, 3, 7, 7, 6, 5, 4, 6, 7,…
$ gen_in_2 <dbl+lbl> 6, 7, 6, 5, 7, 3, 5, 6, 1, 6, 7, 7, 7,…
$ gen_in_3 <dbl+lbl> 5, 7, 5, 7, 4, 3, 4, 7, 6, 6, 7, 5, 6,…
$ gen_in_4 <dbl+lbl> 3, 6, 5, 6, 6, 3, 5, 5, 5, 6, 7, 5, 3,…
$ gen_in_5 <dbl+lbl> 4, 6, 3, 5, 7, 3, 7, 4, 6, 5, 6, 5, 3,…
$ gen_in_6 <dbl+lbl> 6, 7, 5, 6, 4, 2, 5, 7, 6, 6, 7, 5, 7,…
$ ps_m_1 <dbl+lbl> 7, 2, 4, 1, 3, 3, 3, 4, 1, 4, 1, 7, 6,…
$ ps_m_2 <dbl+lbl> 6, 1, 2, 5, 1, 4, 1, 4, 1, 1, 1, 5, 4,…
$ ps_m_3 <dbl+lbl> 6, 2, 4, 3, 4, 2, 4, 4, 1, 4, 7, 3, 6,…
$ hs_m_1 <dbl+lbl> 1, 1, 2, 1, 2, 3, 2, 2, 1, 3, 1, 2, 4,…
$ hs_m_2 <dbl+lbl> 1, 1, 5, 1, 3, 3, 1, 2, 1, 2, 1, 2, 5,…
$ hs_m_3 <dbl+lbl> 1, 2, 1, 1, 2, 4, 1, 2, 1, 3, 1, 3, 5,…
$ shif_1 <dbl+lbl> 1, 1, 2, 2, 2, 6, 1, 1, 1, 2, 1, 5, 5,…
$ shif_2 <dbl+lbl> 1, 1, 2, 1, 2, 5, 1, 1, 1, 2, 1, 4, 2,…
$ shif_3 <dbl+lbl> 1, 1, 1, 4, 2, 3, 1, 1, 1, 2, 3, 5, 3,…
$ femi <dbl+lbl> 7, 7, 3, 5, 5, 1, 7, 5, 6, 2, 4, 2, 1,…
$ co_gen <dbl+lbl> 7, 7, 3, 4, 5, 3, 6, 5, 2, 2, 1, 4, 4,…
$ jus_gen <dbl+lbl> 1, 2, 1, 2, 3, 3, 3, 1, 1, 1, 1, 5, 3,…
$ gen_compe <dbl+lbl> 4, 6, 5, 5, 4, 4, 1, 4, 4, 4, 1, 5, 5,…
$ time_contac_1_First_Click <dbl> 1.842, 12.194, 9.584, 4.779, 10.964, 8.097…
$ time_contac_1_Last_Click <dbl> 138.959, 125.608, 145.143, 147.327, 288.91…
$ time_contac_1_Page_Submit <dbl> 139.507, 126.906, 146.760, 148.154, 289.52…
$ time_contac_1_Click_Count <dbl> 59, 22, 26, 29, 36, 24, 39, 24, 28, 26, 27…
$ ge_ra_wo <dbl> 70, 70, 60, 60, 40, 20, 50, 20, 27, 60, 85…
$ ge_ra_me <dbl> 30, 30, 40, 40, 60, 80, 50, 80, 73, 40, 15…
$ quan_pw <dbl+lbl> 1, 4, 5, 3, 5, 3, 3, 2, 1, 2, 1, 3, 2,…
$ quan_pm <dbl+lbl> 1, 4, 5, 3, 5, 4, 3, 3, 1, 2, 1, 3, 2,…
$ quan_rw <dbl+lbl> 1, 5, 5, 4, 7, 3, 2, 2, 7, 1, 5, 3, 4,…
$ quan_rm <dbl+lbl> 1, 5, 5, 4, 7, 4, 2, 2, 7, 1, 5, 2, 4,…
$ fri_pw <dbl+lbl> 1, 1, 2, 3, 3, 4, 2, 1, 1, 3, 1, 2, 1,…
$ fri_pm <dbl+lbl> 1, 1, 1, 2, 3, 4, 2, 1, 1, 3, 1, 1, 1,…
$ fri_rw <dbl+lbl> 2, 4, 6, 4, 6, 4, 1, 1, 5, 1, 6, 4, 1,…
$ fri_rm <dbl+lbl> 2, 5, 6, 3, 6, 4, 1, 1, 5, 1, 7, 4, 1,…
$ qual_pw <dbl+lbl> 4, 5, 4, 4, 6, 4, 3, 3, 2, 4, 4, 3, 2,…
$ qual_pm <dbl+lbl> 4, 5, 3, 4, 4, 4, 3, 3, 2, 4, 4, 3, 2,…
$ qual_rw <dbl+lbl> 2, 5, 6, 3, 5, 4, 3, 4, 4, 4, 7, 4, 3,…
$ qual_rm <dbl+lbl> 2, 5, 5, 3, 5, 4, 3, 4, 4, 4, 7, 4, 3,…
$ mobi_up_1 <dbl+lbl> 4, 3, 3, 5, 2, 3, 1, 3, 1, 4, 5, 5, 6,…
$ mobi_up_2 <dbl+lbl> 4, 4, 5, 3, 3, 4, 1, 3, 1, 2, 4, 5, 5,…
$ mobi_up_3 <dbl+lbl> 5, 3, 1, 6, 2, 4, 1, 4, 1, 3, 3, 5, 5,…
$ mobi_down_1 <dbl+lbl> 5, 6, 6, 6, 5, 4, 5, 5, 6, 4, 5, 3, 2,…
$ mobi_down_2 <dbl+lbl> 5, 4, 5, 2, 4, 3, 4, 4, 5, 4, 1, 3, 2,…
$ mobi_down_3 <dbl+lbl> 4, 5, 3, 3, 5, 4, 3, 4, 6, 4, 1, 3, 2,…
$ time_stere_pw_1_First_Click <dbl> 1.813, 14.065, 21.732, 17.635, 11.730, 5.6…
$ time_stere_pw_1_Last_Click <dbl> 164.968, 172.405, 183.196, 163.738, 148.81…
$ time_stere_pw_1_Page_Submit <dbl> 165.908, 174.555, 185.603, 165.040, 149.59…
$ time_stere_pw_1_Click_Count <dbl> 106, 41, 55, 44, 48, 49, 53, 38, 45, 37, 4…
$ condi_gender <dbl+lbl> 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1,…
$ condi_class <dbl+lbl> 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0,…
$ mor_1 <dbl+lbl> 1, 4, 3, 6, 3, 4, 2, 3, 3, 5, 5, 2, 5,…
$ mor_2 <dbl+lbl> 2, 3, 4, 5, 2, 5, 3, 4, 4, 4, 5, 3, 4,…
$ mor_3 <dbl+lbl> 2, 3, 3, 4, 3, 4, 2, 3, 4, 3, 6, 3, 2,…
$ inm_1 <dbl+lbl> 7, 5, 6, 3, 6, 4, 2, 3, 3, 4, 1, 7, 4,…
$ inm_2 <dbl+lbl> 6, 4, 4, 2, 3, 3, 2, 3, 5, 2, 1, 6, 3,…
$ inm_3 <dbl+lbl> 5, 5, 4, 1, 6, 5, 2, 4, 4, 4, 2, 5, 5,…
$ war_1 <dbl+lbl> 4, 4, 2, 4, 5, 4, 5, 4, 5, 5, 5, 3, 5,…
$ war_2 <dbl+lbl> 2, 3, 4, 5, 4, 3, 3, 4, 4, 4, 5, 3, 4,…
$ war_3 <dbl+lbl> 4, 4, 2, 6, 5, 3, 5, 5, 4, 5, 5, 3, 4,…
$ com_1 <dbl+lbl> 7, 6, 4, 5, 5, 5, 3, 4, 4, 3, 6, 5, 3,…
$ com_2 <dbl+lbl> 6, 6, 5, 5, 5, 5, 3, 5, 5, 4, 6, 5, 2,…
$ com_3 <dbl+lbl> 5, 5, 3, 5, 5, 6, 3, 4, 5, 5, 6, 4, 3,…
$ ph_1 <dbl+lbl> 4, 1, 2, 1, 6, 2, 1, 1, 5, 1, 1, 6, 3,…
$ ph_2 <dbl+lbl> 4, 1, 6, 1, 6, 2, 1, 1, 5, 4, 1, 5, 4,…
$ ah_1 <dbl+lbl> 2, 2, 1, 1, 5, 2, 2, 1, 1, 1, 1, 1, 2,…
$ ah_2 <dbl+lbl> 2, 1, 2, 1, 5, 2, 2, 1, 1, 1, 1, 1, 1,…
$ pf_1 <dbl+lbl> 4, 4, 5, 5, 3, 4, 2, 7, 3, 5, 7, 5, 5,…
$ pf_2 <dbl+lbl> 1, 5, 1, 4, 3, 5, 5, 2, 5, 2, 4, 3, 4,…
$ af_1 <dbl+lbl> 1, 4, 1, 5, 3, 3, 3, 7, 2, 2, 7, 2, 3,…
$ af_2 <dbl+lbl> 1, 3, 2, 7, 4, 4, 2, 7, 4, 5, 4, 4, 5,…
$ ad_1 <dbl+lbl> 1, 4, 2, 5, 3, 5, 1, 5, 2, 3, 2, 3, 4,…
$ ad_2 <dbl+lbl> 4, 4, 5, 6, 2, 5, 3, 7, 2, 6, 7, 4, 6,…
$ co_1 <dbl+lbl> 2, 1, 1, 1, 6, 2, 4, 1, 5, 1, 1, 1, 3,…
$ co_2 <dbl+lbl> 2, 2, 2, 1, 6, 2, 2, 1, 4, 1, 1, 3, 4,…
$ en_1 <dbl+lbl> 1, 1, 1, 2, 2, 2, 4, 1, 3, 1, 1, 1, 1,…
$ en_2 <dbl+lbl> 1, 1, 1, 1, 2, 2, 4, 1, 4, 1, 1, 1, 1,…
$ pi_1 <dbl+lbl> 1, 1, 6, 4, 5, 1, 2, 6, 4, 3, 6, 6, 6,…
$ pi_2 <dbl+lbl> 1, 1, 6, 3, 1, 2, 1, 7, 2, 4, 7, 5, 5,…
$ sk_1 <dbl+lbl> 7, 6, 6, 7, 6, 2, 7, 6, 4, 5, 7, 5, 3,…
$ sk_2 <dbl+lbl> 7, 7, 6, 5, 7, 2, 7, 7, 5, 6, 7, 3, 5,…
$ sk_3 <dbl+lbl> 7, 7, 7, 7, 7, 2, 7, 5, 4, 6, 7, 7, 5,…
$ ex_po_1 <dbl+lbl> NA, NA, 5, 7, NA, NA, NA, 7, NA, 7…
$ ex_po_2 <dbl+lbl> NA, NA, 6, 5, NA, NA, NA, 6, NA, 7…
$ in_po_1 <dbl+lbl> NA, NA, 4, 2, NA, NA, NA, 4, NA, 4…
$ in_po_2 <dbl+lbl> NA, NA, 2, 1, NA, NA, NA, 5, NA, 5…
$ ex_we_1 <dbl+lbl> 7, 7, NA, NA, 7, 4, 7, NA, 6, NA…
$ ex_we_2 <dbl+lbl> 7, 7, NA, NA, 7, 4, 7, NA, 6, NA…
$ in_we_1 <dbl+lbl> 7, 5, NA, NA, 3, 5, 3, NA, 2, NA…
$ in_we_2 <dbl+lbl> 3, 5, NA, NA, 3, 5, 2, NA, 1, NA…
$ carin_control_1 <dbl+lbl> NA, NA, 4, 7, NA, NA, NA, 2, NA, 4…
$ carin_control_2 <dbl+lbl> NA, NA, 3, 1, NA, NA, NA, 2, NA, 4…
$ carin_attitude_1 <dbl+lbl> NA, NA, 5, 1, NA, NA, NA, 4, NA, 2…
$ carin_attitude_2 <dbl+lbl> NA, NA, 7, 1, NA, NA, NA, 2, NA, 3…
$ carin_reciprocity_1 <dbl+lbl> NA, NA, 3, 4, NA, NA, NA, 3, NA, 3…
$ carin_reciprocity_2 <dbl+lbl> NA, NA, 5, 1, NA, NA, NA, 2, NA, 4…
$ carin_identity_1 <dbl+lbl> NA, NA, 3, 1, NA, NA, NA, 1, NA, 1…
$ carin_identity_2 <dbl+lbl> NA, NA, 1, 2, NA, NA, NA, 5, NA, 1…
$ carin_need_1 <dbl+lbl> NA, NA, 6, 1, NA, NA, NA, 1, NA, 5…
$ carin_need_2 <dbl+lbl> NA, NA, 5, 1, NA, NA, NA, 1, NA, 5…
$ greedy_1 <dbl+lbl> 7, 6, NA, NA, 7, 2, 3, NA, 7, NA…
$ greedy_2 <dbl+lbl> 7, 6, NA, NA, 7, 3, 4, NA, 6, NA…
$ greedy_3 <dbl+lbl> 7, 6, NA, NA, 7, 3, 4, NA, 5, NA…
$ punish_1 <dbl+lbl> 7, 7, NA, NA, 7, 2, 6, NA, 7, NA…
$ punish_2 <dbl+lbl> 7, 7, NA, NA, 7, 2, 7, NA, 7, NA…
$ punish_3 <dbl+lbl> 7, 7, NA, NA, 7, 2, 7, NA, 7, NA…
$ time_dh_1_First_Click <dbl> 1.898, 22.881, 20.927, 11.899, 12.041, 12.…
$ time_dh_1_Last_Click <dbl> 48.775, 34.978, 31.391, 25.883, 21.264, 43…
$ time_dh_1_Page_Submit <dbl> 49.622, 37.663, 32.915, 27.970, 22.766, 44…
$ time_dh_1_Click_Count <dbl> 26, 4, 5, 6, 4, 9, 5, 5, 5, 4, 4, 8, 4, 4,…
$ asc_pw <dbl> 50, 61, 69, 53, 80, 51, 50, 73, 51, 65, 51…
$ asc_pm <dbl> 50, 61, 61, 54, 70, 47, 51, 39, 51, 65, 30…
$ asc_rw <dbl> 50, 76, 40, 48, 80, 65, 51, 73, 51, 15, 80…
$ asc_rm <dbl> 50, 75, 61, 51, 70, 64, 51, 58, 51, 15, 70…
$ time_sexu_First_Click <dbl> 1.181, 9.759, 7.360, 8.072, 8.461, 2.646, …
$ time_sexu_Last_Click <dbl> 71.999, 79.570, 76.440, 46.009, 56.913, 35…
$ time_sexu_Page_Submit <dbl> 72.541, 81.801, 77.506, 47.515, 57.619, 36…
$ time_sexu_Click_Count <dbl> 51, 13, 17, 13, 13, 19, 20, 14, 13, 16, 15…
$ wel_abu_1 <dbl+lbl> 1, 1, 3, 1, 2, 2, 3, 4, 1, 4, 2, 3, 3,…
$ wel_abu_2 <dbl+lbl> 1, 1, 2, 1, 2, 2, 3, 2, 1, 2, 2, 4, 3,…
$ wel_pa_1 <dbl+lbl> 7, 2, 7, 1, 6, 2, 3, 6, 5, 7, 7, 5, 6,…
$ wel_pa_2 <dbl+lbl> 7, 2, 7, 1, 6, 2, 5, 6, 4, 6, 7, 7, 5,…
$ wel_ho_1 <dbl+lbl> 1, 1, 1, 1, 2, 2, 2, 3, 1, 5, 1, 1, 2,…
$ wel_ho_2 <dbl+lbl> 1, 1, 1, 1, 2, 2, 4, 4, 1, 6, 1, 4, 2,…
$ pro_pw <dbl+lbl> 4, 2, 3, 1, 2, 3, 3, 2, 1, 2, 1, 2, 5,…
$ pro_rw <dbl+lbl> 4, 2, 6, 1, 5, 4, 3, 4, 1, 6, 7, 5, 6,…
$ ris_pw <dbl+lbl> 6, 2, 6, 1, 6, 4, 3, 3, 4, 4, 7, 6, 6,…
$ ris_rw <dbl+lbl> 3, 1, 5, 1, 4, 4, 3, 3, 5, 5, 5, 4, 2,…
$ pre_pw <dbl+lbl> 6, 3, 6, 3, 6, 4, 4, 3, 5, 5, 7, 4, 6,…
$ pre_rw <dbl+lbl> 3, 1, 4, 3, 2, 4, 2, 3, 3, 2, 2, 5, 1,…
$ time_poli_1_First_Click <dbl> 1.453, 15.591, 12.917, 12.337, 40.588, 5.5…
$ time_poli_1_Last_Click <dbl> 106.685, 99.407, 95.733, 90.936, 112.949, …
$ time_poli_1_Page_Submit <dbl> 107.394, 101.436, 96.779, 92.410, 114.003,…
$ time_poli_1_Click_Count <dbl> 56, 15, 16, 17, 16, 16, 16, 15, 16, 16, 16…
$ redi_1 <dbl+lbl> 7, 7, 7, 5, 7, 4, 7, 7, 6, 7, 6, 5, 6,…
$ redi_2 <dbl+lbl> 7, 7, 6, 1, 7, 3, 7, 7, 7, 7, 1, 6, 7,…
$ effec_pw_1 <dbl+lbl> 1, 1, 5, 1, 3, 3, 2, 2, 2, 3, 2, 4, 2,…
$ effec_pw_2 <dbl+lbl> 7, 6, 3, 5, 4, 3, 3, 5, 2, 3, 4, 3, 6,…
$ effec_pm_1 <dbl+lbl> 1, 1, 6, 1, 4, 4, 3, 3, 2, 5, 7, 5, 5,…
$ effec_pm_2 <dbl+lbl> 7, 6, 3, 4, 3, 4, 3, 4, 2, 4, 7, 5, 3,…
$ poli_progre_1 <dbl+lbl> 7, 7, 5, 6, 7, 2, 7, 6, 6, 6, 7, 6, 5,…
$ poli_progre_2 <dbl+lbl> 7, 7, 5, 6, 7, 3, 5, 7, 6, 6, 7, 6, 6,…
$ poli_restri_1 <dbl+lbl> 7, 4, 6, 1, 6, 3, 4, 4, 4, 6, 6, 3, 4,…
$ poli_restri_2 <dbl+lbl> 3, 6, 5, 1, 4, 3, 2, 6, 3, 4, 7, 5, 5,…
$ aut_pw_1 <dbl+lbl> 7, 6, 3, 5, 5, 4, 2, 2, 3, 4, 7, 3, 3,…
$ aut_pm_1 <dbl+lbl> 7, 6, 3, 5, 4, 4, 2, 3, 4, 4, 7, 2, 3,…
$ depe_pw_1 <dbl+lbl> 6, 2, 5, 1, 6, 4, 5, 4, 4, 4, 7, 5, 5,…
$ depe_pm_1 <dbl+lbl> 6, 3, 5, 1, 6, 4, 5, 4, 4, 4, 7, 5, 5,…
$ time_violence_First_Click <dbl> 1.529, 11.600, 32.419, 63.577, 13.948, 6.5…
$ time_violence_Last_Click <dbl> 85.503, 117.811, 121.627, 225.891, 87.475,…
$ time_violence_Page_Submit <dbl> 86.202, 120.009, 122.810, 235.969, 93.996,…
$ time_violence_Click_Count <dbl> 58, 22, 27, 26, 25, 40, 30, 22, 23, 25, 25…
$ condi_viole <dbl+lbl> 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1,…
$ hara_pw_1 <dbl+lbl> 7, 6, 3, 7, 5, 5, 5, 5, 5, 6, 4, 5, 4,…
$ hara_pw_2 <dbl+lbl> 7, 7, 7, 7, 7, 7, 7, 6, 7, 7, 7, 7, 5,…
$ hara_pw_3 <dbl+lbl> 7, 6, 2, 7, 6, 7, 7, 5, 6, 7, 7, 7, 6,…
$ abu_pw_1 <dbl+lbl> 7, 7, 3, 7, 5, 7, 7, 6, 6, 7, 7, 7, 7,…
$ abu_pw_2 <dbl+lbl> 7, 7, 4, 7, 6, 7, 7, 7, 7, 7, 7, 7, 7,…
$ abu_pw_3 <dbl+lbl> 7, 7, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,…
$ viole_pw_1 <dbl+lbl> 7, 5, 7, 2, 3, 3, 3, 6, 4, 7, 6, 5, 2,…
$ viole_pw_2 <dbl+lbl> 7, 6, 7, 2, 5, 4, 4, 5, 4, 6, 6, 5, 3,…
$ viole_pw_3 <dbl+lbl> 7, 7, 6, 2, 7, 4, 4, 6, 6, 7, 6, 5, 5,…
$ viole_pw_4 <dbl+lbl> 7, 5, 6, 2, 5, 4, 4, 6, 4, 6, 6, 4, 2,…
$ viole_pw_5 <dbl+lbl> 7, 2, 6, 2, 2, 3, 4, 4, 7, 6, 4, 3, 3,…
$ viole_pw_6 <dbl+lbl> 7, 6, 5, 2, 6, 5, 4, 6, 6, 6, 7, 4, 4,…
$ barri_pw_1 <dbl+lbl> 6, 5, 7, 2, 2, 3, 6, 6, 6, 7, 7, 7, 5,…
$ barri_pw_2 <dbl+lbl> 6, 1, 7, 2, 1, 3, 5, 7, 6, 7, 7, 6, 3,…
$ barri_pw_3 <dbl+lbl> 6, 6, 6, 2, 4, 4, 3, 7, 6, 6, 7, 4, 5,…
$ barri_pw_4 <dbl+lbl> 6, 3, 6, 2, 3, 4, 6, 7, 6, 6, 7, 4, 2,…
$ barri_pw_5 <dbl+lbl> 6, 6, 5, 2, 6, 4, 6, 5, 4, 7, 7, 3, 3,…
$ perpe_1 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ perpe_2 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ perpe_3 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ perpe_4 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ perpe_5 <dbl+lbl> 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,…
$ time_demo_1_First_Click <dbl> 1.801, 1.418, 2.388, 1.532, 1.449, 0.778, …
$ time_demo_1_Last_Click <dbl> 151.620, 113.887, 108.556, 125.513, 61.897…
$ time_demo_1_Page_Submit <dbl> 152.193, 115.771, 110.955, 126.555, 63.446…
$ time_demo_1_Click_Count <dbl> 64, 24, 27, 29, 22, 26, 25, 25, 19, 26, 22…
$ age <dbl+lbl> 54, 58, 57, 30, 25, 22, 27, 29, 22, 41…
$ sex <dbl+lbl> 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2,…
$ sex_other <chr> "", "", "", "", "", "", "", "", "", "", ""…
$ edu <dbl+lbl> 5, 5, 5, 6, 5, 5, 5, 4, 5, 5, 6, 5, 6,…
$ ses <dbl+lbl> 6, 6, 6, 7, 7, 7, 6, 5, 5, 4, 6, 8, 6,…
$ hig_ide <dbl+lbl> 2, 1, 1, 4, 2, 4, 1, 2, 2, 1, 3, 4, 3,…
$ mid_ide <dbl+lbl> 5, 6, 6, 6, 6, 5, 4, 6, 4, 3, 7, 6, 6,…
$ low_ide <dbl+lbl> 3, 1, 2, 2, 1, 2, 3, 2, 3, 5, 1, 3, 2,…
$ po <dbl+lbl> 1, 2, 2, 3, 2, 5, 1, 2, 2, 1, 5, 6, 6,…
$ country_residence <dbl+lbl> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,…
$ country_residence_other <chr> "", "", "", "", "", "", "", "", "", "", ""…
$ country_residence_recoded <dbl+lbl> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,…
$ natio_arge <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_colom <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_espa <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ natio_mex <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_chile <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_peru <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_boli <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_cost <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_cuba <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_ecua <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_elsa <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_guat <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_eqgu <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_hond <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_nica <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_pana <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_para <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_puer <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_domi <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_urug <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_vene <dbl+lbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_other <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ natio_other_text <chr> "", "", "", "", "", "", "", "", "", "", ""…
$ lang <dbl+lbl> 1, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ lang_other <chr> "", "", "Catalán", "Catalán", "", "", "", …
$ lang_recoded <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ inc <dbl> 3200, 1300, 3000, 60000, 3500, 600, 1800, …
$ currency <dbl+lbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,…
$ post_code <chr> "40197", "47001", "08020", "00001", "41005…
$ municipality <chr> "Segovia", "Valladolid", "sant marti", "-"…
$ n_perso <dbl+lbl> 3, 1, 4, 2, 3, 3, 3, 2, 1, 3, 1, 3, 4,…
$ ori_sex <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1,…
$ ori_sex_other <chr> "", "", "", "", "", "", "", "", "", "", ""…
$ relation <dbl+lbl> 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1,…
$ natio_recoded <dbl+lbl> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,…
$ regional_area <dbl+lbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,…
$ PrimarioÚltimo <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…We have 4,386 cases or rows and 283 variables or columns.
4 Processing
4.1 Select
We exclude variables related to attention checks, survey response time, dummy nationalities and the auxiliary variable PrimarioÚltimo, which indicates whether there are duplicate cases.
4.2 Filter
We filter out cases from countries without a sufficiently large sample size for statistical analysis, retaining only those from Argentina, Chile, Colombia, Spain, and Mexico.
frq(db_proc$natio_recoded)Recodification of nationality based on country of residence, declared nationality, and other variables that enabled the identification of the primary and actual nationality in cases of dual or multiple nationalities. (x) <numeric>
# total N=4386 valid N=4386 mean=6.46 sd=5.00
Value | Label | N | Raw % | Valid % | Cum. %
------------------------------------------------------
1 | Argentine | 857 | 19.54 | 19.54 | 19.54
2 | Bolivian | 1 | 0.02 | 0.02 | 19.56
3 | Chilean | 860 | 19.61 | 19.61 | 39.17
4 | Colombian | 824 | 18.79 | 18.79 | 57.96
5 | Costa Rican | 0 | 0.00 | 0.00 | 57.96
6 | Cuban | 5 | 0.11 | 0.11 | 58.07
7 | Ecuadorian | 3 | 0.07 | 0.07 | 58.14
8 | Salvadoran | 2 | 0.05 | 0.05 | 58.19
9 | Spanish | 831 | 18.95 | 18.95 | 77.13
10 | Guatemalan | 1 | 0.02 | 0.02 | 77.15
11 | Equatoguinean | 0 | 0.00 | 0.00 | 77.15
12 | Honduran | 2 | 0.05 | 0.05 | 77.20
13 | Mexican | 837 | 19.08 | 19.08 | 96.28
14 | Nicaraguan | 1 | 0.02 | 0.02 | 96.31
15 | Panamanian | 1 | 0.02 | 0.02 | 96.33
16 | Paraguayan | 2 | 0.05 | 0.05 | 96.37
17 | Peruvian | 70 | 1.60 | 1.60 | 97.97
18 | Puerto Rican | 0 | 0.00 | 0.00 | 97.97
19 | Dominican | 0 | 0.00 | 0.00 | 97.97
20 | Uruguayan | 7 | 0.16 | 0.16 | 98.13
21 | Venezuelan | 75 | 1.71 | 1.71 | 99.84
22 | Rusian | 1 | 0.02 | 0.02 | 99.86
23 | Swiss | 1 | 0.02 | 0.02 | 99.89
24 | EEUU | 1 | 0.02 | 0.02 | 99.91
25 | Brasilian | 4 | 0.09 | 0.09 | 100.00
<NA> | <NA> | 0 | 0.00 | <NA> | <NA>4.3 Recode and transform
Not required.
4.4 Missing values
There is a total of 50.569 missing values in the database, which represents the 5.7% of the total.
n_miss(db_proc) # total of NA's[1] 50569prop_miss(db_proc)*100 # proportion of NA's[1] 5.667214Let’s see the number and percentage of missing values per variable:
db_proc %>%
select(-c(ex_we_1, ex_we_2, in_we_1, in_we_2,
ex_po_1, ex_po_2, in_po_1, in_po_2,
matches("^(greedy|punish|carin)"))) %>%
miss_var_summary(.) %>%
filter(pct_miss > 0) %>%
kable(.,"markdown") | variable | n_miss | pct_miss |
|---|---|---|
| po | 25 | 0.594 |
| age | 21 | 0.499 |
| ori_sex | 11 | 0.261 |
| inc | 10 | 0.238 |
| relation | 8 | 0.190 |
| country_residence | 5 | 0.119 |
| lang_recoded | 2 | 0.0475 |
| n_perso | 1 | 0.0238 |
5 Save and export
Finally, we save and export the processed database db_proc in .RData, .dta and .sav formats.