SPSS和R输出的区别

Question

我正在尝试在以下数据集上提取与 R 的成对比较

> dput(data)
structure(list(Gender = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2), format.spss = "F32.3", labels = c(Women = 1, 
Men = 2), class = c("haven_labelled", "vctrs_vctr", "double")), 
    Education = structure(c(1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 
    1, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 2, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 3, 
    2, 1, 1, 1, 1, 1, 1, 1, 3, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 
    2, 3, 2, 2, 2, 3, 2, 3, 1, 3, 1, 2, 2, 2, 2, 2, 1, 1, 2, 
    2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
    1), format.spss = "F32.3", labels = c(Basic = 1, Medium = 2, 
    Higher = 3), class = c("haven_labelled", "vctrs_vctr", "double"
    )), Avoiding = structure(c(9, 10, 12, 10, 13, 11, 10, 8, 
    5, 6, 7, 8, 12, 6, 9, 10, 9, 11, 9, 13, 11, 10, 10, 14, 13, 
    9, 8, 11, 7, 13, 6, 8, 10, 10, 9, 11, 8, 5, 8, 12, 9, 9, 
    11, 9, 10, 10, 8, 9, 9, 10, 8, 9, 8, 9, 10, 9, 14, 8, 5, 
    11, 5, 7, 14, 8, 11, 8, 9, 9, 8, 15, 9, 6, 8, 10, 9, 9, 10, 
    12, 8, 8, 8, 13, 8, 11, 9, 9, 5, 13, 8, 7, 10, 10, 12, 10, 
    5, 3, 9, 9, 5, 6, 7, 8, 7, 6, 8, 6, 7, 16, 7, 10, 7, 7, 5, 
    4, 11, 16, 6, 9, 10, 10, 5, 9, 9, 7, 9, 12, 11, 10, 8, 10, 
    10, 9, 10, 10, 7, 7, 10, 8, 5, 8, 8, 9, 9, 6, 8), format.spss = "F16.2"), 
    Coping = structure(c(12, 8, 11, 12, 12, 8, 14, 5, 7, 12, 
    10, 15, 10, 7, 7, 7, 13, 7, 9, 12, 13, 11, 11, 15, 7, 5, 
    5, 10, 12, 13, 4, 8, 10, 8, 7, 9, 9, 9, 7, 5, 9, 7, 8, 8, 
    10, 9, 11, 7, 8, 10, 11, 6, 8, 10, 7, 9, 10, 10, 6, 7, 10, 
    12, 13, 9, 13, 8, 9, 11, 6, 6, 7, 8, 6, 13, 12, 9, 15, 11, 
    10, 10, 9, 8, 4, 13, 7, 6, 13, 9, 15, 12, 13, 11, 8, 8, 9, 
    12, 14, 12, 8, 11, 5, 9, 10, 9, 9, 12, 7, 11, 6, 13, 8, 9, 
    9, 5, 14, 16, 13, 10, 7, 14, 9, 9, 10, 8, 8, 13, 9, 14, 11, 
    14, 10, 11, 10, 10, 14, 10, 12, 13, 8, 10, 13, 14, 12, 13, 
    13), format.spss = "F16.2"), Obtaining = structure(c(15, 
    14, 17, 18, 16, 11, 20, 18, 11, 16, 19, 22, 20, 14, 15, 21, 
    19, 15, 16, 22, 19, 15, 18, 19, 20, 13, 16, 22, 20, 22, 20, 
    14, 15, 21, 13, 15, 14, 14, 18, 17, 19, 12, 12, 19, 17, 15, 
    14, 16, 18, 11, 17, 17, 15, 16, 11, 18, 13, 16, 12, 17, 15, 
    18, 21, 18, 18, 10, 14, 15, 15, 22, 16, 20, 14, 16, 21, 17, 
    14, 18, 11, 15, 15, 14, 12, 16, 16, 12, 7, 19, 16, 14, 16, 
    16, 14, 16, 15, 7, 16, 14, 12, 14, 15, 17, 16, 15, 15, 15, 
    13, 11, 7, 19, 17, 18, 16, 6, 20, 22, 14, 19, 19, 16, 18, 
    19, 12, 15, 18, 15, 16, 17, 13, 12, 13, 18, 13, 17, 15, 18, 
    17, 17, 15, 17, 17, 15, 18, 19, 16), format.spss = "F16.2"), 
    Savoring = structure(c(20, 22, 25, 21, 22, 11, 21, 19, 15, 
    18, 23, 24, 19, 20, 20, 22, 19, 24, 22, 22, 24, 19, 19, 25, 
    25, 22, 19, 16, 24, 24, 22, 17, 19, 23, 21, 19, 21, 23, 23, 
    24, 24, 16, 21, 21, 17, 19, 17, 22, 20, 15, 16, 21, 17, 18, 
    19, 21, 17, 18, 23, 21, 13, 17, 24, 14, 19, 21, 21, 19, 20, 
    24, 21, 20, 20, 20, 18, 22, 17, 16, 21, 18, 16, 18, 21, 16, 
    19, 19, 10, 23, 19, 16, 18, 14, 12, 18, 17, 7, 24, 15, 21, 
    16, 11, 17, 21, 17, 15, 21, 12, 14, 12, 14, 16, 16, 19, 19, 
    18, 19, 18, 14, 20, 15, 20, 19, 16, 18, 14, 18, 16, 20, 18, 
    16, 17, 14, 12, 14, 14, 19, 12, 18, 13, 18, 18, 13, 23, 18, 
    19), format.spss = "F16.2"), Efficacy = structure(c(24, 24, 
    29, 28, 29, 22, 30, 26, 16, 22, 26, 30, 32, 20, 24, 31, 28, 
    26, 25, 35, 30, 25, 28, 33, 33, 22, 24, 33, 27, 35, 26, 22, 
    25, 31, 22, 26, 22, 19, 26, 29, 28, 21, 23, 28, 27, 25, 22, 
    25, 27, 21, 25, 26, 23, 25, 21, 27, 27, 24, 17, 28, 20, 25, 
    35, 26, 29, 18, 23, 24, 23, 37, 25, 26, 22, 26, 30, 26, 24, 
    30, 19, 23, 23, 27, 20, 27, 25, 21, 12, 32, 24, 21, 26, 26, 
    26, 26, 20, 10, 25, 23, 17, 20, 22, 25, 23, 21, 23, 21, 20, 
    27, 14, 29, 24, 25, 23, 10, 31, 38, 20, 28, 29, 26, 23, 28, 
    21, 22, 27, 27, 27, 27, 21, 22, 23, 27, 23, 27, 22, 25, 27, 
    25, 20, 25, 25, 24, 27, 25, 24), format.spss = "F16.2")), row.names = c(NA, 
-145L), class = c("tbl_df", "tbl", "data.frame"), na.action = structure(146:422, .Names = c("146", 
"147", "148", "149", "150", "151", "152", "153", "154", "155", 
"156", "157", "158", "159", "160", "161", "162", "163", "164", 
"165", "166", "167", "168", "169", "170", "171", "172", "173", 
"174", "175", "176", "177", "178", "179", "180", "181", "182", 
"183", "184", "185", "186", "187", "188", "189", "190", "191", 
"192", "193", "194", "195", "196", "197", "198", "199", "200", 
"201", "202", "203", "204", "205", "206", "207", "208", "209", 
"210", "211", "212", "213", "214", "215", "216", "217", "218", 
"219", "220", "221", "222", "223", "224", "225", "226", "227", 
"228", "229", "230", "231", "232", "233", "234", "235", "236", 
"237", "238", "239", "240", "241", "242", "243", "244", "245", 
"246", "247", "248", "249", "250", "251", "252", "253", "254", 
"255", "256", "257", "258", "259", "260", "261", "262", "263", 
"264", "265", "266", "267", "268", "269", "270", "271", "272", 
"273", "274", "275", "276", "277", "278", "279", "280", "281", 
"282", "283", "284", "285", "286", "287", "288", "289", "290", 
"291", "292", "293", "294", "295", "296", "297", "298", "299", 
"300", "301", "302", "303", "304", "305", "306", "307", "308", 
"309", "310", "311", "312", "313", "314", "315", "316", "317", 
"318", "319", "320", "321", "322", "323", "324", "325", "326", 
"327", "328", "329", "330", "331", "332", "333", "334", "335", 
"336", "337", "338", "339", "340", "341", "342", "343", "344", 
"345", "346", "347", "348", "349", "350", "351", "352", "353", 
"354", "355", "356", "357", "358", "359", "360", "361", "362", 
"363", "364", "365", "366", "367", "368", "369", "370", "371", 
"372", "373", "374", "375", "376", "377", "378", "379", "380", 
"381", "382", "383", "384", "385", "386", "387", "388", "389", 
"390", "391", "392", "393", "394", "395", "396", "397", "398", 
"399", "400", "401", "402", "403", "404", "405", "406", "407", 
"408", "409", "410", "411", "412", "413", "414", "415", "416", 
"417", "418", "419", "420", "421", "422"), class = "omit"))

我使用了以下代码并且：

library(plotrix)
library(tidyverse)
library(rstatix)
data %>%
  mutate(Gender = names(attributes(Gender)$labels)[Gender],
         Education = names(attributes(Education)$labels[Education]),
         Education = factor(Education, c('Basic', 'Medium', 'Higher')))%>% 
  group_by(Education) %>% 
  tukey_hsd(Savoring ~ Gender,  detailed = TRUE) %>% 
  mutate(stderr = data %>% 
           nest(data = -c(Education)) %>% 
           mutate(ser = map(data, ~std.error(.x$Savoring)), 
                  pval = map(data, ~ tidy(t.test(Savoring ~ Gender, .x))$p.value)) %>% 
           unnest(ser, pval) %>% 
           select(-c(Education, data)))

它返回以下输出：

  Education term   group1 group2 null.value estimate conf.low conf.high    p.adj p.adj.signif stderr~1   $pval
  <fct>     <chr>  <chr>  <chr>       <dbl>    <dbl>    <dbl>     <dbl>    <dbl> <chr>           <dbl>   <dbl>
1 Basic     Gender Men    Women           0     1.51   -0.590    3.60   0.156    ns              0.320 1.27e-2
2 Medium    Gender Men    Women           0     3.34    1.52     5.15   0.000512 ***             0.496 3.59e-4
3 Higher    Gender Men    Women           0    -6.33  -12.6     -0.0835 0.048    *               1.69  9.29e-2

我无法理解为什么 SPSS 和 R 输出之间存在差异，即使它们具有相同的平均差异

谁能提出一个可能的原因？谢谢