Demographic information and standardized test scores of 200 students from the High School and Beyond survey, as distributed by the UCLA Statistical Methods and Data Analytics group.
Format
hsbdemo
A data frame with 200 rows and 13 columns:
- id
Student ID
- female
Gender,
"female"or"male"- ses
Socio-economic status,
"low","middle"or"high"- schtyp
School type,
"public"or"private"- prog
Type of program,
"general","academic"or"vocation"- read
Reading score
- write
Writing score
- math
Math score
- science
Science score
- socst
Social studies score
- honors
Enrollment in honors program,
"enrolled"or"not enrolled"- awards
Number of awards
- cid
Class ID
Source
UCLA Statistical Methods and Data Analytics, https://stats.oarc.ucla.edu/stat/data/hsbdemo.dta
Examples
str(hsbdemo)
#> tibble [200 × 13] (S3: tbl_df/tbl/data.frame)
#> $ id : num [1:200] 45 108 15 67 153 51 164 133 2 53 ...
#> $ female : chr [1:200] "female" "male" "male" "male" ...
#> $ ses : chr [1:200] "low" "middle" "high" "low" ...
#> $ schtyp : chr [1:200] "public" "public" "public" "public" ...
#> $ prog : chr [1:200] "vocation" "general" "vocation" "vocation" ...
#> $ read : num [1:200] 34 34 39 37 39 42 31 50 39 34 ...
#> $ write : num [1:200] 35 33 39 37 31 36 36 31 41 37 ...
#> $ math : num [1:200] 41 41 44 42 40 42 46 40 33 46 ...
#> $ science: num [1:200] 29 36 26 33 39 31 39 34 42 39 ...
#> $ socst : num [1:200] 26 36 42 32 51 39 46 31 41 31 ...
#> $ honors : chr [1:200] "not enrolled" "not enrolled" "not enrolled" "not enrolled" ...
#> $ awards : num [1:200] 0 0 0 0 0 0 0 0 0 0 ...
#> $ cid : num [1:200] 1 1 1 1 1 1 1 1 1 1 ...
mod <- lavaan::sem("read ~ math\nscience ~ read + math", data = hsbdemo)
rmedsem(mod, indep = "math", med = "read", dep = "science")
#> Significance testing of indirect effect (standardized)
#> Model estimated with package 'lavaan'
#> Mediation effect: 'math' -> 'read' -> 'science'
#>
#> Sobel Delta Monte-Carlo
#> Indirect effect 0.251 0.251 0.251
#> Std. Err. 0.046 0.046 0.045
#> z-value 5.501 5.446 5.506
#> p-value 3.79e-08 5.15e-08 3.67e-08
#> CI [0.161, 0.340] [0.160, 0.341] [0.165, 0.341]
#>
#> Baron and Kenny approach to testing mediation
#> STEP 1 - 'math' -> 'read' (X -> M) with B=0.662 and p<0.001
#> STEP 2 - 'read' -> 'science' (M -> Y) with B=0.378 and p<0.001
#> STEP 3 - 'math' -> 'science' (X -> Y) with B=0.380 and p<0.001
#> As STEP 1, STEP 2 and STEP 3 as well as the Sobel's test above
#> are significant the mediation is partial.
#>
#> Zhao, Lynch & Chen's approach to testing mediation
#> Based on p-value estimated using Monte-Carlo
#> STEP 1 - 'math' -> 'science' (X -> Y) with B=0.380 and p<0.001
#> As the Monte-Carlo test above is significant, STEP 1 is
#> significant and their coefficients point in same direction,
#> there is complementary mediation (partial mediation).
#>
#> Effect sizes
#> RIT = (Indirect effect / Total effect)
#> (0.251/0.631) = 0.397
#> Meaning that about 40% of the effect of 'math'
#> on 'science' is mediated by 'read'
#> RID = (Indirect effect / Direct effect)
#> (0.251/0.380) = 0.659
#> That is, the mediated effect is about 0.7 times as
#> large as the direct effect of 'math' on 'science'
#> Upsilon (v) = Variance in Y explained indirectly by X through M
#> v(unadj) = 0.063, v(adj) = 0.061
#>