ICILS2018 USA 測量恆等性
Liz
2023-12-05
ICILS 2018 USA data
ICILS測量八年級學生電腦科學與運算思維的能力與觀感，其中包含以下構念：
Positive Perception = IS2G28A + IS2G28B + IS2G28F + IS2G28G
Negative Perception = IS2G28C + IS2G28D + IS2G28E + IS2G28H
Expectation = IS2G28I + IS2G28J + IS2G28K
Self-Efficacy use
specialist = IS2G27B + IS2G27E + IS2G27G + IS2G27H
Self-Efficacy
use general = IS2G27A + IS2G27C + IS2G27D + IS2G27I + IS2G27J + IS2G27K
+ IS2G27L IS2G27M
每題皆為四點量表
1-4[非常不同意-不同意-同意-非常同意]
dat <- read_sav("./BSGUSAI2.sav")|>
dplyr::select(c(IDSTUD, S_TLANG, S_SEX, S_ISCED, S_P1ISCED, S_P2ISCED, S_HISCED,
S_IMMBGR, S_IMMIG, S_INTNET, S_SPECEFF, S_GENEFF, S_ICTFUT,
S_ICTNEG, S_ICTPOS,
IS2G28A, IS2G28B, IS2G28F, IS2G28G,
IS2G28C, IS2G28D, IS2G28E, IS2G28H,
IS2G28I, IS2G28J, IS2G28K,
IS2G27B, IS2G27E, IS2G27G, IS2G27H,
IS2G27A, IS2G27C, IS2G27D, IS2G27I, IS2G27J, IS2G27K, IS2G27L, IS2G27M))
str(dat)## tibble [6,790 × 38] (S3: tbl_df/tbl/data.frame)
## $ IDSTUD : dbl+lbl [1:6790] 1e+07, 1e+07, 1e+07, 1e+07, 1e+07, 1e+07, 1e+07, 1e+0...
## ..@ label : chr "Student ID"
## ..@ format.spss: chr "F8.0"
## ..@ labels : Named num [1:2] 1e+08 1e+08
## .. ..- attr(*, "names")= chr [1:2] "Not administered or missing by design" "Presented but not answered or invalid"
## $ S_TLANG : dbl+lbl [1:6790] 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,...
## ..@ label : chr "Test language spoken at home"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:4] 0 1 8 9
## .. ..- attr(*, "names")= chr [1:4] "Other Language" "Language of test" "Not administered or missing by design" "Presented but not answered or invalid"
## $ S_SEX : dbl+lbl [1:6790] 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1,...
## ..@ label : chr "Sex of student"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:4] 0 1 8 9
## .. ..- attr(*, "names")= chr [1:4] "Boy" "Girl" "Not administered or missing by design" "Presented but not answered or invalid"
## $ S_ISCED : dbl+lbl [1:6790] 4, 3, 4, 4, 4, 4, 4, 4, 3, 4, 0, 4, 4, 3, 3, 4, 4, 4,...
## ..@ label : chr "Expected ISCED by student"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:7] 0 1 2 3 4 8 9
## .. ..- attr(*, "names")= chr [1:7] "I do not expect to complete ISCED level 2" "ISCED level 2" "ISCED level 3" "ISCED level 4 or 5" ...
## $ S_P1ISCED: dbl+lbl [1:6790] 1, 3, 1, 3, 3, 3, 3, 4, 1, 2, 2, 2, 4, N...
## ..@ label : chr "ISCED of parent 1"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:7] 0 1 2 3 4 8 9
## .. ..- attr(*, "names")= chr [1:7] "He/She did not complete ISCED level 2" "ISCED level 2" "ISCED level 3" "ISCED level 4 or 5" ...
## $ S_P2ISCED: dbl+lbl [1:6790] 1, 3, 1, 2, 3, 2, NA, 4, 2, 3, 3, 2, 3, N...
## ..@ label : chr "ISCED of parent 2"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:7] 0 1 2 3 4 8 9
## .. ..- attr(*, "names")= chr [1:7] "He/She did not complete ISCED level 2" "ISCED level 2" "ISCED level 3" "ISCED level 4 or 5" ...
## $ S_HISCED : dbl+lbl [1:6790] 1, 3, 1, 3, 3, 3, 3, 4, 2, 3, 3, 2, 4, N...
## ..@ label : chr "Highest ISCED of parents"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:7] 0 1 2 3 4 8 9
## .. ..- attr(*, "names")= chr [1:7] "He/She did not complete ISCED level 2" "ISCED level 2" "ISCED level 3" "ISCED level 4 or 5" ...
## $ S_IMMBGR : dbl+lbl [1:6790] 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## ..@ label : chr "Immigration status (dichotomous)"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:4] 0 1 8 9
## .. ..- attr(*, "names")= chr [1:4] "Students without immigrant background" "Students with immigrant background" "Not administered or missing by design" "Presented but not answered or invalid"
## $ S_IMMIG : dbl+lbl [1:6790] 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## ..@ label : chr "Immigration status"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 0 1 2 8 9
## .. ..- attr(*, "names")= chr [1:5] "Students and/or at least one parent born in country of test" "Student born in country of test but both/only parent(s) born abroad" "Student and both/only parent(s) born abroad" "Not administered or missing by design" ...
## $ S_INTNET : dbl+lbl [1:6790] NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## ..@ label : chr "Internet access at home"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:4] 0 1 8 9
## .. ..- attr(*, "names")= chr [1:4] "No" "Yes" "Not administered or missing by design" "Presented but not answered or invalid"
## $ S_SPECEFF: dbl+lbl [1:6790] 54.1, 30.5, 38.7, 38.7, 43.3, 47.1, 50.6, 50.6, 43.3,...
## ..@ label : chr "ICT self-efficacy regarding the use of specialist applications"
## ..@ format.spss: chr "F6.2"
## ..@ labels : Named num [1:2] 998 999
## .. ..- attr(*, "names")= chr [1:2] "Not administered or missing by design" "Presented but not answered or invalid"
## $ S_GENEFF : dbl+lbl [1:6790] 52.5, 29.5, 48.2, 61.1, 52.5, 52.5, 61.1, 48.2, 61.1,...
## ..@ label : chr "ICT self-efficacy regarding the use of general applications"
## ..@ format.spss: chr "F6.2"
## ..@ labels : Named num [1:2] 998 999
## .. ..- attr(*, "names")= chr [1:2] "Not administered or missing by design" "Presented but not answered or invalid"
## $ S_ICTFUT : dbl+lbl [1:6790] 53.0, 53.7, 50.1, 43.4, 43.4, 43.4, 50.1, 36.2, 46.6,...
## ..@ label : chr "Expectations of future ICT use for work and study"
## ..@ format.spss: chr "F6.2"
## ..@ labels : Named num [1:2] 998 999
## .. ..- attr(*, "names")= chr [1:2] "Not administered or missing by design" "Presented but not answered or invalid"
## $ S_ICTNEG : dbl+lbl [1:6790] 54.7, 44.4, 54.7, 38.0, 44.4, 44.4, 47.7, 38.0, 51.1,...
## ..@ label : chr "Negative perceptions of ICT for society"
## ..@ format.spss: chr "F6.2"
## ..@ labels : Named num [1:2] 998 999
## .. ..- attr(*, "names")= chr [1:2] "Not administered or missing by design" "Presented but not answered or invalid"
## $ S_ICTPOS : dbl+lbl [1:6790] 56.1, 51.8, 41.8, 56.1, 46.4, 46.5, 46.5, 56.1, 41.8,...
## ..@ label : chr "Positive perceptions of ICT for society"
## ..@ format.spss: chr "F6.2"
## ..@ labels : Named num [1:2] 998 999
## .. ..- attr(*, "names")= chr [1:2] "Not administered or missing by design" "Presented but not answered or invalid"
## $ IS2G28A : dbl+lbl [1:6790] 1, 2, 3, 2, NA, 2, 2, 2, 2, 3, 2, 3, 2, N...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/Agree or disagree with the following statements about ICT in society/Advances in "| __truncated__
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:6] 1 2 3 4 8 9
## .. ..- attr(*, "names")= chr [1:6] "Strongly agree" "Agree" "Disagree" "Strongly disagree" ...
## $ IS2G28B : dbl+lbl [1:6790] 2, 1, 2, 1, 2, 2, 2, 2, 3, 2, 2, 3, 2, N...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/Agree or disagree with the following statements about ICT in society/ICT helps us"| __truncated__
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:6] 1 2 3 4 8 9
## .. ..- attr(*, "names")= chr [1:6] "Strongly agree" "Agree" "Disagree" "Strongly disagree" ...
## $ IS2G28F : dbl+lbl [1:6790] 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 3, 3, N...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/Agree or disagree with the following statements about ICT in society/ICT is valuable to society"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:6] 1 2 3 4 8 9
## .. ..- attr(*, "names")= chr [1:6] "Strongly agree" "Agree" "Disagree" "Strongly disagree" ...
## $ IS2G28G : dbl+lbl [1:6790] 1, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 3, 2, N...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/Agree or disagree with the following statements about ICT in society/Advances in "| __truncated__
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:6] 1 2 3 4 8 9
## .. ..- attr(*, "names")= chr [1:6] "Strongly agree" "Agree" "Disagree" "Strongly disagree" ...
## $ IS2G28C : dbl+lbl [1:6790] 2, 3, 2, 2, 2, 2, 2, 2, 1, 1, 3, 3, 2, N...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/Agree or disagree with the following statements about ICT in society/Using ICT ma"| __truncated__
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:6] 1 2 3 4 8 9
## .. ..- attr(*, "names")= chr [1:6] "Strongly agree" "Agree" "Disagree" "Strongly disagree" ...
## $ IS2G28D : dbl+lbl [1:6790] 2, 2, 1, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, N...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/Agree or disagree with the following statements about ICT in society/With more IC"| __truncated__
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:6] 1 2 3 4 8 9
## .. ..- attr(*, "names")= chr [1:6] "Strongly agree" "Agree" "Disagree" "Strongly disagree" ...
## $ IS2G28E : dbl+lbl [1:6790] 1, 2, 2, 3, 2, 2, 2, 3, 1, 1, 2, 3, 2, N...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/Agree or disagree with the following statements about ICT in society/People spend"| __truncated__
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:6] 1 2 3 4 8 9
## .. ..- attr(*, "names")= chr [1:6] "Strongly agree" "Agree" "Disagree" "Strongly disagree" ...
## $ IS2G28H : dbl+lbl [1:6790] 2, 3, 2, 4, 3, 3, 2, 4, 3, 2, 2, 3, 2, N...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/Agree or disagree with the following statements about ICT in society/Using ICT ma"| __truncated__
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:6] 1 2 3 4 8 9
## .. ..- attr(*, "names")= chr [1:6] "Strongly agree" "Agree" "Disagree" "Strongly disagree" ...
## $ IS2G28I : dbl+lbl [1:6790] 2, 2, 3, 3, 3, 4, 2, 4, 3, 2, NA, 3, 3, N...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/Agree or disagree with the following statements about ICT in society/I would like"| __truncated__
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:6] 1 2 3 4 8 9
## .. ..- attr(*, "names")= chr [1:6] "Strongly agree" "Agree" "Disagree" "Strongly disagree" ...
## $ IS2G28J : dbl+lbl [1:6790] NA, 2, 2, 3, 3, 3, 3, 4, 3, 3, NA, 3, 3, N...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/Agree or disagree with the following statements about ICT in society/I hope to fi"| __truncated__
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:6] 1 2 3 4 8 9
## .. ..- attr(*, "names")= chr [1:6] "Strongly agree" "Agree" "Disagree" "Strongly disagree" ...
## $ IS2G28K : dbl+lbl [1:6790] 2, 2, 2, 3, 3, 2, 2, 3, 2, 2, NA, 3, 3, N...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/Agree or disagree with the following statements about ICT in society/Learning how"| __truncated__
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:6] 1 2 3 4 8 9
## .. ..- attr(*, "names")= chr [1:6] "Strongly agree" "Agree" "Disagree" "Strongly disagree" ...
## $ IS2G27B : dbl+lbl [1:6790] 2, 3, 3, 3, 3, 2, 2, 2, 2, 3, 3, 2, 2, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Create a database (e.g. using [Microsoft Access ®])"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27E : dbl+lbl [1:6790] 1, 3, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 3, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Build or edit a webpage"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27G : dbl+lbl [1:6790] 2, 3, 3, 3, 2, 3, 2, 2, 3, 3, NA, 2, 3, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Create a computer program, macro, or [app]"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27H : dbl+lbl [1:6790] 2, 3, 3, 3, 3, 3, 2, 2, 3, 3, NA, 2, 3, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Set up a local area network of computers or other ICT"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27A : dbl+lbl [1:6790] 1, 3, 2, 1, 1, 2, 1, 2, 1, 3, 1, 1, 1, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Edit digital photographs or other graphic images"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27C : dbl+lbl [1:6790] 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Write or edit text for a school assignment"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27D : dbl+lbl [1:6790] 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Search for and find relevant information for a school project"| __truncated__
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27I : dbl+lbl [1:6790] 1, 3, 2, 1, 1, 1, 1, 2, 1, 3, NA, 1, 1, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Create a multi-media presentation"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27J : dbl+lbl [1:6790] 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, 1, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Upload text, images, or video to an online profile"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27K : dbl+lbl [1:6790] 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, 1, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Insert an image into a document or message"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27L : dbl+lbl [1:6790] 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, 1, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Install a program or [<app>]"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27M : dbl+lbl [1:6790] 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, NA, 2, 1, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Judge whether you can trust information you find on the Internet"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...# recode & sum
vars <- c("IS2G28A", "IS2G28B", "IS2G28F", "IS2G28G",
"IS2G28C", "IS2G28D", "IS2G28E", "IS2G28H",
"IS2G28I", "IS2G28J", "IS2G28K")
table(dat$IS2G28A)##
## 1 2 3 4
## 1339 3449 900 139##
## 1 2 3 4
## 139 900 3449 1339dat <- dat |>
dplyr::mutate(PP = IS2G28A + IS2G28B + IS2G28F + IS2G28G, # PP Positive Perception
NP = IS2G28C + IS2G28D + IS2G28E + IS2G28H, # NP Negative Perception
EP = IS2G28I + IS2G28J + IS2G28K, # EP Expectation
SEs = IS2G27B + IS2G27E + IS2G27G + IS2G27H,# Self-Efficacy use specialist
SEg = IS2G27A + IS2G27C + IS2G27D + IS2G27I + IS2G27J + IS2G27K + IS2G27L + IS2G27M)|> # Self-Efficacy use general
dplyr::mutate(PP = as.factor(PP),
NP = as.factor(NP),
EP = as.factor(EP),
SEs = as.factor(SEs),
SEg = as.factor(SEg))
names(dat)## [1] "IDSTUD" "S_TLANG" "S_SEX" "S_ISCED" "S_P1ISCED" "S_P2ISCED"
## [7] "S_HISCED" "S_IMMBGR" "S_IMMIG" "S_INTNET" "S_SPECEFF" "S_GENEFF"
## [13] "S_ICTFUT" "S_ICTNEG" "S_ICTPOS" "IS2G28A" "IS2G28B" "IS2G28F"
## [19] "IS2G28G" "IS2G28C" "IS2G28D" "IS2G28E" "IS2G28H" "IS2G28I"
## [25] "IS2G28J" "IS2G28K" "IS2G27B" "IS2G27E" "IS2G27G" "IS2G27H"
## [31] "IS2G27A" "IS2G27C" "IS2G27D" "IS2G27I" "IS2G27J" "IS2G27K"
## [37] "IS2G27L" "IS2G27M" "PP" "NP" "EP" "SEs"
## [43] "SEg"## tibble [6,790 × 28] (S3: tbl_df/tbl/data.frame)
## $ IS2G28A: num [1:6790] 4 3 2 3 NA 3 3 3 3 2 ...
## $ IS2G28B: num [1:6790] 3 4 3 4 3 3 3 3 2 3 ...
## $ IS2G28F: num [1:6790] 3 3 3 3 3 3 3 4 3 4 ...
## $ IS2G28G: num [1:6790] 4 3 3 4 3 3 3 4 3 4 ...
## $ IS2G28C: num [1:6790] 3 2 3 3 3 3 3 3 4 4 ...
## $ IS2G28D: num [1:6790] 3 3 4 2 2 2 2 2 2 2 ...
## $ IS2G28E: num [1:6790] 4 3 3 2 3 3 3 2 4 4 ...
## $ IS2G28H: num [1:6790] 3 2 3 1 2 2 3 1 2 3 ...
## $ IS2G28I: num [1:6790] 3 3 2 2 2 1 3 1 2 3 ...
## $ IS2G28J: num [1:6790] NA 3 3 2 2 2 2 1 2 2 ...
## $ IS2G28K: num [1:6790] 3 3 3 2 2 3 3 2 3 3 ...
## $ IS2G27B: dbl+lbl [1:6790] 2, 3, 3, 3, 3, 2, 2, 2, 2, 3, 3, 2, 2, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Create a database (e.g. using [Microsoft Access ®])"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27E: dbl+lbl [1:6790] 1, 3, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 3, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Build or edit a webpage"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27G: dbl+lbl [1:6790] 2, 3, 3, 3, 2, 3, 2, 2, 3, 3, NA, 2, 3, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Create a computer program, macro, or [app]"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27H: dbl+lbl [1:6790] 2, 3, 3, 3, 3, 3, 2, 2, 3, 3, NA, 2, 3, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Set up a local area network of computers or other ICT"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27A: dbl+lbl [1:6790] 1, 3, 2, 1, 1, 2, 1, 2, 1, 3, 1, 1, 1, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Edit digital photographs or other graphic images"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27C: dbl+lbl [1:6790] 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Write or edit text for a school assignment"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27D: dbl+lbl [1:6790] 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Search for and find relevant information for a school project"| __truncated__
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27I: dbl+lbl [1:6790] 1, 3, 2, 1, 1, 1, 1, 2, 1, 3, NA, 1, 1, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Create a multi-media presentation"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27J: dbl+lbl [1:6790] 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, 1, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Upload text, images, or video to an online profile"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27K: dbl+lbl [1:6790] 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, 1, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Insert an image into a document or message"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27L: dbl+lbl [1:6790] 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, 1, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Install a program or [<app>]"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ IS2G27M: dbl+lbl [1:6790] 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, NA, 2, 1, ...
## ..@ label : chr "YOUR THOUGHTS ABOUT USING ICT/How well can you do/Judge whether you can trust information you find on the Internet"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:5] 1 2 3 8 9
## .. ..- attr(*, "names")= chr [1:5] "I know how to do this." "I have never done this but I could work out how to do this." "I do not think I could do this." "Not administered or missing by design" ...
## $ PP : Factor w/ 13 levels "4","5","6","7",..: 11 10 8 11 NA 9 9 11 8 10 ...
## $ NP : Factor w/ 13 levels "4","5","6","7",..: 10 7 10 5 7 7 8 5 9 10 ...
## $ EP : Factor w/ 10 levels "3","4","5","6",..: NA 7 6 4 4 4 6 2 5 6 ...
## $ SEs : Factor w/ 9 levels "4","5","6","7",..: 4 9 8 8 7 6 5 5 7 8 ...
## $ SEg : Factor w/ 17 levels "8","9","10","11",..: 2 13 3 1 2 2 1 3 1 5 ...## $PP
## [1] "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16"
##
## $NP
## [1] "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16"
##
## $EP
## [1] "3" "4" "5" "6" "7" "8" "9" "10" "11" "12"
##
## $SEs
## [1] "4" "5" "6" "7" "8" "9" "10" "11" "12"
##
## $SEg
## [1] "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22"
## [16] "23" "24"## IDSTUD S_TLANG S_SEX S_ISCED S_P1ISCED S_P2ISCED S_HISCED S_IMMBGR
## 0 361 7 418 819 1099 736 482
## S_IMMIG S_INTNET S_SPECEFF S_GENEFF S_ICTFUT S_ICTNEG S_ICTPOS IS2G28A
## 482 6790 807 786 1156 951 936 963
## IS2G28B IS2G28F IS2G28G IS2G28C IS2G28D IS2G28E IS2G28H IS2G28I
## 967 1007 1050 1069 1026 1017 1039 1182
## IS2G28J IS2G28K IS2G27B IS2G27E IS2G27G IS2G27H IS2G27A IS2G27C
## 1187 1190 874 842 972 1025 791 862
## IS2G27D IS2G27I IS2G27J IS2G27K IS2G27L IS2G27M PP NP
## 851 1023 1033 1019 1079 1080 1167 1293
## EP SEs SEg
## 1274 1189 1403Over View
Perception & Expectation
tabP <- apply(dat[,c(16:26)], 2, function(x) table(x))
par(mfrow=c(3,4))
lapply(colnames(tabP), function(x) barplot(tabP[ , x], main = x, ylim =c(0,3000)))## [[1]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
## [4,] 4.3
##
## [[2]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
## [4,] 4.3
##
## [[3]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
## [4,] 4.3
##
## [[4]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
## [4,] 4.3
##
## [[5]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
## [4,] 4.3
##
## [[6]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
## [4,] 4.3
##
## [[7]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
## [4,] 4.3
##
## [[8]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
## [4,] 4.3
##
## [[9]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
## [4,] 4.3
##
## [[10]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
## [4,] 4.3
##
## [[11]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
## [4,] 4.3
Self-Efficacy
tabE <- apply(dat[ ,c(27:38)], 2, function(x) table(x))
par(mfrow=c(3,4))
lapply(colnames(tabE), function(x) barplot(tabE[ , x], main = x, ylim =c(0,4500)))
## [[1]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
##
## [[2]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
##
## [[3]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
##
## [[4]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
##
## [[5]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
##
## [[6]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
##
## [[7]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
##
## [[8]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
##
## [[9]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
##
## [[10]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
##
## [[11]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1
##
## [[12]]
## [,1]
## [1,] 0.7
## [2,] 1.9
## [3,] 3.1Gender or Culture
1.Culture
pacman::p_load(tidyverse, knitr, furniture, ggplot2)
kable(table1(dat,as.factor(PP), splitby = ~ S_TLANG, col_wise=T, output = 'text2'))| . | 0 | 1 |
|---|---|---|
| n = 914 | n = 4692 | |
| as factor(PP) | ||
| 4 | 9 (1%) | 31 (0.7%) |
| 5 | 0 (0%) | 3 (0.1%) |
| 6 | 5 (0.5%) | 18 (0.4%) |
| 7 | 7 (0.8%) | 27 (0.6%) |
| 8 | 14 (1.5%) | 89 (1.9%) |
| 9 | 40 (4.4%) | 167 (3.6%) |
| 10 | 74 (8.1%) | 405 (8.6%) |
| 11 | 149 (16.3%) | 664 (14.2%) |
| 12 | 282 (30.9%) | 1298 (27.7%) |
| 13 | 114 (12.5%) | 602 (12.8%) |
| 14 | 93 (10.2%) | 572 (12.2%) |
| 15 | 48 (5.3%) | 358 (7.6%) |
| 16 | 79 (8.6%) | 458 (9.8%) |
| . | 0 | 1 |
|---|---|---|
| n = 894 | n = 4587 | |
| as factor(NP) | ||
| 4 | 12 (1.3%) | 49 (1.1%) |
| 5 | 4 (0.4%) | 42 (0.9%) |
| 6 | 9 (1%) | 94 (2%) |
| 7 | 27 (3%) | 225 (4.9%) |
| 8 | 70 (7.8%) | 453 (9.9%) |
| 9 | 100 (11.2%) | 606 (13.2%) |
| 10 | 141 (15.8%) | 719 (15.7%) |
| 11 | 159 (17.8%) | 697 (15.2%) |
| 12 | 161 (18%) | 724 (15.8%) |
| 13 | 88 (9.8%) | 411 (9%) |
| 14 | 61 (6.8%) | 278 (6.1%) |
| 15 | 36 (4%) | 158 (3.4%) |
| 16 | 26 (2.9%) | 131 (2.9%) |
| . | 0 | 1 |
|---|---|---|
| n = 905 | n = 4594 | |
| as factor(EP) | ||
| 3 | 32 (3.5%) | 219 (4.8%) |
| 4 | 15 (1.7%) | 131 (2.9%) |
| 5 | 35 (3.9%) | 282 (6.1%) |
| 6 | 101 (11.2%) | 747 (16.3%) |
| 7 | 121 (13.4%) | 755 (16.4%) |
| 8 | 152 (16.8%) | 641 (14%) |
| 9 | 231 (25.5%) | 834 (18.2%) |
| 10 | 80 (8.8%) | 319 (6.9%) |
| 11 | 56 (6.2%) | 254 (5.5%) |
| 12 | 82 (9.1%) | 412 (9%) |
2.Gender
| . | 0 | 1 |
|---|---|---|
| n = 2834 | n = 2789 | |
| as factor(PP) | ||
| 4 | 26 (0.9%) | 14 (0.5%) |
| 5 | 1 (0%) | 2 (0.1%) |
| 6 | 9 (0.3%) | 15 (0.5%) |
| 7 | 17 (0.6%) | 17 (0.6%) |
| 8 | 59 (2.1%) | 44 (1.6%) |
| 9 | 98 (3.5%) | 110 (3.9%) |
| 10 | 202 (7.1%) | 277 (9.9%) |
| 11 | 323 (11.4%) | 491 (17.6%) |
| 12 | 736 (26%) | 848 (30.4%) |
| 13 | 378 (13.3%) | 342 (12.3%) |
| 14 | 386 (13.6%) | 280 (10%) |
| 15 | 239 (8.4%) | 170 (6.1%) |
| 16 | 360 (12.7%) | 179 (6.4%) |
| . | 0 | 1 |
|---|---|---|
| n = 2759 | n = 2738 | |
| as factor(NP) | ||
| 4 | 42 (1.5%) | 19 (0.7%) |
| 5 | 30 (1.1%) | 16 (0.6%) |
| 6 | 65 (2.4%) | 38 (1.4%) |
| 7 | 160 (5.8%) | 92 (3.4%) |
| 8 | 288 (10.4%) | 236 (8.6%) |
| 9 | 388 (14.1%) | 318 (11.6%) |
| 10 | 433 (15.7%) | 430 (15.7%) |
| 11 | 399 (14.5%) | 458 (16.7%) |
| 12 | 425 (15.4%) | 464 (16.9%) |
| 13 | 227 (8.2%) | 274 (10%) |
| 14 | 142 (5.1%) | 199 (7.3%) |
| 15 | 71 (2.6%) | 125 (4.6%) |
| 16 | 89 (3.2%) | 69 (2.5%) |
| . | 0 | 1 |
|---|---|---|
| n = 2735 | n = 2781 | |
| as factor(EP) | ||
| 3 | 100 (3.7%) | 152 (5.5%) |
| 4 | 54 (2%) | 93 (3.3%) |
| 5 | 121 (4.4%) | 196 (7%) |
| 6 | 350 (12.8%) | 500 (18%) |
| 7 | 351 (12.8%) | 530 (19.1%) |
| 8 | 375 (13.7%) | 419 (15.1%) |
| 9 | 576 (21.1%) | 493 (17.7%) |
| 10 | 246 (9%) | 154 (5.5%) |
| 11 | 216 (7.9%) | 96 (3.5%) |
| 12 | 346 (12.7%) | 148 (5.3%) |
Clean up & Describe
Clean up
datP <- dat|>
dplyr::select(c(IDSTUD, S_TLANG, S_SEX,
S_ICTNEG, S_ICTPOS, S_ICTFUT,
IS2G28A, IS2G28B, IS2G28F, IS2G28G,
IS2G28C, IS2G28D, IS2G28E, IS2G28H,
IS2G28I, IS2G28J, IS2G28K,
PP, NP, EP))|>
dplyr::mutate(S_SEX = recode_factor(as.factor(S_SEX), "0" = "m", "1" = "f"))|>
na.omit(.)
str(datP)## tibble [5,074 × 20] (S3: tbl_df/tbl/data.frame)
## $ IDSTUD : dbl+lbl [1:5074] 1e+07, 1e+07, 1e+07, 1e+07, 1e+07, 1e+07, 1e+07, 1e+0...
## ..@ label : chr "Student ID"
## ..@ format.spss: chr "F8.0"
## ..@ labels : Named num [1:2] 1e+08 1e+08
## .. ..- attr(*, "names")= chr [1:2] "Not administered or missing by design" "Presented but not answered or invalid"
## $ S_TLANG : dbl+lbl [1:5074] 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,...
## ..@ label : chr "Test language spoken at home"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:4] 0 1 8 9
## .. ..- attr(*, "names")= chr [1:4] "Other Language" "Language of test" "Not administered or missing by design" "Presented but not answered or invalid"
## $ S_SEX : Factor w/ 2 levels "m","f": 1 2 2 2 2 1 2 1 2 1 ...
## $ S_ICTNEG: dbl+lbl [1:5074] 44.4, 54.7, 38.0, 44.4, 47.7, 38.0, 51.1, 54.7, 38.0,...
## ..@ label : chr "Negative perceptions of ICT for society"
## ..@ format.spss: chr "F6.2"
## ..@ labels : Named num [1:2] 998 999
## .. ..- attr(*, "names")= chr [1:2] "Not administered or missing by design" "Presented but not answered or invalid"
## $ S_ICTPOS: dbl+lbl [1:5074] 51.8, 41.8, 56.1, 46.5, 46.5, 56.1, 41.8, 51.8, 33.5,...
## ..@ label : chr "Positive perceptions of ICT for society"
## ..@ format.spss: chr "F6.2"
## ..@ labels : Named num [1:2] 998 999
## .. ..- attr(*, "names")= chr [1:2] "Not administered or missing by design" "Presented but not answered or invalid"
## $ S_ICTFUT: dbl+lbl [1:5074] 53.7, 50.1, 43.4, 43.4, 50.1, 36.2, 46.6, 50.1, 43.4,...
## ..@ label : chr "Expectations of future ICT use for work and study"
## ..@ format.spss: chr "F6.2"
## ..@ labels : Named num [1:2] 998 999
## .. ..- attr(*, "names")= chr [1:2] "Not administered or missing by design" "Presented but not answered or invalid"
## $ IS2G28A : num [1:5074] 3 2 3 3 3 3 3 2 2 3 ...
## $ IS2G28B : num [1:5074] 4 3 4 3 3 3 2 3 2 3 ...
## $ IS2G28F : num [1:5074] 3 3 3 3 3 4 3 4 2 2 ...
## $ IS2G28G : num [1:5074] 3 3 4 3 3 4 3 4 2 3 ...
## $ IS2G28C : num [1:5074] 2 3 3 3 3 3 4 4 2 3 ...
## $ IS2G28D : num [1:5074] 3 4 2 2 2 2 2 2 2 2 ...
## $ IS2G28E : num [1:5074] 3 3 2 3 3 2 4 4 2 3 ...
## $ IS2G28H : num [1:5074] 2 3 1 2 3 1 2 3 2 3 ...
## $ IS2G28I : num [1:5074] 3 2 2 1 3 1 2 3 2 2 ...
## $ IS2G28J : num [1:5074] 3 3 2 2 2 1 2 2 2 2 ...
## $ IS2G28K : num [1:5074] 3 3 2 3 3 2 3 3 2 2 ...
## $ PP : Factor w/ 13 levels "4","5","6","7",..: 10 8 11 9 9 11 8 10 5 8 ...
## $ NP : Factor w/ 13 levels "4","5","6","7",..: 7 10 5 7 8 5 9 10 5 8 ...
## $ EP : Factor w/ 10 levels "3","4","5","6",..: 7 6 4 4 6 2 5 6 4 4 ...
## - attr(*, "na.action")= 'omit' Named int [1:1716] 1 5 11 14 17 20 28 30 36 40 ...
## ..- attr(*, "names")= chr [1:1716] "1" "5" "11" "14" ...Describe
## vars n mean sd median trimmed mad min max range skew kurtosis
## IS2G28A 1 5074 3.03 0.69 3 3.06 0.00 1 4 3 -0.47 0.46
## IS2G28B 2 5074 3.14 0.69 3 3.20 0.00 1 4 3 -0.56 0.46
## IS2G28F 3 5074 3.16 0.71 3 3.23 0.00 1 4 3 -0.61 0.41
## IS2G28G 4 5074 3.04 0.75 3 3.09 0.00 1 4 3 -0.52 0.12
## IS2G28C 5 5074 2.70 0.84 3 2.71 1.48 1 4 3 -0.11 -0.61
## IS2G28D 6 5074 2.39 0.86 2 2.36 1.48 1 4 3 0.26 -0.56
## IS2G28E 7 5074 2.91 0.86 3 2.96 1.48 1 4 3 -0.42 -0.47
## IS2G28H 8 5074 2.70 0.86 3 2.75 1.48 1 4 3 -0.25 -0.57
## IS2G28I 9 5074 2.50 0.92 2 2.50 1.48 1 4 3 0.06 -0.85
## IS2G28J 10 5074 2.49 0.92 2 2.49 1.48 1 4 3 0.05 -0.84
## IS2G28K 11 5074 2.91 0.86 3 2.98 1.48 1 4 3 -0.49 -0.36
## PP* 12 5074 9.37 2.14 9 9.40 1.48 1 13 12 -0.43 1.06
## NP* 13 5074 7.70 2.42 8 7.71 2.97 1 13 12 -0.09 -0.06
## EP* 14 5074 5.90 2.31 6 5.90 2.97 1 10 9 -0.04 -0.47
## se
## IS2G28A 0.01
## IS2G28B 0.01
## IS2G28F 0.01
## IS2G28G 0.01
## IS2G28C 0.01
## IS2G28D 0.01
## IS2G28E 0.01
## IS2G28H 0.01
## IS2G28I 0.01
## IS2G28J 0.01
## IS2G28K 0.01
## PP* 0.03
## NP* 0.03
## EP* 0.03# 根據Curran、West與Finch（1996）之建議
# 以偏態係數絕對值小於2及峰度係數絕對值小於7作為判斷資料常態性的標準。
# 選擇所需的變數
ken_vars <- datP |>
dplyr::select(IS2G28A, IS2G28B, IS2G28F, IS2G28G,
IS2G28C, IS2G28D, IS2G28E, IS2G28H,
IS2G28I, IS2G28J, IS2G28K,
PP, NP, EP)
# 將因子轉換為數值型
ken_varp <- as.data.frame(lapply(ken_vars[,c(1:4,12:14)], as.numeric))
ken_varn <- as.data.frame(lapply(ken_vars[,c(5:8,12:14)], as.numeric))
ken_vare <- as.data.frame(lapply(ken_vars[,c(9:11,12:14)], as.numeric))
# 計算Kendall相關係數
ken_matp <- cor(ken_varp, method = "kendall")
ken_matn <- cor(ken_varn, method = "kendall")
ken_mate <- cor(ken_vare, method = "kendall")
kable(ken_matp, digits = 3)| IS2G28A | IS2G28B | IS2G28F | IS2G28G | PP | NP | EP | |
|---|---|---|---|---|---|---|---|
| IS2G28A | 1.000 | 0.489 | 0.345 | 0.340 | 0.634 | -0.041 | 0.248 |
| IS2G28B | 0.489 | 1.000 | 0.406 | 0.359 | 0.672 | -0.027 | 0.250 |
| IS2G28F | 0.345 | 0.406 | 1.000 | 0.424 | 0.654 | -0.025 | 0.221 |
| IS2G28G | 0.340 | 0.359 | 0.424 | 1.000 | 0.652 | -0.076 | 0.225 |
| PP | 0.634 | 0.672 | 0.654 | 0.652 | 1.000 | -0.068 | 0.281 |
| NP | -0.041 | -0.027 | -0.025 | -0.076 | -0.068 | 1.000 | -0.015 |
| EP | 0.248 | 0.250 | 0.221 | 0.225 | 0.281 | -0.015 | 1.000 |
| IS2G28C | IS2G28D | IS2G28E | IS2G28H | PP | NP | EP | |
|---|---|---|---|---|---|---|---|
| IS2G28C | 1.000 | 0.256 | 0.337 | 0.273 | -0.012 | 0.589 | 0.012 |
| IS2G28D | 0.256 | 1.000 | 0.290 | 0.230 | -0.044 | 0.557 | 0.008 |
| IS2G28E | 0.337 | 0.290 | 1.000 | 0.387 | -0.092 | 0.655 | -0.043 |
| IS2G28H | 0.273 | 0.230 | 0.387 | 1.000 | -0.024 | 0.604 | 0.005 |
| PP | -0.012 | -0.044 | -0.092 | -0.024 | 1.000 | -0.068 | 0.281 |
| NP | 0.589 | 0.557 | 0.655 | 0.604 | -0.068 | 1.000 | -0.015 |
| EP | 0.012 | 0.008 | -0.043 | 0.005 | 0.281 | -0.015 | 1.000 |
| IS2G28I | IS2G28J | IS2G28K | PP | NP | EP | |
|---|---|---|---|---|---|---|
| IS2G28I | 1.000 | 0.627 | 0.458 | 0.232 | -0.015 | 0.779 |
| IS2G28J | 0.627 | 1.000 | 0.500 | 0.241 | -0.022 | 0.801 |
| IS2G28K | 0.458 | 0.500 | 1.000 | 0.315 | -0.012 | 0.696 |
| PP | 0.232 | 0.241 | 0.315 | 1.000 | -0.068 | 0.281 |
| NP | -0.015 | -0.022 | -0.012 | -0.068 | 1.000 | -0.015 |
| EP | 0.779 | 0.801 | 0.696 | 0.281 | -0.015 | 1.000 |
| . | m | f |
|---|---|---|
| n = 2528 | n = 2546 | |
| PP | ||
| 4 | 24 (0.9%) | 13 (0.5%) |
| 5 | 1 (0%) | 2 (0.1%) |
| 6 | 6 (0.2%) | 14 (0.5%) |
| 7 | 16 (0.6%) | 13 (0.5%) |
| 8 | 51 (2%) | 41 (1.6%) |
| 9 | 83 (3.3%) | 98 (3.8%) |
| 10 | 176 (7%) | 249 (9.8%) |
| 11 | 284 (11.2%) | 453 (17.8%) |
| 12 | 668 (26.4%) | 771 (30.3%) |
| 13 | 327 (12.9%) | 319 (12.5%) |
| 14 | 344 (13.6%) | 257 (10.1%) |
| 15 | 213 (8.4%) | 151 (5.9%) |
| 16 | 335 (13.3%) | 165 (6.5%) |
| . | m | f |
|---|---|---|
| n = 2528 | n = 2546 | |
| NP | ||
| 4 | 41 (1.6%) | 19 (0.7%) |
| 5 | 27 (1.1%) | 14 (0.5%) |
| 6 | 61 (2.4%) | 35 (1.4%) |
| 7 | 149 (5.9%) | 84 (3.3%) |
| 8 | 261 (10.3%) | 222 (8.7%) |
| 9 | 360 (14.2%) | 290 (11.4%) |
| 10 | 397 (15.7%) | 404 (15.9%) |
| 11 | 359 (14.2%) | 429 (16.8%) |
| 12 | 392 (15.5%) | 434 (17%) |
| 13 | 208 (8.2%) | 256 (10.1%) |
| 14 | 128 (5.1%) | 186 (7.3%) |
| 15 | 62 (2.5%) | 115 (4.5%) |
| 16 | 83 (3.3%) | 58 (2.3%) |
| . | m | f |
|---|---|---|
| n = 2528 | n = 2546 | |
| EP | ||
| 3 | 89 (3.5%) | 143 (5.6%) |
| 4 | 53 (2.1%) | 81 (3.2%) |
| 5 | 112 (4.4%) | 187 (7.3%) |
| 6 | 319 (12.6%) | 466 (18.3%) |
| 7 | 324 (12.8%) | 479 (18.8%) |
| 8 | 337 (13.3%) | 383 (15%) |
| 9 | 543 (21.5%) | 440 (17.3%) |
| 10 | 218 (8.6%) | 142 (5.6%) |
| 11 | 202 (8%) | 88 (3.5%) |
| 12 | 331 (13.1%) | 137 (5.4%) |
Pic
datP |>
gather(key, value, IS2G28A:IS2G28G) |>
ggplot(aes(as.factor(S_SEX), value, group = S_SEX))+
stat_summary(fun.data = 'mean_se')+
facet_grid(~ key)+
labs(x = "gender", y = "Positive Perception")
datP |>
gather(key, value, IS2G28C:IS2G28H) |>
ggplot(aes(as.factor(S_SEX), value, group = S_SEX))+
stat_summary(fun.data = 'mean_se')+
facet_grid(~ key)+
labs(x = "gender", y = "Negative Perception")
datP |>
gather(key, value, IS2G28I:IS2G28K) |>
ggplot(aes(as.factor(S_SEX), value, group = S_SEX))+
stat_summary(fun.data = 'mean_se')+
facet_grid(~ key)+
labs(x = "gender", y = "Expectation")
Model
library(lavaan); library(semTools);library(semPlot)
PMod <-'
PosP =~ IS2G28A + IS2G28B + IS2G28F + IS2G28G
NegP =~ IS2G28C + IS2G28D + IS2G28E + IS2G28H
ExeP =~ IS2G28I + IS2G28J + IS2G28K'資料屬於ordinal[1-4]，因此估計法採用WLSMV。
Base
# total
fitRes <- cfa(PMod, data=datP, estimator="WLSMV",
ordered=c("IS2G28A", "IS2G28B", "IS2G28F", "IS2G28G",
"IS2G28C", "IS2G28D", "IS2G28E", "IS2G28H",
"IS2G28I", "IS2G28J", "IS2G28K"),
std.lv = TRUE, meanstructure = TRUE)
summary(fitRes, fit.measures=T)## lavaan 0.6.16 ended normally after 21 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 47
##
## Number of observations 5074
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 647.500 875.206
## Degrees of freedom 41 41
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.746
## Shift parameter 7.782
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 52758.866 29036.063
## Degrees of freedom 55 55
## P-value 0.000 0.000
## Scaling correction factor 1.819
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.988 0.971
## Tucker-Lewis Index (TLI) 0.985 0.961
##
## Robust Comparative Fit Index (CFI) 0.950
## Robust Tucker-Lewis Index (TLI) 0.933
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.054 0.063
## 90 Percent confidence interval - lower 0.050 0.060
## 90 Percent confidence interval - upper 0.058 0.067
## P-value H_0: RMSEA <= 0.050 0.035 0.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.068
## 90 Percent confidence interval - lower 0.064
## 90 Percent confidence interval - upper 0.072
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.040 0.040
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 0.738 0.009 78.420 0.000
## IS2G28B 0.787 0.009 89.280 0.000
## IS2G28F 0.696 0.010 70.935 0.000
## IS2G28G 0.673 0.010 67.947 0.000
## NegP =~
## IS2G28C 0.573 0.013 43.971 0.000
## IS2G28D 0.523 0.013 40.363 0.000
## IS2G28E 0.784 0.012 65.732 0.000
## IS2G28H 0.627 0.012 52.560 0.000
## ExeP =~
## IS2G28I 0.833 0.007 125.079 0.000
## IS2G28J 0.878 0.006 139.815 0.000
## IS2G28K 0.763 0.008 100.266 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP -0.041 0.019 -2.199 0.028
## ExeP 0.517 0.013 39.967 0.000
## NegP ~~
## ExeP -0.019 0.018 -1.053 0.292
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP 0.000
## NegP 0.000
## ExeP 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A|t1 -1.991 0.038 -51.743 0.000
## IS2G28A|t2 -0.932 0.021 -45.084 0.000
## IS2G28A|t3 0.753 0.020 38.554 0.000
## IS2G28B|t1 -2.044 0.040 -50.784 0.000
## IS2G28B|t2 -1.086 0.022 -49.499 0.000
## IS2G28B|t3 0.535 0.019 28.867 0.000
## IS2G28F|t1 -2.016 0.039 -51.287 0.000
## IS2G28F|t2 -1.089 0.022 -49.565 0.000
## IS2G28F|t3 0.470 0.018 25.639 0.000
## IS2G28G|t1 -1.864 0.035 -53.647 0.000
## IS2G28G|t2 -0.861 0.020 -42.668 0.000
## IS2G28G|t3 0.615 0.019 32.616 0.000
## IS2G28C|t1 -1.466 0.027 -55.263 0.000
## IS2G28C|t2 -0.240 0.018 -13.519 0.000
## IS2G28C|t3 0.935 0.021 45.183 0.000
## IS2G28D|t1 -1.102 0.022 -49.896 0.000
## IS2G28D|t2 0.234 0.018 13.184 0.000
## IS2G28D|t3 1.191 0.023 51.833 0.000
## IS2G28E|t1 -1.534 0.028 -55.516 0.000
## IS2G28E|t2 -0.551 0.019 -29.609 0.000
## IS2G28E|t3 0.638 0.019 33.648 0.000
## IS2G28H|t1 -1.331 0.025 -54.066 0.000
## IS2G28H|t2 -0.306 0.018 -17.071 0.000
## IS2G28H|t3 0.929 0.021 44.985 0.000
## IS2G28I|t1 -1.060 0.022 -48.847 0.000
## IS2G28I|t2 0.042 0.018 2.358 0.018
## IS2G28I|t3 1.002 0.021 47.220 0.000
## IS2G28J|t1 -1.042 0.022 -48.367 0.000
## IS2G28J|t2 0.040 0.018 2.274 0.023
## IS2G28J|t3 1.022 0.021 47.809 0.000
## IS2G28K|t1 -1.476 0.027 -55.316 0.000
## IS2G28K|t2 -0.590 0.019 -31.443 0.000
## IS2G28K|t3 0.647 0.019 34.027 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.456
## .IS2G28B 0.380
## .IS2G28F 0.515
## .IS2G28G 0.547
## .IS2G28C 0.672
## .IS2G28D 0.726
## .IS2G28E 0.386
## .IS2G28H 0.607
## .IS2G28I 0.306
## .IS2G28J 0.229
## .IS2G28K 0.417
## PosP 1.000
## NegP 1.000
## ExeP 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 1.000
## IS2G28B 1.000
## IS2G28F 1.000
## IS2G28G 1.000
## IS2G28C 1.000
## IS2G28D 1.000
## IS2G28E 1.000
## IS2G28H 1.000
## IS2G28I 1.000
## IS2G28J 1.000
## IS2G28K 1.000## PosP NegP ExeP
## alpha 0.7483773 0.6681175 0.8111381
## alpha.ord 0.8094102 0.7186578 0.8568402
## omega 0.7486839 0.6778500 0.8230025
## omega2 0.7486839 0.6778500 0.8230025
## omega3 0.7549905 0.6776522 0.8309717
## avevar 0.5254963 0.4022300 0.6827472# plot
semPaths(fitRes, whatLabels = "std",
edge.label.cex = 1,
layout = "tree", rotation = 2, color = "lightblue")
# gender
fitResG <- cfa(PMod, data=datP, estimator="WLSMV",
ordered=c("IS2G28A", "IS2G28B", "IS2G28F", "IS2G28G",
"IS2G28C", "IS2G28D", "IS2G28E", "IS2G28H",
"IS2G28I", "IS2G28J", "IS2G28K"),
group = "S_SEX",
std.lv = TRUE, meanstructure = TRUE)
summary(fitResG, fit.measures=T)## lavaan 0.6.16 ended normally after 17 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 94
##
## Number of observations per group:
## m 2528
## f 2546
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 715.464 948.300
## Degrees of freedom 82 82
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.767
## Shift parameter 15.824
## simple second-order correction
## Test statistic for each group:
## m 354.129 469.426
## f 361.335 478.874
##
## Model Test Baseline Model:
##
## Test statistic 50760.158 28275.687
## Degrees of freedom 110 110
## P-value 0.000 0.000
## Scaling correction factor 1.798
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.987 0.969
## Tucker-Lewis Index (TLI) 0.983 0.959
##
## Robust Comparative Fit Index (CFI) 0.945
## Robust Tucker-Lewis Index (TLI) 0.927
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.055 0.065
## 90 Percent confidence interval - lower 0.052 0.061
## 90 Percent confidence interval - upper 0.059 0.068
## P-value H_0: RMSEA <= 0.050 0.010 0.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.070
## 90 Percent confidence interval - lower 0.066
## 90 Percent confidence interval - upper 0.075
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.043 0.043
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [m]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 0.753 0.012 61.575 0.000
## IS2G28B 0.813 0.011 74.024 0.000
## IS2G28F 0.721 0.013 54.494 0.000
## IS2G28G 0.698 0.013 53.965 0.000
## NegP =~
## IS2G28C 0.584 0.017 33.380 0.000
## IS2G28D 0.550 0.018 30.914 0.000
## IS2G28E 0.758 0.016 47.039 0.000
## IS2G28H 0.662 0.016 42.020 0.000
## ExeP =~
## IS2G28I 0.836 0.009 96.137 0.000
## IS2G28J 0.886 0.008 111.699 0.000
## IS2G28K 0.797 0.010 80.746 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP 0.031 0.025 1.242 0.214
## ExeP 0.587 0.016 35.918 0.000
## NegP ~~
## ExeP 0.045 0.025 1.774 0.076
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP 0.000
## NegP 0.000
## ExeP 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A|t1 -1.975 0.054 -36.707 0.000
## IS2G28A|t2 -1.052 0.031 -34.321 0.000
## IS2G28A|t3 0.565 0.026 21.378 0.000
## IS2G28B|t1 -2.011 0.055 -36.264 0.000
## IS2G28B|t2 -1.162 0.032 -36.169 0.000
## IS2G28B|t3 0.380 0.026 14.837 0.000
## IS2G28F|t1 -1.968 0.053 -36.788 0.000
## IS2G28F|t2 -1.109 0.031 -35.332 0.000
## IS2G28F|t3 0.308 0.025 12.151 0.000
## IS2G28G|t1 -1.809 0.047 -38.316 0.000
## IS2G28G|t2 -0.892 0.029 -30.861 0.000
## IS2G28G|t3 0.504 0.026 19.314 0.000
## IS2G28C|t1 -1.357 0.035 -38.374 0.000
## IS2G28C|t2 -0.159 0.025 -6.361 0.000
## IS2G28C|t3 0.955 0.030 32.339 0.000
## IS2G28D|t1 -1.002 0.030 -33.329 0.000
## IS2G28D|t2 0.286 0.025 11.319 0.000
## IS2G28D|t3 1.198 0.033 36.678 0.000
## IS2G28E|t1 -1.398 0.036 -38.661 0.000
## IS2G28E|t2 -0.418 0.026 -16.255 0.000
## IS2G28E|t3 0.749 0.028 27.079 0.000
## IS2G28H|t1 -1.235 0.033 -37.160 0.000
## IS2G28H|t2 -0.247 0.025 -9.814 0.000
## IS2G28H|t3 0.992 0.030 33.127 0.000
## IS2G28I|t1 -1.255 0.034 -37.390 0.000
## IS2G28I|t2 -0.233 0.025 -9.258 0.000
## IS2G28I|t3 0.801 0.028 28.532 0.000
## IS2G28J|t1 -1.210 0.033 -36.842 0.000
## IS2G28J|t2 -0.165 0.025 -6.599 0.000
## IS2G28J|t3 0.799 0.028 28.495 0.000
## IS2G28K|t1 -1.550 0.040 -39.200 0.000
## IS2G28K|t2 -0.692 0.027 -25.416 0.000
## IS2G28K|t3 0.471 0.026 18.140 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.433
## .IS2G28B 0.340
## .IS2G28F 0.480
## .IS2G28G 0.512
## .IS2G28C 0.659
## .IS2G28D 0.698
## .IS2G28E 0.425
## .IS2G28H 0.562
## .IS2G28I 0.302
## .IS2G28J 0.215
## .IS2G28K 0.365
## PosP 1.000
## NegP 1.000
## ExeP 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 1.000
## IS2G28B 1.000
## IS2G28F 1.000
## IS2G28G 1.000
## IS2G28C 1.000
## IS2G28D 1.000
## IS2G28E 1.000
## IS2G28H 1.000
## IS2G28I 1.000
## IS2G28J 1.000
## IS2G28K 1.000
##
##
## Group 2 [f]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 0.703 0.015 45.815 0.000
## IS2G28B 0.756 0.015 51.285 0.000
## IS2G28F 0.660 0.015 43.692 0.000
## IS2G28G 0.642 0.015 41.424 0.000
## NegP =~
## IS2G28C 0.570 0.019 29.525 0.000
## IS2G28D 0.495 0.019 26.007 0.000
## IS2G28E 0.785 0.018 43.199 0.000
## IS2G28H 0.591 0.018 32.222 0.000
## ExeP =~
## IS2G28I 0.815 0.011 71.867 0.000
## IS2G28J 0.859 0.011 77.427 0.000
## IS2G28K 0.710 0.012 58.299 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP -0.089 0.028 -3.194 0.001
## ExeP 0.387 0.021 18.154 0.000
## NegP ~~
## ExeP -0.026 0.026 -1.013 0.311
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP 0.000
## NegP 0.000
## ExeP 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A|t1 -2.007 0.055 -36.450 0.000
## IS2G28A|t2 -0.825 0.028 -29.287 0.000
## IS2G28A|t3 0.971 0.030 32.799 0.000
## IS2G28B|t1 -2.078 0.059 -35.494 0.000
## IS2G28B|t2 -1.016 0.030 -33.743 0.000
## IS2G28B|t3 0.704 0.027 25.862 0.000
## IS2G28F|t1 -2.070 0.058 -35.613 0.000
## IS2G28F|t2 -1.069 0.031 -34.751 0.000
## IS2G28F|t3 0.643 0.027 23.999 0.000
## IS2G28G|t1 -1.925 0.051 -37.399 0.000
## IS2G28G|t2 -0.832 0.028 -29.469 0.000
## IS2G28G|t3 0.733 0.027 26.728 0.000
## IS2G28C|t1 -1.595 0.041 -39.343 0.000
## IS2G28C|t2 -0.323 0.025 -12.738 0.000
## IS2G28C|t3 0.916 0.029 31.550 0.000
## IS2G28D|t1 -1.214 0.033 -37.028 0.000
## IS2G28D|t2 0.183 0.025 7.328 0.000
## IS2G28D|t3 1.184 0.032 36.616 0.000
## IS2G28E|t1 -1.706 0.044 -39.062 0.000
## IS2G28E|t2 -0.693 0.027 -25.522 0.000
## IS2G28E|t3 0.536 0.026 20.453 0.000
## IS2G28H|t1 -1.439 0.037 -39.029 0.000
## IS2G28H|t2 -0.364 0.025 -14.313 0.000
## IS2G28H|t3 0.870 0.029 30.447 0.000
## IS2G28I|t1 -0.901 0.029 -31.196 0.000
## IS2G28I|t2 0.317 0.025 12.541 0.000
## IS2G28I|t3 1.252 0.033 37.495 0.000
## IS2G28J|t1 -0.901 0.029 -31.196 0.000
## IS2G28J|t2 0.246 0.025 9.779 0.000
## IS2G28J|t3 1.310 0.034 38.109 0.000
## IS2G28K|t1 -1.410 0.036 -38.870 0.000
## IS2G28K|t2 -0.494 0.026 -19.014 0.000
## IS2G28K|t3 0.843 0.028 29.760 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.505
## .IS2G28B 0.429
## .IS2G28F 0.565
## .IS2G28G 0.588
## .IS2G28C 0.676
## .IS2G28D 0.755
## .IS2G28E 0.384
## .IS2G28H 0.651
## .IS2G28I 0.335
## .IS2G28J 0.263
## .IS2G28K 0.496
## PosP 1.000
## NegP 1.000
## ExeP 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 1.000
## IS2G28B 1.000
## IS2G28F 1.000
## IS2G28G 1.000
## IS2G28C 1.000
## IS2G28D 1.000
## IS2G28E 1.000
## IS2G28H 1.000
## IS2G28I 1.000
## IS2G28J 1.000
## IS2G28K 1.000## $m
## PosP NegP ExeP
## alpha 0.7724314 0.6798854 0.8261390
## alpha.ord 0.8302690 0.7292777 0.8712987
## omega 0.7710003 0.6909177 0.8356101
## omega2 0.7710003 0.6909177 0.8356101
## omega3 0.7754805 0.6932554 0.8423391
## avevar 0.5588009 0.4137162 0.7061027
##
## $f
## PosP NegP ExeP
## alpha 0.7100299 0.6476386 0.7789774
## alpha.ord 0.7764945 0.7006369 0.8278674
## omega 0.7128381 0.6566092 0.7917626
## omega2 0.7128381 0.6566092 0.7917626
## omega3 0.7218309 0.6559818 0.8002435
## avevar 0.4781747 0.3836946 0.6355555semPaths(fitResG, whatLabels = "std",
edge.label.cex = 1,
layout = "tree", rotation = 2, color = "lightblue")
# 因模型參數不同 不能直接comparefit
# 提取χ²值和自由度
chi2_fitRes <- fitmeasures(fitRes)[["chisq"]]
df_fitRes <- fitmeasures(fitRes)[["df"]]
chi2_fitResG <- fitmeasures(fitResG)[["chisq"]]
df_fitResG <- fitmeasures(fitResG)[["df"]]
# 計算CFI、TLI、RMSEA等
cfi_fitRes <- fitmeasures(fitRes)[["cfi"]]
tli_fitRes <- fitmeasures(fitRes)[["tli"]]
rmsea_fitRes <- fitmeasures(fitRes)[["rmsea"]]
cfi_fitResG <- fitmeasures(fitResG)[["cfi"]]
tli_fitResG <- fitmeasures(fitResG)[["tli"]]
rmsea_fitResG <- fitmeasures(fitResG)[["rmsea"]]
# 構建比較表
comparison_table <- data.frame(
Model = c("fitRes", "fitResG"),
ChiSquare = c(chi2_fitRes, chi2_fitResG),
DegreesOfFreedom = c(df_fitRes, df_fitResG),
CFI = c(cfi_fitRes, cfi_fitResG),
TLI = c(tli_fitRes, tli_fitResG),
RMSEA = c(rmsea_fitRes, rmsea_fitResG))
# 打印比較表
print(comparison_table, digits = 3)## Model ChiSquare DegreesOfFreedom CFI TLI RMSEA
## 1 fitRes 648 41 0.988 0.985 0.0540
## 2 fitResG 715 82 0.987 0.983 0.0552Alpha <- data.frame(round(rbind(semTools::reliability(fitRes, what = "alpha")[1,1:3],
semTools::reliability(fitResG, what = "alpha")$m[1,1:3],
semTools::reliability(fitResG, what = "alpha")$f[1,1:3]),2),
row.names = c("Whole", "Male", "Female"))
CR <- data.frame(round(rbind(semTools::reliability(fitRes, what = "omega3")[1,1:3],
semTools::reliability(fitResG, what = "omega3")$m[1,1:3],
semTools::reliability(fitResG, what = "omega3")$f[1,1:3]),2),
row.names = c("Whole", "Male", "Female"))
CR## PosP NegP ExeP
## Whole 0.75 0.68 0.83
## Male 0.78 0.69 0.84
## Female 0.72 0.66 0.80AVE <- data.frame(round(rbind(semTools::reliability(fitRes, what = "ave")[1,1:3],
semTools::reliability(fitResG, what = "ave")$m[1,1:3],
semTools::reliability(fitResG, what = "ave")$f[1,1:3]),2),
row.names = c("Whole", "Male", "Female"))
AVE## PosP NegP ExeP
## Whole 0.53 0.40 0.68
## Male 0.56 0.41 0.71
## Female 0.48 0.38 0.64若AVE > 0.5 alpha > 0.8 CR(omega3) > 0.8則良好
Configural
ConfiguralRes <- cfa(PMod, data = datP, estimator = "WLSMV",
ordered = c("IS2G28A", "IS2G28B", "IS2G28F", "IS2G28G",
"IS2G28C", "IS2G28D", "IS2G28E", "IS2G28H",
"IS2G28I", "IS2G28J", "IS2G28K"),
fixed.x = F, group= "S_SEX")
summary(ConfiguralRes, fit.measures=T)## lavaan 0.6.16 ended normally after 45 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 94
##
## Number of observations per group:
## m 2528
## f 2546
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 715.464 948.300
## Degrees of freedom 82 82
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.767
## Shift parameter 15.824
## simple second-order correction
## Test statistic for each group:
## m 354.129 469.426
## f 361.335 478.874
##
## Model Test Baseline Model:
##
## Test statistic 50760.158 28275.687
## Degrees of freedom 110 110
## P-value 0.000 0.000
## Scaling correction factor 1.798
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.987 0.969
## Tucker-Lewis Index (TLI) 0.983 0.959
##
## Robust Comparative Fit Index (CFI) 0.945
## Robust Tucker-Lewis Index (TLI) 0.927
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.055 0.065
## 90 Percent confidence interval - lower 0.052 0.061
## 90 Percent confidence interval - upper 0.059 0.068
## P-value H_0: RMSEA <= 0.050 0.010 0.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.070
## 90 Percent confidence interval - lower 0.066
## 90 Percent confidence interval - upper 0.075
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.043 0.043
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [m]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 1.000
## IS2G28B 1.080 0.023 47.180 0.000
## IS2G28F 0.958 0.022 43.832 0.000
## IS2G28G 0.928 0.021 43.536 0.000
## NegP =~
## IS2G28C 1.000
## IS2G28D 0.942 0.039 24.124 0.000
## IS2G28E 1.299 0.050 26.143 0.000
## IS2G28H 1.133 0.042 27.288 0.000
## ExeP =~
## IS2G28I 1.000
## IS2G28J 1.060 0.016 64.698 0.000
## IS2G28K 0.954 0.014 69.323 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP 0.014 0.011 1.230 0.219
## ExeP 0.369 0.014 26.801 0.000
## NegP ~~
## ExeP 0.022 0.012 1.756 0.079
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP 0.000
## NegP 0.000
## ExeP 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A|t1 -1.975 0.054 -36.707 0.000
## IS2G28A|t2 -1.052 0.031 -34.321 0.000
## IS2G28A|t3 0.565 0.026 21.378 0.000
## IS2G28B|t1 -2.011 0.055 -36.264 0.000
## IS2G28B|t2 -1.162 0.032 -36.169 0.000
## IS2G28B|t3 0.380 0.026 14.837 0.000
## IS2G28F|t1 -1.968 0.053 -36.788 0.000
## IS2G28F|t2 -1.109 0.031 -35.332 0.000
## IS2G28F|t3 0.308 0.025 12.151 0.000
## IS2G28G|t1 -1.809 0.047 -38.316 0.000
## IS2G28G|t2 -0.892 0.029 -30.861 0.000
## IS2G28G|t3 0.504 0.026 19.314 0.000
## IS2G28C|t1 -1.357 0.035 -38.374 0.000
## IS2G28C|t2 -0.159 0.025 -6.361 0.000
## IS2G28C|t3 0.955 0.030 32.339 0.000
## IS2G28D|t1 -1.002 0.030 -33.329 0.000
## IS2G28D|t2 0.286 0.025 11.319 0.000
## IS2G28D|t3 1.198 0.033 36.678 0.000
## IS2G28E|t1 -1.398 0.036 -38.661 0.000
## IS2G28E|t2 -0.418 0.026 -16.255 0.000
## IS2G28E|t3 0.749 0.028 27.079 0.000
## IS2G28H|t1 -1.235 0.033 -37.160 0.000
## IS2G28H|t2 -0.247 0.025 -9.814 0.000
## IS2G28H|t3 0.992 0.030 33.127 0.000
## IS2G28I|t1 -1.255 0.034 -37.390 0.000
## IS2G28I|t2 -0.233 0.025 -9.258 0.000
## IS2G28I|t3 0.801 0.028 28.532 0.000
## IS2G28J|t1 -1.210 0.033 -36.842 0.000
## IS2G28J|t2 -0.165 0.025 -6.599 0.000
## IS2G28J|t3 0.799 0.028 28.495 0.000
## IS2G28K|t1 -1.550 0.040 -39.200 0.000
## IS2G28K|t2 -0.692 0.027 -25.416 0.000
## IS2G28K|t3 0.471 0.026 18.140 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.433
## .IS2G28B 0.340
## .IS2G28F 0.480
## .IS2G28G 0.512
## .IS2G28C 0.659
## .IS2G28D 0.698
## .IS2G28E 0.425
## .IS2G28H 0.562
## .IS2G28I 0.302
## .IS2G28J 0.215
## .IS2G28K 0.365
## PosP 0.567 0.018 30.788 0.000
## NegP 0.341 0.020 16.690 0.000
## ExeP 0.698 0.015 48.069 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 1.000
## IS2G28B 1.000
## IS2G28F 1.000
## IS2G28G 1.000
## IS2G28C 1.000
## IS2G28D 1.000
## IS2G28E 1.000
## IS2G28H 1.000
## IS2G28I 1.000
## IS2G28J 1.000
## IS2G28K 1.000
##
##
## Group 2 [f]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 1.000
## IS2G28B 1.074 0.033 32.122 0.000
## IS2G28F 0.938 0.028 33.520 0.000
## IS2G28G 0.912 0.028 32.316 0.000
## NegP =~
## IS2G28C 1.000
## IS2G28D 0.869 0.043 20.387 0.000
## IS2G28E 1.378 0.062 22.256 0.000
## IS2G28H 1.038 0.046 22.718 0.000
## ExeP =~
## IS2G28I 1.000
## IS2G28J 1.053 0.024 43.249 0.000
## IS2G28K 0.871 0.017 50.574 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP -0.036 0.011 -3.205 0.001
## ExeP 0.222 0.014 15.829 0.000
## NegP ~~
## ExeP -0.012 0.012 -1.013 0.311
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP 0.000
## NegP 0.000
## ExeP 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A|t1 -2.007 0.055 -36.450 0.000
## IS2G28A|t2 -0.825 0.028 -29.287 0.000
## IS2G28A|t3 0.971 0.030 32.799 0.000
## IS2G28B|t1 -2.078 0.059 -35.494 0.000
## IS2G28B|t2 -1.016 0.030 -33.743 0.000
## IS2G28B|t3 0.704 0.027 25.862 0.000
## IS2G28F|t1 -2.070 0.058 -35.613 0.000
## IS2G28F|t2 -1.069 0.031 -34.751 0.000
## IS2G28F|t3 0.643 0.027 23.999 0.000
## IS2G28G|t1 -1.925 0.051 -37.399 0.000
## IS2G28G|t2 -0.832 0.028 -29.469 0.000
## IS2G28G|t3 0.733 0.027 26.728 0.000
## IS2G28C|t1 -1.595 0.041 -39.343 0.000
## IS2G28C|t2 -0.323 0.025 -12.738 0.000
## IS2G28C|t3 0.916 0.029 31.550 0.000
## IS2G28D|t1 -1.214 0.033 -37.028 0.000
## IS2G28D|t2 0.183 0.025 7.328 0.000
## IS2G28D|t3 1.184 0.032 36.616 0.000
## IS2G28E|t1 -1.706 0.044 -39.062 0.000
## IS2G28E|t2 -0.693 0.027 -25.522 0.000
## IS2G28E|t3 0.536 0.026 20.453 0.000
## IS2G28H|t1 -1.439 0.037 -39.029 0.000
## IS2G28H|t2 -0.364 0.025 -14.313 0.000
## IS2G28H|t3 0.870 0.029 30.447 0.000
## IS2G28I|t1 -0.901 0.029 -31.196 0.000
## IS2G28I|t2 0.317 0.025 12.541 0.000
## IS2G28I|t3 1.252 0.033 37.495 0.000
## IS2G28J|t1 -0.901 0.029 -31.196 0.000
## IS2G28J|t2 0.246 0.025 9.779 0.000
## IS2G28J|t3 1.310 0.034 38.109 0.000
## IS2G28K|t1 -1.410 0.036 -38.870 0.000
## IS2G28K|t2 -0.494 0.026 -19.014 0.000
## IS2G28K|t3 0.843 0.028 29.760 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.505
## .IS2G28B 0.429
## .IS2G28F 0.565
## .IS2G28G 0.588
## .IS2G28C 0.676
## .IS2G28D 0.755
## .IS2G28E 0.384
## .IS2G28H 0.651
## .IS2G28I 0.335
## .IS2G28J 0.263
## .IS2G28K 0.496
## PosP 0.495 0.022 22.908 0.000
## NegP 0.324 0.022 14.763 0.000
## ExeP 0.665 0.019 35.934 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 1.000
## IS2G28B 1.000
## IS2G28F 1.000
## IS2G28G 1.000
## IS2G28C 1.000
## IS2G28D 1.000
## IS2G28E 1.000
## IS2G28H 1.000
## IS2G28I 1.000
## IS2G28J 1.000
## IS2G28K 1.000PP NP EP的
covariance代表構念間有相關，是正常的，因為相同子量表下的變項。
但不要高度相關，因為可能是相同變項；也可注意變項相關方向是否正確(正相關、附相關)
假設每個題目都是一個常態分配(所以四個點的分數有+-)
IS2G28A
此題可以有4個點 -0.56 1.05 1.975
在不同能力的人會掉入不同的分數值域
Metric
# 1
MetricRes <- cfa(PMod, data = datP, estimator = "WLSMV",
ordered = c("IS2G28A", "IS2G28B", "IS2G28F", "IS2G28G",
"IS2G28C", "IS2G28D", "IS2G28E", "IS2G28H",
"IS2G28I", "IS2G28J", "IS2G28K"),
fixed.x = F, group= "S_SEX",
group.equal=c("loadings"))
summary(MetricRes, fit.measures=T)## lavaan 0.6.16 ended normally after 35 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 94
## Number of equality constraints 8
##
## Number of observations per group:
## m 2528
## f 2546
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 735.401 953.677
## Degrees of freedom 90 90
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.785
## Shift parameter 16.719
## simple second-order correction
## Test statistic for each group:
## m 361.770 469.252
## f 373.632 484.424
##
## Model Test Baseline Model:
##
## Test statistic 50760.158 28275.687
## Degrees of freedom 110 110
## P-value 0.000 0.000
## Scaling correction factor 1.798
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.987 0.969
## Tucker-Lewis Index (TLI) 0.984 0.963
##
## Robust Comparative Fit Index (CFI) 0.944
## Robust Tucker-Lewis Index (TLI) 0.932
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.053 0.062
## 90 Percent confidence interval - lower 0.050 0.058
## 90 Percent confidence interval - upper 0.057 0.065
## P-value H_0: RMSEA <= 0.050 0.069 0.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.068
## 90 Percent confidence interval - lower 0.064
## 90 Percent confidence interval - upper 0.072
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.043 0.043
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [m]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 1.000
## IS2G28B (.p2.) 1.078 0.019 57.057 0.000
## IS2G28F (.p3.) 0.951 0.017 55.119 0.000
## IS2G28G (.p4.) 0.923 0.017 54.246 0.000
## NegP =~
## IS2G28C 1.000
## IS2G28D (.p6.) 0.908 0.029 31.534 0.000
## IS2G28E (.p7.) 1.331 0.039 34.326 0.000
## IS2G28H (.p8.) 1.089 0.031 35.482 0.000
## ExeP =~
## IS2G28I 1.000
## IS2G28J (.10.) 1.058 0.014 77.899 0.000
## IS2G28K (.11.) 0.924 0.011 86.000 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP 0.013 0.011 1.187 0.235
## ExeP 0.374 0.014 27.566 0.000
## NegP ~~
## ExeP 0.021 0.013 1.712 0.087
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP 0.000
## NegP 0.000
## ExeP 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A|t1 -1.975 0.054 -36.707 0.000
## IS2G28A|t2 -1.052 0.031 -34.321 0.000
## IS2G28A|t3 0.565 0.026 21.378 0.000
## IS2G28B|t1 -2.011 0.055 -36.264 0.000
## IS2G28B|t2 -1.162 0.032 -36.169 0.000
## IS2G28B|t3 0.380 0.026 14.837 0.000
## IS2G28F|t1 -1.968 0.053 -36.788 0.000
## IS2G28F|t2 -1.109 0.031 -35.332 0.000
## IS2G28F|t3 0.308 0.025 12.151 0.000
## IS2G28G|t1 -1.809 0.047 -38.316 0.000
## IS2G28G|t2 -0.892 0.029 -30.861 0.000
## IS2G28G|t3 0.504 0.026 19.314 0.000
## IS2G28C|t1 -1.357 0.035 -38.374 0.000
## IS2G28C|t2 -0.159 0.025 -6.361 0.000
## IS2G28C|t3 0.955 0.030 32.339 0.000
## IS2G28D|t1 -1.002 0.030 -33.329 0.000
## IS2G28D|t2 0.286 0.025 11.319 0.000
## IS2G28D|t3 1.198 0.033 36.678 0.000
## IS2G28E|t1 -1.398 0.036 -38.661 0.000
## IS2G28E|t2 -0.418 0.026 -16.255 0.000
## IS2G28E|t3 0.749 0.028 27.079 0.000
## IS2G28H|t1 -1.235 0.033 -37.160 0.000
## IS2G28H|t2 -0.247 0.025 -9.814 0.000
## IS2G28H|t3 0.992 0.030 33.127 0.000
## IS2G28I|t1 -1.255 0.034 -37.390 0.000
## IS2G28I|t2 -0.233 0.025 -9.258 0.000
## IS2G28I|t3 0.801 0.028 28.532 0.000
## IS2G28J|t1 -1.210 0.033 -36.842 0.000
## IS2G28J|t2 -0.165 0.025 -6.599 0.000
## IS2G28J|t3 0.799 0.028 28.495 0.000
## IS2G28K|t1 -1.550 0.040 -39.200 0.000
## IS2G28K|t2 -0.692 0.027 -25.416 0.000
## IS2G28K|t3 0.471 0.026 18.140 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.430
## .IS2G28B 0.337
## .IS2G28F 0.484
## .IS2G28G 0.514
## .IS2G28C 0.654
## .IS2G28D 0.714
## .IS2G28E 0.386
## .IS2G28H 0.589
## .IS2G28I 0.290
## .IS2G28J 0.206
## .IS2G28K 0.394
## PosP 0.570 0.016 35.042 0.000
## NegP 0.346 0.017 20.680 0.000
## ExeP 0.710 0.013 54.740 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 1.000
## IS2G28B 1.000
## IS2G28F 1.000
## IS2G28G 1.000
## IS2G28C 1.000
## IS2G28D 1.000
## IS2G28E 1.000
## IS2G28H 1.000
## IS2G28I 1.000
## IS2G28J 1.000
## IS2G28K 1.000
##
##
## Group 2 [f]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 1.000
## IS2G28B (.p2.) 1.078 0.019 57.057 0.000
## IS2G28F (.p3.) 0.951 0.017 55.119 0.000
## IS2G28G (.p4.) 0.923 0.017 54.246 0.000
## NegP =~
## IS2G28C 1.000
## IS2G28D (.p6.) 0.908 0.029 31.534 0.000
## IS2G28E (.p7.) 1.331 0.039 34.326 0.000
## IS2G28H (.p8.) 1.089 0.031 35.482 0.000
## ExeP =~
## IS2G28I 1.000
## IS2G28J (.10.) 1.058 0.014 77.899 0.000
## IS2G28K (.11.) 0.924 0.011 86.000 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP -0.035 0.011 -3.164 0.002
## ExeP 0.218 0.013 16.476 0.000
## NegP ~~
## ExeP -0.011 0.012 -0.950 0.342
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP 0.000
## NegP 0.000
## ExeP 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A|t1 -2.007 0.055 -36.450 0.000
## IS2G28A|t2 -0.825 0.028 -29.287 0.000
## IS2G28A|t3 0.971 0.030 32.799 0.000
## IS2G28B|t1 -2.078 0.059 -35.494 0.000
## IS2G28B|t2 -1.016 0.030 -33.743 0.000
## IS2G28B|t3 0.704 0.027 25.862 0.000
## IS2G28F|t1 -2.070 0.058 -35.613 0.000
## IS2G28F|t2 -1.069 0.031 -34.751 0.000
## IS2G28F|t3 0.643 0.027 23.999 0.000
## IS2G28G|t1 -1.925 0.051 -37.399 0.000
## IS2G28G|t2 -0.832 0.028 -29.469 0.000
## IS2G28G|t3 0.733 0.027 26.728 0.000
## IS2G28C|t1 -1.595 0.041 -39.343 0.000
## IS2G28C|t2 -0.323 0.025 -12.738 0.000
## IS2G28C|t3 0.916 0.029 31.550 0.000
## IS2G28D|t1 -1.214 0.033 -37.028 0.000
## IS2G28D|t2 0.183 0.025 7.328 0.000
## IS2G28D|t3 1.184 0.032 36.616 0.000
## IS2G28E|t1 -1.706 0.044 -39.062 0.000
## IS2G28E|t2 -0.693 0.027 -25.522 0.000
## IS2G28E|t3 0.536 0.026 20.453 0.000
## IS2G28H|t1 -1.439 0.037 -39.029 0.000
## IS2G28H|t2 -0.364 0.025 -14.313 0.000
## IS2G28H|t3 0.870 0.029 30.447 0.000
## IS2G28I|t1 -0.901 0.029 -31.196 0.000
## IS2G28I|t2 0.317 0.025 12.541 0.000
## IS2G28I|t3 1.252 0.033 37.495 0.000
## IS2G28J|t1 -0.901 0.029 -31.196 0.000
## IS2G28J|t2 0.246 0.025 9.779 0.000
## IS2G28J|t3 1.310 0.034 38.109 0.000
## IS2G28K|t1 -1.410 0.036 -38.870 0.000
## IS2G28K|t2 -0.494 0.026 -19.014 0.000
## IS2G28K|t3 0.843 0.028 29.760 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.512
## .IS2G28B 0.433
## .IS2G28F 0.558
## .IS2G28G 0.584
## .IS2G28C 0.681
## .IS2G28D 0.737
## .IS2G28E 0.434
## .IS2G28H 0.621
## .IS2G28I 0.354
## .IS2G28J 0.277
## .IS2G28K 0.449
## PosP 0.488 0.016 31.382 0.000
## NegP 0.319 0.016 20.423 0.000
## ExeP 0.646 0.013 50.912 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 1.000
## IS2G28B 1.000
## IS2G28F 1.000
## IS2G28G 1.000
## IS2G28C 1.000
## IS2G28D 1.000
## IS2G28E 1.000
## IS2G28H 1.000
## IS2G28I 1.000
## IS2G28J 1.000
## IS2G28K 1.000##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## ConfiguralRes 82 715.46
## MetricRes 90 735.40 23.352 8 0.002941 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 19.724 8 0.011
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p2. == .p88. 0.068 1 0.794
## 2 .p3. == .p89. 0.225 1 0.635
## 3 .p4. == .p90. 0.067 1 0.796
## 4 .p6. == .p92. 1.715 1 0.190
## 5 .p7. == .p93. 4.496 1 0.034
## 6 .p8. == .p94. 2.561 1 0.110
## 7 .p10. == .p96. 2.960 1 0.085
## 8 .p11. == .p97. 11.916 1 0.001# p11 = IS2G28K release ExeP =~ IS2G28K
# 2
PartialMetricRes <- cfa(PMod, data = datP, estimator = "WLSMV",
ordered = c("IS2G28A", "IS2G28B", "IS2G28F", "IS2G28G",
"IS2G28C", "IS2G28D", "IS2G28E", "IS2G28H",
"IS2G28I", "IS2G28J", "IS2G28K"),
fixed.x = F, group= "S_SEX",
group.equal=c("loadings"),
group.partial=c("ExeP =~ IS2G28K"))
summary(PartialMetricRes, fit.measures=T)## lavaan 0.6.16 ended normally after 35 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 94
## Number of equality constraints 7
##
## Number of observations per group:
## m 2528
## f 2546
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 723.150 937.275
## Degrees of freedom 89 89
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.786
## Shift parameter 16.663
## simple second-order correction
## Test statistic for each group:
## m 357.485 463.402
## f 365.665 473.874
##
## Model Test Baseline Model:
##
## Test statistic 50760.158 28275.687
## Degrees of freedom 110 110
## P-value 0.000 0.000
## Scaling correction factor 1.798
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.987 0.970
## Tucker-Lewis Index (TLI) 0.985 0.963
##
## Robust Comparative Fit Index (CFI) 0.945
## Robust Tucker-Lewis Index (TLI) 0.933
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.053 0.061
## 90 Percent confidence interval - lower 0.049 0.058
## 90 Percent confidence interval - upper 0.057 0.065
## P-value H_0: RMSEA <= 0.050 0.081 0.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.067
## 90 Percent confidence interval - lower 0.063
## 90 Percent confidence interval - upper 0.072
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.043 0.043
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [m]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 1.000
## IS2G28B (.p2.) 1.078 0.019 57.081 0.000
## IS2G28F (.p3.) 0.951 0.017 55.113 0.000
## IS2G28G (.p4.) 0.922 0.017 54.234 0.000
## NegP =~
## IS2G28C 1.000
## IS2G28D (.p6.) 0.908 0.029 31.533 0.000
## IS2G28E (.p7.) 1.331 0.039 34.325 0.000
## IS2G28H (.p8.) 1.089 0.031 35.481 0.000
## ExeP =~
## IS2G28I 1.000
## IS2G28J (.10.) 1.058 0.014 77.823 0.000
## IS2G28K 0.953 0.013 72.182 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP 0.013 0.011 1.187 0.235
## ExeP 0.371 0.013 27.672 0.000
## NegP ~~
## ExeP 0.021 0.012 1.713 0.087
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP 0.000
## NegP 0.000
## ExeP 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A|t1 -1.975 0.054 -36.707 0.000
## IS2G28A|t2 -1.052 0.031 -34.321 0.000
## IS2G28A|t3 0.565 0.026 21.378 0.000
## IS2G28B|t1 -2.011 0.055 -36.264 0.000
## IS2G28B|t2 -1.162 0.032 -36.169 0.000
## IS2G28B|t3 0.380 0.026 14.837 0.000
## IS2G28F|t1 -1.968 0.053 -36.788 0.000
## IS2G28F|t2 -1.109 0.031 -35.332 0.000
## IS2G28F|t3 0.308 0.025 12.151 0.000
## IS2G28G|t1 -1.809 0.047 -38.316 0.000
## IS2G28G|t2 -0.892 0.029 -30.861 0.000
## IS2G28G|t3 0.504 0.026 19.314 0.000
## IS2G28C|t1 -1.357 0.035 -38.374 0.000
## IS2G28C|t2 -0.159 0.025 -6.361 0.000
## IS2G28C|t3 0.955 0.030 32.339 0.000
## IS2G28D|t1 -1.002 0.030 -33.329 0.000
## IS2G28D|t2 0.286 0.025 11.319 0.000
## IS2G28D|t3 1.198 0.033 36.678 0.000
## IS2G28E|t1 -1.398 0.036 -38.661 0.000
## IS2G28E|t2 -0.418 0.026 -16.255 0.000
## IS2G28E|t3 0.749 0.028 27.079 0.000
## IS2G28H|t1 -1.235 0.033 -37.160 0.000
## IS2G28H|t2 -0.247 0.025 -9.814 0.000
## IS2G28H|t3 0.992 0.030 33.127 0.000
## IS2G28I|t1 -1.255 0.034 -37.390 0.000
## IS2G28I|t2 -0.233 0.025 -9.258 0.000
## IS2G28I|t3 0.801 0.028 28.532 0.000
## IS2G28J|t1 -1.210 0.033 -36.842 0.000
## IS2G28J|t2 -0.165 0.025 -6.599 0.000
## IS2G28J|t3 0.799 0.028 28.495 0.000
## IS2G28K|t1 -1.550 0.040 -39.200 0.000
## IS2G28K|t2 -0.692 0.027 -25.416 0.000
## IS2G28K|t3 0.471 0.026 18.140 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.430
## .IS2G28B 0.338
## .IS2G28F 0.484
## .IS2G28G 0.515
## .IS2G28C 0.654
## .IS2G28D 0.714
## .IS2G28E 0.386
## .IS2G28H 0.589
## .IS2G28I 0.300
## .IS2G28J 0.217
## .IS2G28K 0.365
## PosP 0.570 0.016 35.036 0.000
## NegP 0.346 0.017 20.680 0.000
## ExeP 0.700 0.013 54.155 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 1.000
## IS2G28B 1.000
## IS2G28F 1.000
## IS2G28G 1.000
## IS2G28C 1.000
## IS2G28D 1.000
## IS2G28E 1.000
## IS2G28H 1.000
## IS2G28I 1.000
## IS2G28J 1.000
## IS2G28K 1.000
##
##
## Group 2 [f]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 1.000
## IS2G28B (.p2.) 1.078 0.019 57.081 0.000
## IS2G28F (.p3.) 0.951 0.017 55.113 0.000
## IS2G28G (.p4.) 0.922 0.017 54.234 0.000
## NegP =~
## IS2G28C 1.000
## IS2G28D (.p6.) 0.908 0.029 31.533 0.000
## IS2G28E (.p7.) 1.331 0.039 34.325 0.000
## IS2G28H (.p8.) 1.089 0.031 35.481 0.000
## ExeP =~
## IS2G28I 1.000
## IS2G28J (.10.) 1.058 0.014 77.823 0.000
## IS2G28K 0.873 0.016 56.141 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP -0.035 0.011 -3.164 0.002
## ExeP 0.220 0.013 16.485 0.000
## NegP ~~
## ExeP -0.011 0.012 -0.962 0.336
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP 0.000
## NegP 0.000
## ExeP 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A|t1 -2.007 0.055 -36.450 0.000
## IS2G28A|t2 -0.825 0.028 -29.287 0.000
## IS2G28A|t3 0.971 0.030 32.799 0.000
## IS2G28B|t1 -2.078 0.059 -35.494 0.000
## IS2G28B|t2 -1.016 0.030 -33.743 0.000
## IS2G28B|t3 0.704 0.027 25.862 0.000
## IS2G28F|t1 -2.070 0.058 -35.613 0.000
## IS2G28F|t2 -1.069 0.031 -34.751 0.000
## IS2G28F|t3 0.643 0.027 23.999 0.000
## IS2G28G|t1 -1.925 0.051 -37.399 0.000
## IS2G28G|t2 -0.832 0.028 -29.469 0.000
## IS2G28G|t3 0.733 0.027 26.728 0.000
## IS2G28C|t1 -1.595 0.041 -39.343 0.000
## IS2G28C|t2 -0.323 0.025 -12.738 0.000
## IS2G28C|t3 0.916 0.029 31.550 0.000
## IS2G28D|t1 -1.214 0.033 -37.028 0.000
## IS2G28D|t2 0.183 0.025 7.328 0.000
## IS2G28D|t3 1.184 0.032 36.616 0.000
## IS2G28E|t1 -1.706 0.044 -39.062 0.000
## IS2G28E|t2 -0.693 0.027 -25.522 0.000
## IS2G28E|t3 0.536 0.026 20.453 0.000
## IS2G28H|t1 -1.439 0.037 -39.029 0.000
## IS2G28H|t2 -0.364 0.025 -14.313 0.000
## IS2G28H|t3 0.870 0.029 30.447 0.000
## IS2G28I|t1 -0.901 0.029 -31.196 0.000
## IS2G28I|t2 0.317 0.025 12.541 0.000
## IS2G28I|t3 1.252 0.033 37.495 0.000
## IS2G28J|t1 -0.901 0.029 -31.196 0.000
## IS2G28J|t2 0.246 0.025 9.779 0.000
## IS2G28J|t3 1.310 0.034 38.109 0.000
## IS2G28K|t1 -1.410 0.036 -38.870 0.000
## IS2G28K|t2 -0.494 0.026 -19.014 0.000
## IS2G28K|t3 0.843 0.028 29.760 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.512
## .IS2G28B 0.433
## .IS2G28F 0.558
## .IS2G28G 0.584
## .IS2G28C 0.681
## .IS2G28D 0.737
## .IS2G28E 0.434
## .IS2G28H 0.621
## .IS2G28I 0.338
## .IS2G28J 0.259
## .IS2G28K 0.496
## PosP 0.488 0.016 31.391 0.000
## NegP 0.319 0.016 20.423 0.000
## ExeP 0.662 0.013 52.094 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 1.000
## IS2G28B 1.000
## IS2G28F 1.000
## IS2G28G 1.000
## IS2G28C 1.000
## IS2G28D 1.000
## IS2G28E 1.000
## IS2G28H 1.000
## IS2G28I 1.000
## IS2G28J 1.000
## IS2G28K 1.000##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## ConfiguralRes 82 715.46
## PartialMetricRes 89 723.15 8.8617 7 0.2627Scalar
# 1
PartailScalarRes <- cfa(PMod, data = datP, estimator = "WLSMV",
ordered = c("IS2G28A", "IS2G28B", "IS2G28F", "IS2G28G",
"IS2G28C", "IS2G28D", "IS2G28E", "IS2G28H",
"IS2G28I", "IS2G28J", "IS2G28K"),
fixed.x = F, group= "S_SEX",
group.equal=c("loadings", "thresholds"),
group.partial=c("ExeP =~ IS2G28K"))
summary(PartailScalarRes, fit.measures=T)## lavaan 0.6.16 ended normally after 55 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 108
## Number of equality constraints 40
##
## Number of observations per group:
## m 2528
## f 2546
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 769.105 1004.803
## Degrees of freedom 108 108
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.780
## Shift parameter 18.692
## simple second-order correction
## Test statistic for each group:
## m 383.832 501.444
## f 385.273 503.359
##
## Model Test Baseline Model:
##
## Test statistic 50760.158 28275.687
## Degrees of freedom 110 110
## P-value 0.000 0.000
## Scaling correction factor 1.798
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.987 0.968
## Tucker-Lewis Index (TLI) 0.987 0.968
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.049 0.057
## 90 Percent confidence interval - lower 0.046 0.054
## 90 Percent confidence interval - upper 0.052 0.060
## P-value H_0: RMSEA <= 0.050 0.663 0.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.043 0.043
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [m]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 1.000
## IS2G28B (.p2.) 1.072 0.020 54.781 0.000
## IS2G28F (.p3.) 0.942 0.018 50.914 0.000
## IS2G28G (.p4.) 0.913 0.018 50.558 0.000
## NegP =~
## IS2G28C 1.000
## IS2G28D (.p6.) 0.916 0.031 29.801 0.000
## IS2G28E (.p7.) 1.343 0.042 31.971 0.000
## IS2G28H (.p8.) 1.120 0.033 33.926 0.000
## ExeP =~
## IS2G28I 1.000
## IS2G28J (.10.) 1.046 0.014 74.483 0.000
## IS2G28K 0.948 0.013 71.372 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP 0.013 0.011 1.187 0.235
## ExeP 0.375 0.014 27.766 0.000
## NegP ~~
## ExeP 0.021 0.012 1.717 0.086
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP 0.000
## NegP 0.000
## ExeP 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A (.12.) -1.978 0.046 -43.123 0.000
## IS2G28A (.13.) -0.990 0.027 -37.192 0.000
## IS2G28A (.14.) 0.590 0.024 24.179 0.000
## IS2G28B (.15.) -2.033 0.048 -42.759 0.000
## IS2G28B (.16.) -1.143 0.029 -39.645 0.000
## IS2G28B (.17.) 0.370 0.024 15.635 0.000
## IS2G28F (.18.) -1.979 0.045 -44.021 0.000
## IS2G28F (.19.) -1.123 0.027 -41.423 0.000
## IS2G28F (.20.) 0.321 0.022 14.431 0.000
## IS2G28G (.21.) -1.845 0.040 -45.876 0.000
## IS2G28G (.22.) -0.915 0.024 -37.426 0.000
## IS2G28G (.23.) 0.460 0.023 19.928 0.000
## IS2G28C (.24.) -1.342 0.031 -42.618 0.000
## IS2G28C (.25.) -0.158 0.020 -7.903 0.000
## IS2G28C (.26.) 0.968 0.025 38.167 0.000
## IS2G28D (.27.) -1.005 0.026 -38.721 0.000
## IS2G28D (.28.) 0.293 0.020 14.689 0.000
## IS2G28D (.29.) 1.217 0.029 42.675 0.000
## IS2G28E (.30.) -1.389 0.034 -41.258 0.000
## IS2G28E (.31.) -0.434 0.023 -18.923 0.000
## IS2G28E (.32.) 0.708 0.025 27.927 0.000
## IS2G28H (.33.) -1.244 0.030 -41.844 0.000
## IS2G28H (.34.) -0.224 0.021 -10.620 0.000
## IS2G28H (.35.) 1.000 0.027 37.542 0.000
## IS2G28I (.36.) -1.248 0.030 -40.993 0.000
## IS2G28I (.37.) -0.162 0.022 -7.251 0.000
## IS2G28I (.38.) 0.792 0.027 29.490 0.000
## IS2G28J (.39.) -1.236 0.031 -40.503 0.000
## IS2G28J (.40.) -0.177 0.023 -7.789 0.000
## IS2G28J (.41.) 0.801 0.027 29.262 0.000
## IS2G28K (.42.) -1.541 0.036 -42.281 0.000
## IS2G28K (.43.) -0.722 0.024 -30.309 0.000
## IS2G28K (.44.) 0.441 0.023 19.552 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.423
## .IS2G28B 0.337
## .IS2G28F 0.488
## .IS2G28G 0.519
## .IS2G28C 0.662
## .IS2G28D 0.716
## .IS2G28E 0.390
## .IS2G28H 0.576
## .IS2G28I 0.292
## .IS2G28J 0.225
## .IS2G28K 0.364
## PosP 0.577 0.017 34.338 0.000
## NegP 0.338 0.017 19.569 0.000
## ExeP 0.708 0.013 52.468 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 1.000
## IS2G28B 1.000
## IS2G28F 1.000
## IS2G28G 1.000
## IS2G28C 1.000
## IS2G28D 1.000
## IS2G28E 1.000
## IS2G28H 1.000
## IS2G28I 1.000
## IS2G28J 1.000
## IS2G28K 1.000
##
##
## Group 2 [f]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 1.000
## IS2G28B (.p2.) 1.072 0.020 54.781 0.000
## IS2G28F (.p3.) 0.942 0.018 50.914 0.000
## IS2G28G (.p4.) 0.913 0.018 50.558 0.000
## NegP =~
## IS2G28C 1.000
## IS2G28D (.p6.) 0.916 0.031 29.801 0.000
## IS2G28E (.p7.) 1.343 0.042 31.971 0.000
## IS2G28H (.p8.) 1.120 0.033 33.926 0.000
## ExeP =~
## IS2G28I 1.000
## IS2G28J (.10.) 1.046 0.014 74.483 0.000
## IS2G28K 0.794 0.026 30.472 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP -0.028 0.009 -3.167 0.002
## ExeP 0.180 0.014 13.172 0.000
## NegP ~~
## ExeP -0.010 0.010 -0.986 0.324
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP -0.232 0.023 -10.103 0.000
## NegP 0.139 0.019 7.247 0.000
## ExeP -0.397 0.026 -15.083 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A (.12.) -1.978 0.046 -43.123 0.000
## IS2G28A (.13.) -0.990 0.027 -37.192 0.000
## IS2G28A (.14.) 0.590 0.024 24.179 0.000
## IS2G28B (.15.) -2.033 0.048 -42.759 0.000
## IS2G28B (.16.) -1.143 0.029 -39.645 0.000
## IS2G28B (.17.) 0.370 0.024 15.635 0.000
## IS2G28F (.18.) -1.979 0.045 -44.021 0.000
## IS2G28F (.19.) -1.123 0.027 -41.423 0.000
## IS2G28F (.20.) 0.321 0.022 14.431 0.000
## IS2G28G (.21.) -1.845 0.040 -45.876 0.000
## IS2G28G (.22.) -0.915 0.024 -37.426 0.000
## IS2G28G (.23.) 0.460 0.023 19.928 0.000
## IS2G28C (.24.) -1.342 0.031 -42.618 0.000
## IS2G28C (.25.) -0.158 0.020 -7.903 0.000
## IS2G28C (.26.) 0.968 0.025 38.167 0.000
## IS2G28D (.27.) -1.005 0.026 -38.721 0.000
## IS2G28D (.28.) 0.293 0.020 14.689 0.000
## IS2G28D (.29.) 1.217 0.029 42.675 0.000
## IS2G28E (.30.) -1.389 0.034 -41.258 0.000
## IS2G28E (.31.) -0.434 0.023 -18.923 0.000
## IS2G28E (.32.) 0.708 0.025 27.927 0.000
## IS2G28H (.33.) -1.244 0.030 -41.844 0.000
## IS2G28H (.34.) -0.224 0.021 -10.620 0.000
## IS2G28H (.35.) 1.000 0.027 37.542 0.000
## IS2G28I (.36.) -1.248 0.030 -40.993 0.000
## IS2G28I (.37.) -0.162 0.022 -7.251 0.000
## IS2G28I (.38.) 0.792 0.027 29.490 0.000
## IS2G28J (.39.) -1.236 0.031 -40.503 0.000
## IS2G28J (.40.) -0.177 0.023 -7.789 0.000
## IS2G28J (.41.) 0.801 0.027 29.262 0.000
## IS2G28K (.42.) -1.541 0.036 -42.281 0.000
## IS2G28K (.43.) -0.722 0.024 -30.309 0.000
## IS2G28K (.44.) 0.441 0.023 19.552 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.389
## .IS2G28B 0.328
## .IS2G28F 0.403
## .IS2G28G 0.440
## .IS2G28C 0.569
## .IS2G28D 0.644
## .IS2G28E 0.347
## .IS2G28H 0.617
## .IS2G28I 0.297
## .IS2G28J 0.221
## .IS2G28K 0.377
## PosP 0.370 0.022 17.107 0.000
## NegP 0.273 0.018 15.230 0.000
## ExeP 0.587 0.033 17.732 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 1.148 0.028 41.713 0.000
## IS2G28B 1.153 0.029 39.906 0.000
## IS2G28F 1.169 0.028 42.199 0.000
## IS2G28G 1.156 0.027 43.339 0.000
## IS2G28C 1.090 0.025 43.025 0.000
## IS2G28D 1.070 0.024 44.399 0.000
## IS2G28E 1.091 0.028 39.603 0.000
## IS2G28H 1.021 0.024 43.249 0.000
## IS2G28I 1.064 0.027 39.408 0.000
## IS2G28J 1.076 0.028 38.560 0.000
## IS2G28K 1.156 0.030 38.237 0.000##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## PartialMetricRes 89 723.15
## PartailScalarRes 108 769.11 65.504 19 5.056e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 53.57 40 0.074
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p2. == .p88. 0.262 1 0.609
## 2 .p3. == .p89. 1.494 1 0.222
## 3 .p4. == .p90. 1.063 1 0.302
## 4 .p6. == .p92. 2.899 1 0.089
## 5 .p7. == .p93. 5.485 1 0.019
## 6 .p8. == .p94. 0.439 1 0.507
## 7 .p10. == .p96. 3.114 1 0.078
## 8 .p12. == .p98. 0.008 1 0.930
## 9 .p13. == .p99. 9.084 1 0.003
## 10 .p14. == .p100. 2.806 1 0.094
## 11 .p15. == .p101. 0.390 1 0.532
## 12 .p16. == .p102. 0.869 1 0.351
## 13 .p17. == .p103. 0.387 1 0.534
## 14 .p18. == .p104. 0.106 1 0.745
## 15 .p19. == .p105. 0.463 1 0.496
## 16 .p20. == .p106. 0.653 1 0.419
## 17 .p21. == .p107. 1.596 1 0.206
## 18 .p22. == .p108. 1.473 1 0.225
## 19 .p23. == .p109. 7.788 1 0.005
## 20 .p24. == .p110. 0.758 1 0.384
## 21 .p25. == .p111. 0.004 1 0.950
## 22 .p26. == .p112. 0.585 1 0.444
## 23 .p27. == .p113. 0.035 1 0.851
## 24 .p28. == .p114. 0.131 1 0.718
## 25 .p29. == .p115. 1.164 1 0.281
## 26 .p30. == .p116. 0.284 1 0.594
## 27 .p31. == .p117. 0.953 1 0.329
## 28 .p32. == .p118. 6.366 1 0.012
## 29 .p33. == .p119. 0.297 1 0.585
## 30 .p34. == .p120. 1.981 1 0.159
## 31 .p35. == .p121. 0.256 1 0.613
## 32 .p36. == .p122. 0.111 1 0.739
## 33 .p37. == .p123. 18.291 1 0.000
## 34 .p38. == .p124. 0.442 1 0.506
## 35 .p39. == .p125. 2.053 1 0.152
## 36 .p40. == .p126. 0.510 1 0.475
## 37 .p41. == .p127. 0.010 1 0.920
## 38 .p42. == .p128. 0.261 1 0.610
## 39 .p43. == .p129. 3.044 1 0.081
## 40 .p44. == .p130. 3.624 1 0.057# .p37. == .p123.
# seek
para.tab <- parametertable(PartailScalarRes)
para.tab[para.tab$label == '.p37.' & para.tab$plabel == '.p123.',]## id lhs op rhs user block group free ustart exo label plabel start
## 123 123 IS2G28I | t2 0 2 2 81 NA 0 .p37. .p123. 0.317
## est se
## 123 -0.162 0.022# is IS2G28I | t2
# 2
PartailScalarRes2 <- cfa(PMod, data = datP, estimator = "WLSMV",
ordered = c("IS2G28A", "IS2G28B", "IS2G28F", "IS2G28G",
"IS2G28C", "IS2G28D", "IS2G28E", "IS2G28H",
"IS2G28I", "IS2G28J", "IS2G28K"),
fixed.x = F, group= "S_SEX",
group.equal=c("loadings", "thresholds"),
group.partial=c("ExeP =~ IS2G28K","IS2G28I | t2"))
summary(PartailScalarRes2, fit.measures=T)## lavaan 0.6.16 ended normally after 53 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 108
## Number of equality constraints 39
##
## Number of observations per group:
## m 2528
## f 2546
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 750.797 979.639
## Degrees of freedom 107 107
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.781
## Shift parameter 18.527
## simple second-order correction
## Test statistic for each group:
## m 373.871 487.831
## f 376.926 491.808
##
## Model Test Baseline Model:
##
## Test statistic 50760.158 28275.687
## Degrees of freedom 110 110
## P-value 0.000 0.000
## Scaling correction factor 1.798
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.987 0.969
## Tucker-Lewis Index (TLI) 0.987 0.968
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.049 0.057
## 90 Percent confidence interval - lower 0.045 0.053
## 90 Percent confidence interval - upper 0.052 0.060
## P-value H_0: RMSEA <= 0.050 0.735 0.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.043 0.043
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [m]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 1.000
## IS2G28B (.p2.) 1.072 0.020 54.782 0.000
## IS2G28F (.p3.) 0.942 0.018 50.914 0.000
## IS2G28G (.p4.) 0.913 0.018 50.558 0.000
## NegP =~
## IS2G28C 1.000
## IS2G28D (.p6.) 0.916 0.031 29.801 0.000
## IS2G28E (.p7.) 1.343 0.042 31.971 0.000
## IS2G28H (.p8.) 1.120 0.033 33.925 0.000
## ExeP =~
## IS2G28I 1.000
## IS2G28J (.10.) 1.052 0.014 73.732 0.000
## IS2G28K 0.950 0.013 71.370 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP 0.013 0.011 1.187 0.235
## ExeP 0.374 0.013 27.724 0.000
## NegP ~~
## ExeP 0.021 0.012 1.715 0.086
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP 0.000
## NegP 0.000
## ExeP 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A (.12.) -1.978 0.046 -43.123 0.000
## IS2G28A (.13.) -0.990 0.027 -37.192 0.000
## IS2G28A (.14.) 0.590 0.024 24.179 0.000
## IS2G28B (.15.) -2.033 0.048 -42.759 0.000
## IS2G28B (.16.) -1.143 0.029 -39.645 0.000
## IS2G28B (.17.) 0.370 0.024 15.635 0.000
## IS2G28F (.18.) -1.979 0.045 -44.021 0.000
## IS2G28F (.19.) -1.123 0.027 -41.423 0.000
## IS2G28F (.20.) 0.321 0.022 14.431 0.000
## IS2G28G (.21.) -1.845 0.040 -45.876 0.000
## IS2G28G (.22.) -0.915 0.024 -37.426 0.000
## IS2G28G (.23.) 0.460 0.023 19.928 0.000
## IS2G28C (.24.) -1.342 0.031 -42.618 0.000
## IS2G28C (.25.) -0.158 0.020 -7.903 0.000
## IS2G28C (.26.) 0.968 0.025 38.167 0.000
## IS2G28D (.27.) -1.005 0.026 -38.721 0.000
## IS2G28D (.28.) 0.293 0.020 14.689 0.000
## IS2G28D (.29.) 1.217 0.029 42.675 0.000
## IS2G28E (.30.) -1.389 0.034 -41.259 0.000
## IS2G28E (.31.) -0.434 0.023 -18.923 0.000
## IS2G28E (.32.) 0.708 0.025 27.927 0.000
## IS2G28H (.33.) -1.244 0.030 -41.844 0.000
## IS2G28H (.34.) -0.224 0.021 -10.620 0.000
## IS2G28H (.35.) 1.000 0.027 37.542 0.000
## IS2G28I (.36.) -1.232 0.031 -40.357 0.000
## IS2G28I -0.233 0.025 -9.258 0.000
## IS2G28I (.38.) 0.805 0.027 29.884 0.000
## IS2G28J (.39.) -1.218 0.031 -39.768 0.000
## IS2G28J (.40.) -0.163 0.023 -7.110 0.000
## IS2G28J (.41.) 0.811 0.027 29.556 0.000
## IS2G28K (.42.) -1.539 0.036 -42.219 0.000
## IS2G28K (.43.) -0.715 0.024 -29.896 0.000
## IS2G28K (.44.) 0.455 0.023 19.982 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.423
## .IS2G28B 0.337
## .IS2G28F 0.488
## .IS2G28G 0.519
## .IS2G28C 0.662
## .IS2G28D 0.716
## .IS2G28E 0.390
## .IS2G28H 0.576
## .IS2G28I 0.296
## .IS2G28J 0.221
## .IS2G28K 0.364
## PosP 0.577 0.017 34.339 0.000
## NegP 0.338 0.017 19.569 0.000
## ExeP 0.704 0.014 52.094 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 1.000
## IS2G28B 1.000
## IS2G28F 1.000
## IS2G28G 1.000
## IS2G28C 1.000
## IS2G28D 1.000
## IS2G28E 1.000
## IS2G28H 1.000
## IS2G28I 1.000
## IS2G28J 1.000
## IS2G28K 1.000
##
##
## Group 2 [f]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 1.000
## IS2G28B (.p2.) 1.072 0.020 54.782 0.000
## IS2G28F (.p3.) 0.942 0.018 50.914 0.000
## IS2G28G (.p4.) 0.913 0.018 50.558 0.000
## NegP =~
## IS2G28C 1.000
## IS2G28D (.p6.) 0.916 0.031 29.801 0.000
## IS2G28E (.p7.) 1.343 0.042 31.971 0.000
## IS2G28H (.p8.) 1.120 0.033 33.925 0.000
## ExeP =~
## IS2G28I 1.000
## IS2G28J (.10.) 1.052 0.014 73.732 0.000
## IS2G28K 0.810 0.027 30.223 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP -0.028 0.009 -3.167 0.002
## ExeP 0.180 0.014 13.174 0.000
## NegP ~~
## ExeP -0.010 0.010 -0.983 0.325
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP -0.232 0.023 -10.103 0.000
## NegP 0.139 0.019 7.247 0.000
## ExeP -0.369 0.027 -13.854 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A (.12.) -1.978 0.046 -43.123 0.000
## IS2G28A (.13.) -0.990 0.027 -37.192 0.000
## IS2G28A (.14.) 0.590 0.024 24.179 0.000
## IS2G28B (.15.) -2.033 0.048 -42.759 0.000
## IS2G28B (.16.) -1.143 0.029 -39.645 0.000
## IS2G28B (.17.) 0.370 0.024 15.635 0.000
## IS2G28F (.18.) -1.979 0.045 -44.021 0.000
## IS2G28F (.19.) -1.123 0.027 -41.423 0.000
## IS2G28F (.20.) 0.321 0.022 14.431 0.000
## IS2G28G (.21.) -1.845 0.040 -45.876 0.000
## IS2G28G (.22.) -0.915 0.024 -37.426 0.000
## IS2G28G (.23.) 0.460 0.023 19.928 0.000
## IS2G28C (.24.) -1.342 0.031 -42.618 0.000
## IS2G28C (.25.) -0.158 0.020 -7.903 0.000
## IS2G28C (.26.) 0.968 0.025 38.167 0.000
## IS2G28D (.27.) -1.005 0.026 -38.721 0.000
## IS2G28D (.28.) 0.293 0.020 14.689 0.000
## IS2G28D (.29.) 1.217 0.029 42.675 0.000
## IS2G28E (.30.) -1.389 0.034 -41.259 0.000
## IS2G28E (.31.) -0.434 0.023 -18.923 0.000
## IS2G28E (.32.) 0.708 0.025 27.927 0.000
## IS2G28H (.33.) -1.244 0.030 -41.844 0.000
## IS2G28H (.34.) -0.224 0.021 -10.620 0.000
## IS2G28H (.35.) 1.000 0.027 37.542 0.000
## IS2G28I (.36.) -1.232 0.031 -40.357 0.000
## IS2G28I -0.070 0.028 -2.503 0.012
## IS2G28I (.38.) 0.805 0.027 29.884 0.000
## IS2G28J (.39.) -1.218 0.031 -39.768 0.000
## IS2G28J (.40.) -0.163 0.023 -7.110 0.000
## IS2G28J (.41.) 0.811 0.027 29.556 0.000
## IS2G28K (.42.) -1.539 0.036 -42.219 0.000
## IS2G28K (.43.) -0.715 0.024 -29.896 0.000
## IS2G28K (.44.) 0.455 0.023 19.982 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.389
## .IS2G28B 0.328
## .IS2G28F 0.403
## .IS2G28G 0.440
## .IS2G28C 0.569
## .IS2G28D 0.644
## .IS2G28E 0.347
## .IS2G28H 0.617
## .IS2G28I 0.305
## .IS2G28J 0.214
## .IS2G28K 0.384
## PosP 0.370 0.022 17.107 0.000
## NegP 0.273 0.018 15.230 0.000
## ExeP 0.582 0.033 17.702 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 1.148 0.028 41.713 0.000
## IS2G28B 1.153 0.029 39.906 0.000
## IS2G28F 1.169 0.028 42.199 0.000
## IS2G28G 1.156 0.027 43.339 0.000
## IS2G28C 1.090 0.025 43.025 0.000
## IS2G28D 1.070 0.024 44.399 0.000
## IS2G28E 1.091 0.028 39.603 0.000
## IS2G28H 1.021 0.024 43.249 0.000
## IS2G28I 1.062 0.027 39.377 0.000
## IS2G28J 1.079 0.028 38.476 0.000
## IS2G28K 1.143 0.030 38.145 0.000##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## PartialMetricRes 89 723.15
## PartailScalarRes2 107 750.80 39.541 18 0.002409 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 35.276 39 0.64
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p2. == .p88. 0.262 1 0.609
## 2 .p3. == .p89. 1.493 1 0.222
## 3 .p4. == .p90. 1.063 1 0.303
## 4 .p6. == .p92. 2.899 1 0.089
## 5 .p7. == .p93. 5.482 1 0.019
## 6 .p8. == .p94. 0.439 1 0.508
## 7 .p10. == .p96. 1.124 1 0.289
## 8 .p12. == .p98. 0.008 1 0.929
## 9 .p13. == .p99. 9.082 1 0.003
## 10 .p14. == .p100. 2.807 1 0.094
## 11 .p15. == .p101. 0.390 1 0.532
## 12 .p16. == .p102. 0.869 1 0.351
## 13 .p17. == .p103. 0.387 1 0.534
## 14 .p18. == .p104. 0.106 1 0.745
## 15 .p19. == .p105. 0.463 1 0.496
## 16 .p20. == .p106. 0.653 1 0.419
## 17 .p21. == .p107. 1.595 1 0.207
## 18 .p22. == .p108. 1.473 1 0.225
## 19 .p23. == .p109. 7.788 1 0.005
## 20 .p24. == .p110. 0.758 1 0.384
## 21 .p25. == .p111. 0.004 1 0.950
## 22 .p26. == .p112. 0.585 1 0.444
## 23 .p27. == .p113. 0.035 1 0.851
## 24 .p28. == .p114. 0.131 1 0.718
## 25 .p29. == .p115. 1.164 1 0.281
## 26 .p30. == .p116. 0.283 1 0.595
## 27 .p31. == .p117. 0.953 1 0.329
## 28 .p32. == .p118. 6.365 1 0.012
## 29 .p33. == .p119. 0.297 1 0.586
## 30 .p34. == .p120. 1.981 1 0.159
## 31 .p35. == .p121. 0.256 1 0.613
## 32 .p36. == .p122. 1.512 1 0.219
## 33 .p38. == .p124. 0.109 1 0.741
## 34 .p39. == .p125. 0.188 1 0.664
## 35 .p40. == .p126. 0.031 1 0.861
## 36 .p41. == .p127. 0.801 1 0.371
## 37 .p42. == .p128. 0.343 1 0.558
## 38 .p43. == .p129. 1.769 1 0.183
## 39 .p44. == .p130. 1.132 1 0.287# .p13. == .p99. 9.082
para.tab <- parametertable(PartailScalarRes2)
para.tab[para.tab$label == '.p13.' & para.tab$plabel == '.p99.',]## id lhs op rhs user block group free ustart exo label plabel start est
## 99 99 IS2G28A | t2 0 2 2 57 NA 0 .p13. .p99. -0.825 -0.99
## se
## 99 0.027# IS2G28A | t2
# 3
PartailScalarRes3 <- cfa(PMod, data = datP, estimator = "WLSMV",
ordered = c("IS2G28A", "IS2G28B", "IS2G28F", "IS2G28G",
"IS2G28C", "IS2G28D", "IS2G28E", "IS2G28H",
"IS2G28I", "IS2G28J", "IS2G28K"),
fixed.x = F, group= "S_SEX",
group.equal=c("loadings", "thresholds"),
group.partial=c("ExeP =~ IS2G28K","IS2G28I | t2", "IS2G28A | t2"))
summary(PartailScalarRes3, fit.measures=T)## lavaan 0.6.16 ended normally after 52 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 108
## Number of equality constraints 38
##
## Number of observations per group:
## m 2528
## f 2546
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 741.717 969.588
## Degrees of freedom 106 106
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.780
## Shift parameter 18.402
## simple second-order correction
## Test statistic for each group:
## m 368.436 481.655
## f 373.281 487.933
##
## Model Test Baseline Model:
##
## Test statistic 50760.158 28275.687
## Degrees of freedom 110 110
## P-value 0.000 0.000
## Scaling correction factor 1.798
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.987 0.969
## Tucker-Lewis Index (TLI) 0.987 0.968
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.049 0.057
## 90 Percent confidence interval - lower 0.045 0.053
## 90 Percent confidence interval - upper 0.052 0.060
## P-value H_0: RMSEA <= 0.050 0.747 0.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.043 0.043
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [m]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 1.000
## IS2G28B (.p2.) 1.085 0.020 53.504 0.000
## IS2G28F (.p3.) 0.953 0.019 49.728 0.000
## IS2G28G (.p4.) 0.924 0.019 49.562 0.000
## NegP =~
## IS2G28C 1.000
## IS2G28D (.p6.) 0.916 0.031 29.800 0.000
## IS2G28E (.p7.) 1.343 0.042 31.970 0.000
## IS2G28H (.p8.) 1.120 0.033 33.925 0.000
## ExeP =~
## IS2G28I 1.000
## IS2G28J (.10.) 1.052 0.014 73.712 0.000
## IS2G28K 0.950 0.013 71.379 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP 0.013 0.011 1.186 0.236
## ExeP 0.371 0.013 27.579 0.000
## NegP ~~
## ExeP 0.021 0.012 1.715 0.086
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP 0.000
## NegP 0.000
## ExeP 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A (.12.) -1.941 0.047 -40.903 0.000
## IS2G28A -1.052 0.031 -34.321 0.000
## IS2G28A (.14.) 0.586 0.024 24.088 0.000
## IS2G28B (.15.) -2.024 0.048 -42.278 0.000
## IS2G28B (.16.) -1.134 0.029 -38.850 0.000
## IS2G28B (.17.) 0.377 0.024 15.820 0.000
## IS2G28F (.18.) -1.970 0.045 -43.573 0.000
## IS2G28F (.19.) -1.115 0.027 -40.661 0.000
## IS2G28F (.20.) 0.327 0.022 14.623 0.000
## IS2G28G (.21.) -1.837 0.041 -45.328 0.000
## IS2G28G (.22.) -0.907 0.025 -36.793 0.000
## IS2G28G (.23.) 0.465 0.023 20.114 0.000
## IS2G28C (.24.) -1.342 0.031 -42.619 0.000
## IS2G28C (.25.) -0.158 0.020 -7.903 0.000
## IS2G28C (.26.) 0.968 0.025 38.167 0.000
## IS2G28D (.27.) -1.005 0.026 -38.721 0.000
## IS2G28D (.28.) 0.293 0.020 14.689 0.000
## IS2G28D (.29.) 1.217 0.029 42.675 0.000
## IS2G28E (.30.) -1.389 0.034 -41.258 0.000
## IS2G28E (.31.) -0.434 0.023 -18.922 0.000
## IS2G28E (.32.) 0.708 0.025 27.927 0.000
## IS2G28H (.33.) -1.244 0.030 -41.844 0.000
## IS2G28H (.34.) -0.224 0.021 -10.620 0.000
## IS2G28H (.35.) 1.000 0.027 37.542 0.000
## IS2G28I (.36.) -1.232 0.031 -40.357 0.000
## IS2G28I -0.233 0.025 -9.258 0.000
## IS2G28I (.38.) 0.805 0.027 29.884 0.000
## IS2G28J (.39.) -1.218 0.031 -39.768 0.000
## IS2G28J (.40.) -0.163 0.023 -7.110 0.000
## IS2G28J (.41.) 0.811 0.027 29.556 0.000
## IS2G28K (.42.) -1.539 0.036 -42.219 0.000
## IS2G28K (.43.) -0.715 0.024 -29.896 0.000
## IS2G28K (.44.) 0.455 0.023 19.983 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.433
## .IS2G28B 0.332
## .IS2G28F 0.485
## .IS2G28G 0.516
## .IS2G28C 0.662
## .IS2G28D 0.716
## .IS2G28E 0.390
## .IS2G28H 0.576
## .IS2G28I 0.296
## .IS2G28J 0.221
## .IS2G28K 0.364
## PosP 0.567 0.017 33.596 0.000
## NegP 0.338 0.017 19.568 0.000
## ExeP 0.704 0.014 52.091 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 1.000
## IS2G28B 1.000
## IS2G28F 1.000
## IS2G28G 1.000
## IS2G28C 1.000
## IS2G28D 1.000
## IS2G28E 1.000
## IS2G28H 1.000
## IS2G28I 1.000
## IS2G28J 1.000
## IS2G28K 1.000
##
##
## Group 2 [f]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 1.000
## IS2G28B (.p2.) 1.085 0.020 53.504 0.000
## IS2G28F (.p3.) 0.953 0.019 49.728 0.000
## IS2G28G (.p4.) 0.924 0.019 49.562 0.000
## NegP =~
## IS2G28C 1.000
## IS2G28D (.p6.) 0.916 0.031 29.800 0.000
## IS2G28E (.p7.) 1.343 0.042 31.970 0.000
## IS2G28H (.p8.) 1.120 0.033 33.925 0.000
## ExeP =~
## IS2G28I 1.000
## IS2G28J (.10.) 1.052 0.014 73.712 0.000
## IS2G28K 0.810 0.027 30.222 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP -0.027 0.009 -3.168 0.002
## ExeP 0.176 0.013 13.087 0.000
## NegP ~~
## ExeP -0.010 0.010 -0.983 0.325
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP -0.217 0.023 -9.279 0.000
## NegP 0.139 0.019 7.247 0.000
## ExeP -0.369 0.027 -13.854 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A (.12.) -1.941 0.047 -40.903 0.000
## IS2G28A -0.916 0.035 -25.862 0.000
## IS2G28A (.14.) 0.586 0.024 24.088 0.000
## IS2G28B (.15.) -2.024 0.048 -42.278 0.000
## IS2G28B (.16.) -1.134 0.029 -38.850 0.000
## IS2G28B (.17.) 0.377 0.024 15.820 0.000
## IS2G28F (.18.) -1.970 0.045 -43.573 0.000
## IS2G28F (.19.) -1.115 0.027 -40.661 0.000
## IS2G28F (.20.) 0.327 0.022 14.623 0.000
## IS2G28G (.21.) -1.837 0.041 -45.328 0.000
## IS2G28G (.22.) -0.907 0.025 -36.793 0.000
## IS2G28G (.23.) 0.465 0.023 20.114 0.000
## IS2G28C (.24.) -1.342 0.031 -42.619 0.000
## IS2G28C (.25.) -0.158 0.020 -7.903 0.000
## IS2G28C (.26.) 0.968 0.025 38.167 0.000
## IS2G28D (.27.) -1.005 0.026 -38.721 0.000
## IS2G28D (.28.) 0.293 0.020 14.689 0.000
## IS2G28D (.29.) 1.217 0.029 42.675 0.000
## IS2G28E (.30.) -1.389 0.034 -41.258 0.000
## IS2G28E (.31.) -0.434 0.023 -18.922 0.000
## IS2G28E (.32.) 0.708 0.025 27.927 0.000
## IS2G28H (.33.) -1.244 0.030 -41.844 0.000
## IS2G28H (.34.) -0.224 0.021 -10.620 0.000
## IS2G28H (.35.) 1.000 0.027 37.542 0.000
## IS2G28I (.36.) -1.232 0.031 -40.357 0.000
## IS2G28I -0.070 0.028 -2.503 0.012
## IS2G28I (.38.) 0.805 0.027 29.884 0.000
## IS2G28J (.39.) -1.218 0.031 -39.768 0.000
## IS2G28J (.40.) -0.163 0.023 -7.110 0.000
## IS2G28J (.41.) 0.811 0.027 29.556 0.000
## IS2G28K (.42.) -1.539 0.036 -42.219 0.000
## IS2G28K (.43.) -0.715 0.024 -29.896 0.000
## IS2G28K (.44.) 0.455 0.023 19.983 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.360
## .IS2G28B 0.330
## .IS2G28F 0.403
## .IS2G28G 0.440
## .IS2G28C 0.569
## .IS2G28D 0.644
## .IS2G28E 0.347
## .IS2G28H 0.617
## .IS2G28I 0.305
## .IS2G28J 0.214
## .IS2G28K 0.384
## PosP 0.357 0.022 16.534 0.000
## NegP 0.273 0.018 15.229 0.000
## ExeP 0.582 0.033 17.702 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 1.181 0.031 38.042 0.000
## IS2G28B 1.155 0.029 39.825 0.000
## IS2G28F 1.172 0.028 42.032 0.000
## IS2G28G 1.159 0.027 43.162 0.000
## IS2G28C 1.090 0.025 43.025 0.000
## IS2G28D 1.070 0.024 44.399 0.000
## IS2G28E 1.091 0.028 39.603 0.000
## IS2G28H 1.021 0.024 43.249 0.000
## IS2G28I 1.062 0.027 39.377 0.000
## IS2G28J 1.079 0.028 38.475 0.000
## IS2G28K 1.143 0.030 38.145 0.000##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## PartialMetricRes 89 723.15
## PartailScalarRes3 106 741.72 26.941 17 0.05895 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Strict Invariance
fixing one factor loarding to 1 to fixing the latent factor’s
varaicne to 1
https://groups.google.com/g/lavaan/c/ivqyRRVR5cQ
# use parameterization = "theta"
PartailStrictRes <- cfa(PMod, data = datP, estimator = "WLSMV",
ordered = c("IS2G28A", "IS2G28B", "IS2G28F", "IS2G28G",
"IS2G28C", "IS2G28D", "IS2G28E", "IS2G28H",
"IS2G28I", "IS2G28J", "IS2G28K"),
fixed.x = F, group= "S_SEX",
group.equal=c("loadings", "thresholds"),
group.partial=c("ExeP =~ IS2G28K","IS2G28I | t2", "IS2G28A | t2"),
parameterization = "theta")
summary(PartailStrictRes, fit.measures=T)## lavaan 0.6.16 ended normally after 145 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 108
## Number of equality constraints 38
##
## Number of observations per group:
## m 2528
## f 2546
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 741.717 969.587
## Degrees of freedom 106 106
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.780
## Shift parameter 18.402
## simple second-order correction
## Test statistic for each group:
## m 368.436 481.655
## f 373.280 487.932
##
## Model Test Baseline Model:
##
## Test statistic 50760.158 28275.687
## Degrees of freedom 110 110
## P-value 0.000 0.000
## Scaling correction factor 1.798
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.987 0.969
## Tucker-Lewis Index (TLI) 0.987 0.968
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.049 0.057
## 90 Percent confidence interval - lower 0.045 0.053
## 90 Percent confidence interval - upper 0.052 0.060
## P-value H_0: RMSEA <= 0.050 0.747 0.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.043 0.043
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [m]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 1.000
## IS2G28B (.p2.) 1.238 0.061 20.437 0.000
## IS2G28F (.p3.) 0.901 0.039 22.880 0.000
## IS2G28G (.p4.) 0.846 0.036 23.530 0.000
## NegP =~
## IS2G28C 1.000
## IS2G28D (.p6.) 0.881 0.043 20.572 0.000
## IS2G28E (.p7.) 1.750 0.106 16.563 0.000
## IS2G28H (.p8.) 1.201 0.057 21.176 0.000
## ExeP =~
## IS2G28I 1.000
## IS2G28J (.10.) 1.218 0.065 18.868 0.000
## IS2G28K 0.857 0.036 23.910 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP 0.025 0.021 1.177 0.239
## ExeP 1.036 0.064 16.304 0.000
## NegP ~~
## ExeP 0.048 0.028 1.702 0.089
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP 0.000
## NegP 0.000
## ExeP 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A (.12.) -2.951 0.087 -33.741 0.000
## IS2G28A -1.599 0.052 -30.797 0.000
## IS2G28A (.14.) 0.891 0.038 23.407 0.000
## IS2G28B (.15.) -3.510 0.106 -33.019 0.000
## IS2G28B (.16.) -1.967 0.065 -30.384 0.000
## IS2G28B (.17.) 0.653 0.042 15.483 0.000
## IS2G28F (.18.) -2.830 0.075 -37.804 0.000
## IS2G28F (.19.) -1.601 0.046 -34.526 0.000
## IS2G28F (.20.) 0.469 0.032 14.686 0.000
## IS2G28G (.21.) -2.558 0.063 -40.830 0.000
## IS2G28G (.22.) -1.263 0.038 -33.268 0.000
## IS2G28G (.23.) 0.647 0.032 20.050 0.000
## IS2G28C (.24.) -1.649 0.042 -39.429 0.000
## IS2G28C (.25.) -0.194 0.025 -7.897 0.000
## IS2G28C (.26.) 1.190 0.034 35.108 0.000
## IS2G28D (.27.) -1.187 0.032 -37.366 0.000
## IS2G28D (.28.) 0.346 0.024 14.424 0.000
## IS2G28D (.29.) 1.438 0.036 39.488 0.000
## IS2G28E (.30.) -2.224 0.077 -28.749 0.000
## IS2G28E (.31.) -0.695 0.041 -17.017 0.000
## IS2G28E (.32.) 1.133 0.050 22.633 0.000
## IS2G28H (.33.) -1.640 0.042 -39.056 0.000
## IS2G28H (.34.) -0.296 0.028 -10.590 0.000
## IS2G28H (.35.) 1.319 0.039 34.185 0.000
## IS2G28I (.36.) -2.263 0.068 -33.321 0.000
## IS2G28I -0.428 0.047 -9.153 0.000
## IS2G28I (.38.) 1.479 0.053 28.137 0.000
## IS2G28J (.39.) -2.590 0.087 -29.920 0.000
## IS2G28J (.40.) -0.346 0.049 -7.003 0.000
## IS2G28J (.41.) 1.725 0.070 24.644 0.000
## IS2G28K (.42.) -2.550 0.065 -39.475 0.000
## IS2G28K (.43.) -1.184 0.042 -27.940 0.000
## IS2G28K (.44.) 0.753 0.037 20.177 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 1.000
## .IS2G28B 1.000
## .IS2G28F 1.000
## .IS2G28G 1.000
## .IS2G28C 1.000
## .IS2G28D 1.000
## .IS2G28E 1.000
## .IS2G28H 1.000
## .IS2G28I 1.000
## .IS2G28J 1.000
## .IS2G28K 1.000
## PosP 1.311 0.090 14.539 0.000
## NegP 0.511 0.039 12.952 0.000
## ExeP 2.374 0.154 15.437 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 0.658
## IS2G28B 0.577
## IS2G28F 0.696
## IS2G28G 0.718
## IS2G28C 0.814
## IS2G28D 0.846
## IS2G28E 0.624
## IS2G28H 0.759
## IS2G28I 0.544
## IS2G28J 0.470
## IS2G28K 0.604
##
##
## Group 2 [f]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PosP =~
## IS2G28A 1.000
## IS2G28B (.p2.) 1.238 0.061 20.437 0.000
## IS2G28F (.p3.) 0.901 0.039 22.880 0.000
## IS2G28G (.p4.) 0.846 0.036 23.530 0.000
## NegP =~
## IS2G28C 1.000
## IS2G28D (.p6.) 0.881 0.043 20.572 0.000
## IS2G28E (.p7.) 1.750 0.106 16.563 0.000
## IS2G28H (.p8.) 1.201 0.057 21.176 0.000
## ExeP =~
## IS2G28I 1.000
## IS2G28J (.10.) 1.218 0.065 18.868 0.000
## IS2G28K 0.730 0.031 23.461 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PosP ~~
## NegP -0.051 0.016 -3.154 0.002
## ExeP 0.492 0.040 12.226 0.000
## NegP ~~
## ExeP -0.023 0.023 -0.983 0.326
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.000
## .IS2G28B 0.000
## .IS2G28F 0.000
## .IS2G28G 0.000
## .IS2G28C 0.000
## .IS2G28D 0.000
## .IS2G28E 0.000
## .IS2G28H 0.000
## .IS2G28I 0.000
## .IS2G28J 0.000
## .IS2G28K 0.000
## PosP -0.330 0.037 -8.932 0.000
## NegP 0.170 0.024 7.161 0.000
## ExeP -0.677 0.052 -13.136 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A (.12.) -2.951 0.087 -33.741 0.000
## IS2G28A -1.392 0.062 -22.515 0.000
## IS2G28A (.14.) 0.891 0.038 23.407 0.000
## IS2G28B (.15.) -3.510 0.106 -33.019 0.000
## IS2G28B (.16.) -1.967 0.065 -30.384 0.000
## IS2G28B (.17.) 0.653 0.042 15.483 0.000
## IS2G28F (.18.) -2.830 0.075 -37.804 0.000
## IS2G28F (.19.) -1.601 0.046 -34.526 0.000
## IS2G28F (.20.) 0.469 0.032 14.686 0.000
## IS2G28G (.21.) -2.558 0.063 -40.830 0.000
## IS2G28G (.22.) -1.263 0.038 -33.268 0.000
## IS2G28G (.23.) 0.647 0.032 20.050 0.000
## IS2G28C (.24.) -1.649 0.042 -39.429 0.000
## IS2G28C (.25.) -0.194 0.025 -7.897 0.000
## IS2G28C (.26.) 1.190 0.034 35.108 0.000
## IS2G28D (.27.) -1.187 0.032 -37.366 0.000
## IS2G28D (.28.) 0.346 0.024 14.424 0.000
## IS2G28D (.29.) 1.438 0.036 39.488 0.000
## IS2G28E (.30.) -2.224 0.077 -28.749 0.000
## IS2G28E (.31.) -0.695 0.041 -17.017 0.000
## IS2G28E (.32.) 1.133 0.050 22.633 0.000
## IS2G28H (.33.) -1.640 0.042 -39.056 0.000
## IS2G28H (.34.) -0.296 0.028 -10.590 0.000
## IS2G28H (.35.) 1.319 0.039 34.185 0.000
## IS2G28I (.36.) -2.263 0.068 -33.321 0.000
## IS2G28I -0.128 0.051 -2.490 0.013
## IS2G28I (.38.) 1.479 0.053 28.137 0.000
## IS2G28J (.39.) -2.590 0.087 -29.920 0.000
## IS2G28J (.40.) -0.346 0.049 -7.003 0.000
## IS2G28J (.41.) 1.725 0.070 24.644 0.000
## IS2G28K (.42.) -2.550 0.065 -39.475 0.000
## IS2G28K (.43.) -1.184 0.042 -27.940 0.000
## IS2G28K (.44.) 0.753 0.037 20.177 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IS2G28A 0.831 0.060 13.817 0.000
## .IS2G28B 0.992 0.073 13.582 0.000
## .IS2G28F 0.832 0.053 15.663 0.000
## .IS2G28G 0.852 0.051 16.652 0.000
## .IS2G28C 0.860 0.049 17.432 0.000
## .IS2G28D 0.900 0.049 18.413 0.000
## .IS2G28E 0.891 0.072 12.356 0.000
## .IS2G28H 1.071 0.062 17.316 0.000
## .IS2G28I 1.028 0.072 14.350 0.000
## .IS2G28J 0.969 0.083 11.690 0.000
## .IS2G28K 1.054 0.061 17.412 0.000
## PosP 0.824 0.064 12.973 0.000
## NegP 0.413 0.033 12.464 0.000
## ExeP 1.965 0.138 14.288 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IS2G28A 0.777
## IS2G28B 0.666
## IS2G28F 0.816
## IS2G28G 0.833
## IS2G28C 0.887
## IS2G28D 0.905
## IS2G28E 0.681
## IS2G28H 0.775
## IS2G28I 0.578
## IS2G28J 0.507
## IS2G28K 0.690##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## PartailScalarRes3 106 741.72
## PartailStrictRes 106 741.72 2.8121e-07 0Compare
## ################### Nested Model Comparison #########################
##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## ConfiguralRes 82 715.46
## PartialMetricRes 89 723.15 8.862 7 0.2627
## PartailScalarRes 108 769.11 65.504 19 5.056e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq.scaled df.scaled pvalue.scaled rmsea.scaled cfi.scaled
## ConfiguralRes 948.300 82 .000 .065 .969
## PartialMetricRes 937.275† 89 .000 .061 .970†
## PartailScalarRes 1004.803 108 .000 .057† .968
## tli.scaled srmr
## ConfiguralRes .959 .043†
## PartialMetricRes .963 .043
## PartailScalarRes .968† .043
##
## ################## Differences in Fit Indices #######################
## df.scaled rmsea.scaled cfi.scaled
## PartialMetricRes - ConfiguralRes 7 -0.003 0.001
## PartailScalarRes - PartialMetricRes 19 -0.004 -0.002
## tli.scaled srmr
## PartialMetricRes - ConfiguralRes 0.004 0
## PartailScalarRes - PartialMetricRes 0.005 0