I would like to carry on a latent change score model for 5 different psychological constructs measured at two time points in lavaan. People told me it can do ok with missing data. I'm struggling with some points:
Let's say I have 4100 respondents at the first measurement point and only 700 at the second and I'm running 5 different models for each of the 5 variables that I'm dealing with. Please assume that self awareness is sa in this example, sm is self-management . Does the following model have the correct specifications?
model <- "
int =~ 1 * sa_t1 + 1 * sa_t2
slope =~ 1 * sa_t2
int ~~ int
slope ~~ slope
sa_t1 ~ 0
sa_t2 ~ 0
int ~ 1
slope ~ 1
sa_t1 ~~ 0 * sa_t1
sa_t2 ~~ 0 * sa_t2
"
fit <- lavaan::sem(model, df_elementary_t1t2_full %>% select(sa_t1, sa_t2))
semPlot::semPaths(fit, layout="tree2", sizeMan=7, sizeInt = 4, normalize=FALSE,
edge.label.cex = .9,
whatLabels="est", width=4, height=1, rotation=2, nCharNodes = 0)
Comparing this result with traditional repeat measures ANOVA, the results are very different
df_elementary_t1t2_full %>%
lm(sa_t2 ~ sa_t1, data = .) %>%
apaTables::apa.aov.table()
I'm based on this post from Terrance (he's a wiz) and this book.
If possible to run all the 5 constructs at once, it would be great. Thanks
Part of the real dataframe
df_elementary_t1t2_full = structure(list(gender = structure(c(2L, 1L, 1L, NA, 2L, NA, NA,
1L, NA, 2L, 1L, 2L, 3L, NA, 1L, 2L, 1L, 1L, 1L, 2L, 2L, NA, NA,
1L, 1L, 2L, NA, 2L, NA, NA, 2L, 2L, NA, 1L, 2L, NA, NA, 2L, 2L,
2L, NA, NA, NA, 1L, NA, 1L, 2L, 2L, 2L, 2L), levels = c("f",
"m", "prefer not to say"), class = "factor"), grade = c(4, 4,
6, 5, 6, 6, 4, 6, 6, 4, 6, 6, 5, 6, 6, 4, 4, 5, 4, 4, 4, 6, 5,
5, 6, 4, 6, 4, 4, 4, 5, 5, 5, 5, 4, 5, 5, 5, 5, 5, 4, 5, 4, 5,
4, 4, 4, 4, 4, 5), sa_t1 = structure(c(0.229204895870929, -0.314854576777207,
-0.314854576777207, 0.773264368519065, -0.314854576777207, 0.229204895870929,
-0.0428248404531391, -1.40297352207348, 0.229204895870929, -0.858914049425343,
0.501234632194997, 0.773264368519065, 0.773264368519065, 0.229204895870929,
0.229204895870929, -0.314854576777207, 1.04529410484313, -0.0428248404531391,
0.501234632194997, 0.773264368519065, 1.58935357749127, 0.501234632194997,
0.501234632194997, -0.0428248404531391, -0.314854576777207, -0.0428248404531391,
1.04529410484313, -0.858914049425343, -0.858914049425343, -0.0428248404531391,
-0.858914049425343, -0.314854576777207, 0.501234632194997, -1.40297352207348,
0.773264368519065, 0.229204895870929, -1.13094378574941, 0.773264368519065,
-0.314854576777207, -1.13094378574941, 0.229204895870929, 0.773264368519065,
-0.586884313101275, 1.58935357749127, 0.501234632194997, -0.586884313101275,
1.3173238411672, 0.229204895870929, 1.04529410484313, -0.0428248404531391
), "`\`scaled:center\``" = 14.1574270557029, "`\`scaled:scale\``" = 3.67606870305055, dim = c(50L,
1L)), sa_t2 = structure(c(-0.332172641489581, NA, -0.599928625867865,
0.47109531164527, NA, NA, 0.47109531164527, NA, NA, NA, NA, NA,
NA, -0.332172641489581, NA, NA, NA, NA, NA, NA, 1.27436326478012,
NA, NA, NA, NA, NA, -2.20646453213757, NA, NA, NA, 1.27436326478012,
NA, NA, NA, 1.27436326478012, NA, NA, -0.599928625867865, NA,
NA, NA, NA, NA, NA, NA, NA, 1.54211924915841, 0.203339327266987,
NA, NA), "`\`scaled:center\``" = 14.2405797101449, "`\`scaled:scale\``" = 3.73474379040286, dim = c(50L,
1L)), sm_t1 = structure(c(0.208683559625981, 0.208683559625981,
-2.36865638948498, 1.2699411857305, -1.30739876338047, 1.11833295342985,
0.511900024227271, -0.397749369576599, -1.61061522798176, -2.06543992488369,
0.360291791926626, 0.208683559625981, 0.815116488828561, 0.511900024227271,
0.966724721129206, -1.00418229877918, 0.057075327325336, -0.549357601877244,
0.360291791926626, 1.72476588263243, 1.72476588263243, 0.966724721129206,
0.511900024227271, 0.360291791926626, -1.15579053107982, -0.246141137275954,
0.815116488828561, 0.360291791926626, 0.511900024227271, 0.663508256527916,
-0.397749369576599, -0.0945329049753089, 1.11833295342985, -1.00418229877918,
0.966724721129206, 0.208683559625981, -0.397749369576599, 0.663508256527916,
0.815116488828561, -1.15579053107982, 0.663508256527916, 0.057075327325336,
-0.549357601877244, 2.02798234723372, 0.663508256527916, -1.15579053107982,
-0.0945329049753089, -1.45900699568111, 1.11833295342985, -0.246141137275954
), "`\`scaled:center\``" = 28.6235341151386, "`\`scaled:scale\``" = 6.59594789032934, dim = c(50L,
1L)), sm_t2 = structure(c(-0.432614415195137, NA, -2.53755178724211,
1.37161761798798, NA, NA, 0.770206940260276, NA, NA, NA, NA,
NA, NA, -0.131909076331284, NA, NA, NA, NA, NA, NA, 1.67232295685184,
NA, NA, NA, NA, NA, -0.883672423490917, NA, NA, NA, -1.18437776235477,
NA, NA, NA, 1.52197028741991, NA, NA, 0.920559609692202, NA,
NA, NA, NA, NA, NA, NA, NA, -0.582967084627064, 1.07091227912413,
NA, NA), "`\`scaled:center\``" = 28.8773311230833, "`\`scaled:scale\``" = 6.65102923531902, dim = c(50L,
1L)), soca_t1 = structure(c(-0.565012428107959, 0.423748326054346,
-0.565012428107959, -0.0706320510268063, -1.88336010032437, 1.2477156211896,
0.0941614080002446, 0.423748326054346, 0.588541785081397, -2.04815355935142,
1.08292216216255, 0.918128703135499, 0.588541785081397, -0.235425510053857,
1.08292216216255, -0.72980588713501, 0.0941614080002446, 0.918128703135499,
-0.235425510053857, 1.5773025392437, 0.258954867027295, -0.235425510053857,
1.5773025392437, 0.0941614080002446, 0.423748326054346, 0.588541785081397,
0.918128703135499, -1.55377318227026, -0.565012428107959, -0.72980588713501,
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-0.72980588713501, 1.41250908021665, -0.0706320510268063, -0.894599346162061,
-0.72980588713501, -1.22418626421616, 0.918128703135499, 0.753335244108448,
-0.565012428107959, 1.2477156211896, 1.41250908021665, -1.05939280518911,
0.423748326054346, -0.565012428107959, 1.08292216216255, 0.918128703135499
), "`\`scaled:center\``" = 28.4286095543101, "`\`scaled:scale\``" = 6.06820201423073, dim = c(50L,
1L)), soca_t2 = structure(c(-1.19778259737864, NA, -0.552580257562162,
0.0926220822543204, NA, NA, 0.0926220822543204, NA, NA, NA, NA,
NA, NA, 0.253922667208441, NA, NA, NA, NA, NA, NA, 0.576523837116682,
NA, NA, NA, NA, NA, 0.899125007024924, NA, NA, NA, -1.52038376728689,
NA, NA, NA, 1.22172617693316, NA, NA, -0.391279672608042, NA,
NA, NA, NA, NA, NA, NA, NA, 1.06042559197904, 0.0926220822543204,
NA, NA), "`\`scaled:center\``" = 28.4257796257796, "`\`scaled:scale\``" = 6.19960553946183, dim = c(50L,
1L)), rel_t1 = structure(c(-0.256736839498058, 0.484059419012018,
-1.88648860822022, -0.553055342902088, -0.701214594604103, 0.484059419012018,
0.187740915607988, -0.404896091200073, -1.44201085311418, -2.33096636332627,
0.780377922416048, 0.484059419012018, 1.66933343262814, 0.484059419012018,
1.81749268433015, -0.701214594604103, -0.553055342902088, 0.484059419012018,
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1.81749268433015, 1.22485567752209, -0.108577587796043, 0.0395816639059724,
0.484059419012018, -0.256736839498058, -1.29385160141216, -0.256736839498058,
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0.484059419012018, -0.108577587796043, -1.29385160141216, -0.256736839498058,
0.484059419012018, -1.73832935651821, 1.07669642582008, 0.632218670714033,
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-0.256736839498058, -1.73832935651821, 1.07669642582008, 0.484059419012018
), "`\`scaled:center\``" = 33.7328437917223, "`\`scaled:scale\``" = 6.74949413224121, dim = c(50L,
1L)), rel_t2 = structure(c(-0.814176449272021, NA, -0.959841291895562,
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1L)), rdm_t1 = structure(c(-1.17089225239951, 0.880911938734223,
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1L)), rdm_t2 = structure(c(-0.825337957180077, NA, -2.3372624123534,
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"data.frame"))