Cheung00: Fifty Studies of Correlation Matrices used in Cheung and Chan...

Description Usage Details Note Source References Examples

Description

This data set includes fifty studies of correlation matrices on the theory of planned theory reported by Cheung and Chan (2000).

Usage

1

Details

A list of data with the following structure:

data

A list of 50 studies of correlation matrices. The variables are the attitude toward behavior att, subjective norm sn, behavioral intention bi, and behavior beh

n

A vector of sample sizes

Note

These studies were extracted from the original data set for illustration purpose. Some samples contained two or more correlation matrices, and only one of them was arbitrarily selected to avoid the problem of dependence. Moreover, studies with less than 3 correlation coefficients were also excluded.

Source

Cheung, S.-F., & Chan, D. K.-S. (2000). The role of perceived behavioral control in predicting human behavior: A meta-analytic review of studies on the theory of planned behavior. Unpublished manuscript, Chinese University of Hong Kong.

References

Cheung, M.W.-L., & Cheung, S.-F. (2016). Random-effects models for meta-analytic structural equation modeling: Review, issues, and illustrations. Research Synthesis Methods, 7, 140-155.

Examples

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## Not run: 
data(Cheung00)

## Variable labels
labels <- colnames(Cheung00$data[[1]])

## Full mediation model
S <- create.mxMatrix(c("1", 
                       ".2*cov_att_sn", "1", 
                       0, 0, ".2*e_bi", 
                       0, 0, 0, ".2*e_beh"), 
                     type="Symm", as.mxMatrix=FALSE, byrow=TRUE)
dimnames(S) <- list(labels, labels)
S

A <- matrix(c("0","0","0","0",
              "0","0","0","0",
              ".2*att2bi", ".2*sn2bi", "0", "0",
              "0", "0", ".2*bi2beh", "0"),
            byrow=TRUE, 4, 4)
dimnames(A) <- list(labels, labels)
A

#### Random-effects model

## Stage 1 analysis
random_1 <- tssem1(Cheung00$data, Cheung00$n, method="REM", RE.type="Symm",
                   acov="individual")
summary(random_1)

## Stage 2 analysis
random_2 <- tssem2(random_1, Amatrix=A, Smatrix=S, intervals.type="LB",
                   diag.constraints=TRUE)
summary(random_2)

## Display the model
plot(random_2, what="path")

## Display the model with the parameter estimates
plot(random_2, color="yellow")

## Load the library
library("semPlot")

## End(Not run)

Example output

Loading required package: OpenMx
To take full advantage of multiple cores, use:
  mxOption(NULL, 'Number of Threads', parallel::detectCores()) #now
  Sys.setenv(OMP_NUM_THREADS=parallel::detectCores()) #before library(OpenMx)
"SLSQP" is set as the default optimizer in OpenMx.
mxOption(NULL, "Gradient algorithm") is set at "central".
mxOption(NULL, "Optimality tolerance") is set at "6.3e-14".
mxOption(NULL, "Gradient iterations") is set at "2".
    att             sn              bi        beh       
att "1"             ".2*cov_att_sn" "0"       "0"       
sn  ".2*cov_att_sn" "1"             "0"       "0"       
bi  "0"             "0"             ".2*e_bi" "0"       
beh "0"             "0"             "0"       ".2*e_beh"
    att         sn         bi          beh
att "0"         "0"        "0"         "0"
sn  "0"         "0"        "0"         "0"
bi  ".2*att2bi" ".2*sn2bi" "0"         "0"
beh "0"         "0"        ".2*bi2beh" "0"

Call:
meta(y = ES, v = acovR, RE.startvalues = RE.startvalues, RE.lbound = RE.lbound, 
    I2 = I2, model.name = model.name, suppressWarnings = TRUE, 
    silent = silent, run = run)

95% confidence intervals: z statistic approximation
Coefficients:
              Estimate   Std.Error      lbound      ubound z value  Pr(>|z|)
Intercept1  3.5786e-01  2.5405e-02  3.0806e-01  4.0765e-01 14.0860 < 2.2e-16
Intercept2  4.8609e-01  2.1221e-02  4.4450e-01  5.2768e-01 22.9063 < 2.2e-16
Intercept3  2.7404e-01  2.9832e-02  2.1557e-01  3.3251e-01  9.1862 < 2.2e-16
Intercept4  3.0912e-01  2.2665e-02  2.6470e-01  3.5355e-01 13.6384 < 2.2e-16
Intercept5  1.3441e-01  2.4022e-02  8.7324e-02  1.8149e-01  5.5951 2.205e-08
Intercept6  4.3889e-01  2.5076e-02  3.8975e-01  4.8804e-01 17.5023 < 2.2e-16
Tau2_1_1    2.3687e-02  6.2407e-03  1.1455e-02  3.5918e-02  3.7955 0.0001473
Tau2_2_1    7.6585e-03  4.2202e-03 -6.1289e-04  1.5930e-02  1.8147 0.0695647
Tau2_2_2    1.6694e-02  4.3198e-03  8.2273e-03  2.5160e-02  3.8645 0.0001113
Tau2_3_1   -1.3553e-03  5.9083e-03 -1.2935e-02  1.0225e-02 -0.2294 0.8185626
Tau2_3_2    9.8327e-03  5.0193e-03 -4.8893e-06  1.9670e-02  1.9590 0.0501140
Tau2_3_3    2.0259e-02  6.8305e-03  6.8715e-03  3.3647e-02  2.9660 0.0030173
Tau2_4_1    1.5840e-02  4.9188e-03  6.1999e-03  2.5481e-02  3.2204 0.0012800
Tau2_4_2    8.2122e-03  3.6144e-03  1.1282e-03  1.5296e-02  2.2721 0.0230809
Tau2_4_3    8.4800e-04  4.7935e-03 -8.5471e-03  1.0243e-02  0.1769 0.8595835
Tau2_4_4    1.8448e-02  5.0346e-03  8.5806e-03  2.8316e-02  3.6643 0.0002480
Tau2_5_1    3.0916e-03  4.8633e-03 -6.4403e-03  1.2624e-02  0.6357 0.5249667
Tau2_5_2    2.1540e-03  3.8729e-03 -5.4367e-03  9.7447e-03  0.5562 0.5780927
Tau2_5_3    8.5391e-03  4.3976e-03 -7.9912e-05  1.7158e-02  1.9418 0.0521623
Tau2_5_4    4.8330e-03  4.0364e-03 -3.0781e-03  1.2744e-02  1.1974 0.2311637
Tau2_5_5    8.1853e-03  4.0756e-03  1.9732e-04  1.6173e-02  2.0084 0.0446031
Tau2_6_1   -5.0488e-03  4.5104e-03 -1.3889e-02  3.7915e-03 -1.1194 0.2629901
Tau2_6_2    2.4050e-03  3.8909e-03 -5.2211e-03  1.0031e-02  0.6181 0.5365119
Tau2_6_3    1.3528e-02  5.0640e-03  3.6027e-03  2.3453e-02  2.6714 0.0075534
Tau2_6_4   -2.5042e-03  3.8828e-03 -1.0114e-02  5.1060e-03 -0.6449 0.5189674
Tau2_6_5    6.7686e-03  3.8473e-03 -7.7206e-04  1.4309e-02  1.7593 0.0785281
Tau2_6_6    2.0749e-02  5.5566e-03  9.8587e-03  3.1640e-02  3.7342 0.0001883
              
Intercept1 ***
Intercept2 ***
Intercept3 ***
Intercept4 ***
Intercept5 ***
Intercept6 ***
Tau2_1_1   ***
Tau2_2_1   .  
Tau2_2_2   ***
Tau2_3_1      
Tau2_3_2   .  
Tau2_3_3   ** 
Tau2_4_1   ** 
Tau2_4_2   *  
Tau2_4_3      
Tau2_4_4   ***
Tau2_5_1      
Tau2_5_2      
Tau2_5_3   .  
Tau2_5_4      
Tau2_5_5   *  
Tau2_6_1      
Tau2_6_2      
Tau2_6_3   ** 
Tau2_6_4      
Tau2_6_5   .  
Tau2_6_6   ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Q statistic on the homogeneity of effect sizes: 1334.941
Degrees of freedom of the Q statistic: 242
P value of the Q statistic: 0

Heterogeneity indices (based on the estimated Tau2):
                             Estimate
Intercept1: I2 (Q statistic)   0.8387
Intercept2: I2 (Q statistic)   0.8428
Intercept3: I2 (Q statistic)   0.7927
Intercept4: I2 (Q statistic)   0.7901
Intercept5: I2 (Q statistic)   0.5757
Intercept6: I2 (Q statistic)   0.8465

Number of studies (or clusters): 50
Number of observed statistics: 248
Number of estimated parameters: 27
Degrees of freedom: 221
-2 log likelihood: -300.878 
OpenMx status1: 0 ("0" or "1": The optimization is considered fine.
Other values may indicate problems.)

Call:
wls(Cov = pooledS, aCov = aCov, n = tssem1.obj$total.n, Amatrix = Amatrix, 
    Smatrix = Smatrix, Fmatrix = Fmatrix, diag.constraints = diag.constraints, 
    cor.analysis = cor.analysis, intervals.type = intervals.type, 
    mx.algebras = mx.algebras, model.name = model.name, suppressWarnings = suppressWarnings, 
    silent = silent, run = run)

95% confidence intervals: Likelihood-based statistic
Coefficients:
           Estimate Std.Error  lbound  ubound z value Pr(>|z|)
bi2beh      0.43523        NA 0.38560 0.48491      NA       NA
att2bi      0.42435        NA 0.38097 0.46805      NA       NA
sn2bi       0.15682        NA 0.11921 0.19508      NA       NA
e_beh       0.81057        NA 0.76485 0.85131      NA       NA
e_bi        0.74736        NA 0.70588 0.78596      NA       NA
cov_att_sn  0.36045        NA 0.31088 0.41007      NA       NA

Goodness-of-fit indices:
                                               Value
Sample size                                8182.0000
Chi-square of target model                    8.0237
DF of target model                            2.0000
p value of target model                       0.0181
Number of constraints imposed on "Smatrix"    2.0000
DF manually adjusted                          0.0000
Chi-square of independence model            903.3607
DF of independence model                      6.0000
RMSEA                                         0.0192
RMSEA lower 95% CI                            0.0067
RMSEA upper 95% CI                            0.0339
SRMR                                          0.0266
TLI                                           0.9799
CFI                                           0.9933
AIC                                           4.0237
BIC                                          -9.9957
OpenMx status1: 0 ("0" or "1": The optimization is considered fine.
Other values indicate problems.)

metaSEM documentation built on May 18, 2021, 1:06 a.m.