**BASED ON PAPER:** Toward a Greater Understanding of How Dissatisfaction Drives Employee Turnover
Author(s): Peter W. Hom and Angelo J. Kinicki.

**Source:** The Academy of Management Journal, Vol. 44, No. 5 (Oct., 2001), pp. 975-987
Published by: Academy of Management.

Stable URL: https://www.jstor.org/stable/3069441

This example sensitivity analysis uses the published data and model from Hom and Kinicki (2001). We create a covariance matrix from their data, set up lavaan models for their original model and the sensitivity model, run the analysis, and view the results.

# Load lavaan and SEMsens packages require(lavaan) require(SEMsens) set.seed(1)

Hom and Kinicki (2001) published correlations and standard deviations for all indicator variables, so we use those to create a covariance matrix `covmat`

.

# Correlation matrix from published data cormat <- diag(1,29) # start with the diagonal cormat[lower.tri(cormat)] <- c( # add lower triangle data .46,.22,.30,-.20,-.30,-.20,-.22,-.33,-.19,-.25,-.63,-.60,-.48,-.17,-.42,-.09,-.36,.09, .04,-.12,0,-.20,-.20,-.43,-.33,-.49,-.18,-.03,.21,.30,-.15,-.25,-.10,-.20,-.31,-.15, -.24,-.40,-.37,-.32,-.08,-.24,-.02,-.27,.06,.07,-.08,0,-.10,-.16,-.25,-.18,-.35,-.06, -.05,.04,-.05,-.09,-.12,-.10,-.01,.03,.02,-.18,-.14,-.09,-.02,-.12,-.05,-.08,.03,.07, -.08,-.06,-.10,-.07,-.10,-.03,-.11,.04,.03,-.07,-.24,0,-.12,-.50,-.32,-.45,-.38,-.29, -.31,-.16,-.24,-.16,-.37,.12,.05,-.28,-.13,-.06,-.03,-.16,-.12,-.23,-.08,.06,.29,.31, .10,.08,-.02,.07,.21,.24,.25,.06,.09,.04,.14,-.02,-.01,.10,.04,.04,.13,.19,.03,.18, .14,-.04,.38,.41,.23,.13,.22,.36,.35,.35,.09,.14,.11,.14,-.09,-.04,.11,.04,.11,.15, .30,.17,.33,.12,-.02,.31,.05,.03,.15,.22,.20,.21,-.05,.10,-.05,.03,-.04,-.06,.03,.05, .03,.07,.24,.19,.23,.13,-.08,.17,.15,.19,.28,.23,.23,.04,.18,.08,.10,-.09,-.06,.07, .01,.11,.08,.21,.20,.28,.06,-.03,.43,.56,.35,.32,.26,.16,.27,.13,.45,-.04,-.04,.16, .07,.14,.15,.23,.12,.29,.09,-.07,.67,.24,.26,.27,.15,.21,.13,.25,-.10,-.02,.12,0,.12, .10,.18,.21,.27,.17,-.06,.36,.32,.31,.11,.29,.16,.40,-.10,-.05,.22,.10,.17,.15,.27, .23,.33,.17,-.08,.78,.77,.11,.49,.09,.40,-.23,-.14,.19,-.07,.29,.27,.57,.40,.68,.27,0, .82,.10,.57,.08,.42,-.29,-.10,.13,-.12,.33,.31,.66,.46,.75,.32,.02,.10,.49,.08,.38, -.31,-.11,.18,-.06,.33,.34,.54,.38,.67,.31,-.08,.14,.52,.20,.22,.31,.42,.39,.01,.01, .07,.06,.13,-.03,.06,.14,.42,-.29,-.12,.24,-.06,.25,.25,.38,.28,.48,.18,-.01,.16,.16, .28,.68,.48,.06,.10,.01,.12,.15,-.05,.04,.02,.03,.24,.11,.14,.16,.31,.22,.32,.09,-.01, .37,.12,.18,-.20,-.15,-.22,-.08,-.23,-.11,.07,.13,.18,-.07,0,-.10,.04,-.06,-.03,.15, .54,.11,.12,.07,.10,.16,-.01,-.02,-.06,-.01,-.09,-.05,-.10,-.14,.06,.57,.28,.17,.36, .27,-.05,.32,.21,.36,.18,.05,.42,.68,.26,-.05,.55,.23,.04,.28,-.03,-.07 ) cormat[upper.tri(cormat)] <- t(cormat)[upper.tri(cormat)] # mirror lower to upper # Standard deviations from published data sds <- diag(c(1,.38,.73,.65,.58,.78,.75,.55,.59,.54,.45,.74,.85,.80,.57, .74,.69,.42,.57,.63,.62,.70,1.01,.94,.65,.37,.73,.40,1)) # Create covariance matrix covmat with row and column names covmat <- sds %*% cormat %*% sds rownames(covmat) <- colnames(covmat) <- c( "FACES","Duty","Team","Hours","Absent","Effort","Sick","Quality","Family", "Community","Personal","Thoughts","SI","QI","Stress","Jobs","Costs", "Benefits","Joblessness","Moving","Impact","Interference","V1","V2", "Prepare","Look","Intensity","Resign","Unemployed" )

The SEMsens package requires both an original model and a sensitivity model that adds the phantom variables. These models are defined SEMsens using the lavaan model syntax.

We first define the original model:

# Original model from Hom and Kinicki (2001) model <- "JSat =~ FACES + Duty + Team IC =~ Family + Personal + Community JAvoid =~ Quality + Absent + Effort + Sick WC =~ QI + Thoughts + SI WEU =~ Stress + Benefits + Impact + Jobs JSearch =~ Prepare + Look + Intensity CA =~ V1 + V2 Turnover =~ Resign Resign ~~ 0*Resign UR =~ Unemployed Unemployed ~~ 0*Unemployed JSat ~ IC JAvoid ~ JSat WC ~ JAvoid + JSat + IC WEU ~ WC + JSat + UR JSearch ~ WEU CA ~ JSearch Turnover ~ CA + WC + UR"

For the sensitivity model, we use the same model definition as the original model but add one phantom variable with paths phantom1, phantom2, etc. to each of the latent variables in the original model.

# Sensitivity model sens.model <- "JSat =~ FACES + Duty + Team IC =~ Family + Personal + Community JAvoid =~ Quality + Absent + Effort + Sick WC =~ QI + Thoughts + SI WEU =~ Stress + Benefits + Impact + Jobs JSearch =~ Prepare + Look + Intensity CA =~ V1 + V2 Turnover =~ Resign Resign ~~ 0*Resign UR =~ Unemployed Unemployed ~~ 0*Unemployed JSat ~ IC JAvoid ~ JSat WC ~ JAvoid + JSat + IC WEU ~ WC + JSat + UR JSearch ~ WEU CA ~ JSearch Turnover ~ CA + WC + UR IC ~ phantom1*phantom UR ~ phantom2*phantom JSat ~ phantom3*phantom JAvoid ~ phantom4*phantom WC ~ phantom5*phantom WEU ~ phantom6*phantom JSearch ~ phantom7*phantom CA ~ phantom8*phantom Turnover ~ phantom9*phantom phantom =~ 0 phantom ~~ 1*phantom"

Before running the analysis, we need to identify the rows in a lavaan parameter table that include the paths we are interested in looking at in the analysis. To save space, only the most relevant section of the parameter table is displayed below.

ptab = lavaanify(model = model, auto = TRUE, model.type="sem") ptab[26:40,1:4]

For this example, we are interested in all the paths between latent variables. These are located on rows 27 to 39 in the parameter table for this model. That information will be used in the next step for the analysis.

The `sa.aco`

function is used to run the sensitivity analysis.

my.sa <- sa.aco(model = model, sens.model = sens.model, sample.cov = covmat, sample.nobs = 410, opt.fun = 3, paths = 27:39, seed = 1, k = 5, max.iter = 20)

We specified the original model (`model`

), the sensitivity model (`sens.model`

), the covariance matrix (`sample.cov`

), the number of observations in the sample (`sample.nobs`

), the number of sensitivity parameters included (`n.of.sens.pars`

), a preset optimization function (`opt.fun`

), the paths from the previous step (`paths`

), and a seed for reproducibility (`seed`

).

**Note:** We only used `k = 5`

and `max.iter = 20`

so the analysis would run quickly for illustration purposes. For actual analyses, please specify these parameters as larger numbers (e.g., the default values are `k = 50`

and `max.iter = 1000`

).

The `sens.tables`

function helps summarize the results of a sensitivity analysis. We specify `path = TRUE`

to only obtain results for the structural paths.

my.sa.results = sens.tables(my.sa, path=TRUE) # get results my.sa.results = lapply(my.sa.results, round, digits = 3) # round results (optional)

The tables contain several categories of results. Each is displayed below.

The `sens.summary`

table contains the estimates and p values for each path in the original model as well as the mean, minimum, and maximum values for the paths that were estimated during the sensitivity analysis.

```
my.sa.results$sens.summary
```

The `phan.paths`

table contains the mean, minimum, and maximum of the sensitivity parameters from the analysis

```
my.sa.results$phan.paths
```

The `phan.min`

table provides the set of tested sensitivity parameter values for each path that resulted in the smallest coefficient value for the path.

```
my.sa.results$phan.min
```

Likewise, the `phan.max`

table provides the set of tested sensitivity parameter values for each path that resulted in the *largest* coefficient value for the path.

```
my.sa.results$phan.max
```

The final table `p.paths`

provides the sensitivity parameters that lead to a change in significance for each path according to the significance level specified in the `sens.tables`

function. We used the default significance level of .05 for that. The first column contains the original p values and the second contains the changed p values that are obtained with the listed phantom variable path coefficients. The NAs occur when there is no change in p value for any of the tested phantom variable path coefficients.

```
my.sa.results$p.paths
```

Leite, W., Shen, Z., Marcoulides, K., Fish, C., & Harring, J. (2022). Using ant colony optimization for sensitivity analysis in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 29 (1), 47-56.

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