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.2 Panduan Model PROCESS

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.2 Panduan Model PROCESS
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  SPSS PROCESS documentation, 2 January 2013 Page 1 PROCESS PROCESS VARS = varlist /Y =  yvar  /X = xvar  /M =  mvlist /MODEL = num   [/W = wvar  ] [/Z = zvar  ] [/V = vvar  ] [/Q = qvar  ] [/WMODVAL = wval ] [/ZMODVAL = zval ] [/VMODVAL = vval ] [/QMODVAL = qval ] [/MMODVAL =  mval ] [/XMODVAL = xval] [/CLUSTER = clvar  ] [/CONTRAST = cst ] [/BOOT = z  {1000**}] [/CONF = ci {95**}] [/EFFSIZE = size {0**}] [/NORMAL = t {0**}] [/JN = j {0}] [/COEFFCI = cci{1***}] [/VARORDER = vord  (2**}] [/HC3 = h {0**}] [/COVMY = cov  {0**}] [/TOTAL = tot {0**}] [/CENTER = cntr  {0**}] [/QUANTILE = qnt {0**}] [/DETAIL = dt {1**}] [/PLOT = pt{0**}] [/SEED = sd{random**} [/PERCENT = pb{0**}] [/ITERATE = it{10000*}] [/CONVERGE = cvg{.00000001}]. Subcommands in brackets are optional. User input in italics. Brackets should not be included in PROCESS command. ** Default if subcommand is omitted Overview    PROCESS   is a computational tool for path analysis-based moderation and mediation analysis as well as their combination as a “ conditional process model ”  (Hayes and Preacher, in press; Hayes, in press). In a single command, it provides many of the capabilities of SOBEL (Preacher and Hayes, 2004), INDIRECT (Preacher & Hayes, 2008), MODPROBE (Hayes & Matthes, 2009), MODMED (Preacher, Rucker, & Hayes, 2007), and MED3/C (Hayes, Preacher, & Myers, 2011) while greatly expanding the number and complexity of models that combine moderation and mediation (“mediated moderation” and “ moderated mediation ”). In addition to estimating the coefficients of the model  using OLS regression (for continuous outcomes) or maximum likelihood logistic regression (for dichotomous outcomes ), PROCESS generates direct and indirect effects in mediation and mediated moderation models, conditional effects in moderation models, and conditional indirect effects in moderated mediation models with a single or multiple mediators. PROCESS offers various tools for probing 2 and 3 way interactions and can construct percentile based bootstrap confidence intervals for conditional and unconditional indirect effects. In mediation models, multiple mediator variables can be specified to operate in parallel or in sequence. Heteroscedasticity-consistent standard errors are available for  PROCESS is written by Andrew F. Hayes, http://www.afhayes.com Page 2 inference about paths coefficients, in the Sobel test for indirect effects, and when probing interactions in moderation analysis. Various measures of effect size for indirect effects are generated in mediation models, along with bootstrap confidence intervals for effect size inference. An option is available for  partialing out contextual level variation when individual data are nested under a higher-level organizational structure. Individual paths in moderated mediation models can be estimated as moderated by one or two variables either additively or multiplicatively. Some models estimated by PROCESS allow up to four moderators simultaneously. See Hayes (in press) for an introduction to the use of PROCESS as well as the basic concepts and  principles of mediation, moderation, and conditional process analysis.   Preparing for Use The PROCESS.sps file should be opened as a syntax file in SPSS. Once it has been opened, execute the entire file exactly as is . Do not modify the code at all. Once the program is executed, the PROCESS  program window can be closed. You then have access to the PROCESS   command until you quit SPSS. The PROCESS.sps file must be loaded and reexecuted each time SPSS is opened. See the “Examples” section below for some examples of how to set up a PROCESS command in a syntax window. Please also read the “Model Designation and Estimation” and “Notes” sections below for important details  pertinent to execution. Examples (1) Moderation  process vars = protest sexism liking tenure exper/y = liking/x = protest/m = sexism /model = 1/quantile = 1/center = 1/plot = 1/jn = 1.    Estimates a simple moderation model with the effect of protest  on liking  moderated by sexism      tenure  and exper  are included in the model as covariates .      sexism  and protest are mean centered prior to analysis.    Generates the conditiona l effects (a.k.a. “simple slopes”) of   protest  on liking  at values of sexism  equal to the 10 th , 25 th , 50 th , 75 th , and 90 th  percentiles of the distribution in the sample.    Produces a table of estimated values of   liking  for various values of protest  and  sexism      Implements the Johnson-Neyman technique to identify the values on the continuum of sexism  at which point the effect of protest  transitions between statistically significant and nonsignificant at the 0.05 level. (2) Moderation  process vars = mathprob gender explms treat/y = mathprob/x = treat/m = explms /w = gender/mmodval = 4/model = 3.    Estimates a moderated regression model predicting mathprob from treat  while including a three way interaction between treat , explms , and gender  in the model.    Generates the conditional effect of   treat   (a.k.a. “simple slopes”) on  mathprob  for both males and females when explms  = 4.  SPSS PROCESS documentation, 2 January 2013 Page 3 (3) Simple Mediation  process vars = cond pmi react/y = react/x = cond/m = pmi/model = 4/total = 1 /effsize = 1/boot=10000.    Estimates the total and direct effect of cond  on react , as well as the indirect effect of cond  on react  through  pmi .      Generates a bias-corrected 95% bootstrap confidence interval for the indirect effect using 10,000  bootstrap samples.    Produces point and bias-corrected 95% bootstrap confidence interval estimates of various indices of effect size for the indirect effects. (4) Multiple Mediation with Mediators operating in parallel  process vars = know educ attn elab sex age/y=know/x=educ/m=attn elab/model=4 /contrast=1/normal=1/conf=90.    Estimates the direct effect of educ  on know , as well as the total and specific indirect effects of educ  on know  through   attn   and   elab, with  attn  and  elab  functioning as parallel mediators.      sex  and age  are included in the model as covariates.    Produces the Sobel test for the specific indirect effects.    Generates 90% bias-corrected confidence intervals for the indirect effects using 1,000 bootstrap samples.    Calculates the difference between the two specific indirect effects and produces a bootstrap confidence interval for the difference. (5) Multiple Mediation with Mediators operating in Serial  process vars = cond import pmi react nbhrhood/y = react/x = cond/m = import pmi /model = 6/hc3 = 1/effsize = 1/total = 1/boot = 10000/cluster = nbhrhood.    Estimates the total and direct effect of cond  on react , as well as the total and all possible specific indirect effects of cond  on react  through   pmi and  import.      pmi  and import  function as mediators in serial, with  import  affecting pmi.      Standard errors for model coefficients are based on the HC3 heteroscedasticity-consistent standard error estimator.    Generates 95% bias corrected bootstrap confidence intervals for the indirect effects using 10,000  bootstrap samples.    Produces point estimates and bias corrected 95% bootstrap confidence interval for various indices of effect size for the indirect effects.    With cases nested within neighborhoods (coded with a variable named nbhrhood ), partials out neighborhood-level effects from all effect estimates. (6) Moderated Mediation  process vars = frame euskept peffic risk turnout/y = turnout/x = frame/m = risk /w = euskept/z = peffic/model = 68/boot = 20000/wmodval = 2/center = 1.    Estimates the direct effect of   frame on turnout , as well as the conditional indirect effects of frame  on turnout  through   risk. The effect of   frame  on  risk  is modeled as multiplicatively  PROCESS is written by Andrew F. Hayes, http://www.afhayes.com Page 4 moderated by both  peffic  and euskept , and the effect of risk  on turnout  is modeled as moderated  by euskept .    euskept , peffic , and  frame  are mean centered prior to analysis.    Calculates the conditional indirect effects of frame  on turnou t through risk  among cases 2 units above the sample mean on  euskept and with values of peffic  at the   sample means as well as with peffic  one standard deviation above and below the sample mean.    Generates bias corrected 95% bootstrap confidence intervals for the conditional indirect effects using 20,000 bootstrap samples. (7) Moderated Mediation  process vars = calling livecall carcomm workmean jobsat/y = jobsat/m = carcomm  workmean/x = calling/w = livecall/model = 7/boot = 5000/seed=34421.      Estimates the direct effect of   calling on jobsat , as well as the conditional indirect effects of calling  on jobsat  through  both carcomm and  workmean operating in parallel.   The effects of calling  on both carcomm  and workmean  are modeled as moderated by livecall .      Produces the conditional indirect effects calling when   livecall is equal to the sample mean as well as plus and minus one standard deviation from the mean.    Generates bias corrected 95% bootstrap confidence intervals for the conditional indirect effects using 5,000 bootstrap samples.    Seeds the random number generator for bootstrap sampling with the value 34421. (8) M oderated Mediation (and “Mediated Moderation”)    process vars = protest sexism respappr liking age sex/y = liking/x = protest /m = respappr/w = sexism/model = 8/boot = 5000/quantile = 1/percent=1.    Generates model coefficients for model 8, estimating the effect of protest  on liking  directly as well as indirectly through respappr , with both direct and indirect effects moderated by sexism . The effect of protest  on respappr  is modeled as moderated by sexism .      age  and sex  are included in the model as covariates.    Generates 95% percentile-based confidence intervals based on 5,000 bootstrap samples for the conditional indirect effect of   protest  at the 10 th , 25 th , 50 th , 75 th , and 90 th  percentile values of sexism .    Produces the indirect effect of the product of protest  and  sexism  on liking  through respappr , along with a percentile-based 95% bootstrap confidence interval. Model Designation and Estimation PROCESS can estimate many different models, and which model is estimated is determined by the num   argument in the required /MODEL  subcommand. The models that PROCESS can estimate are depicted conceptually and in the form of a path diagram in the Appendix, along with their corresponding model number as recognized by PROCESS in the /MODEL  subcommand. Each model has certain minimum requirements as to which variables must be designated and  provided in the PROCESS command. Any variable in the data set that appears in the model must be listed in the varlist  argument of the PROCESS command (e.g., vars = xvar yvar mvar wvar ). Furthermore, all models require  SPSS PROCESS documentation, 2 January 2013 Page 5    a single outcome variable yvar  listed in the /Y  subcommand (i.e., /y  = yvar) ,   where yvar  is the name of the variable in your data functioning as Y   in the model      a single antecedent causal agent  xvar  listed in the /X  subcommand (i.e., /x = xvar) ,   where xvar  is the name of the variable in your data functioning as  X   in the model    either a single moderator (models 1, 2, and 3) or at least one mediator (models 4 and higher) specified in the mvlist   in the / M   subcommand (i..e, / m = mvlist ) , where mvlist   is the name of the variable or variables in your data functioning as moderator (models 1, 2, and 3) or mediator(s) (models 4 and higher). Other than /X , /Y , /M , /MODEL , and  /VARS , the remaining required inputs to PROCESS will be model dependent. In general, any variable that is a part of the conceptual model in the model template must be provided as an input to PROCESS, and any variable that is not a part of the conceptual model must be left out unless such variables are to be treated as covariates by inclusion in  varlist . For instance, observe that model 21 has, in addition to  X  ,  M  , and  Y  , two moderators, W   and V  . Thus, PROCESS must also be told which two variables in the data set correspond to W   and V   in the diagram. This would be done with the use of the /W  and /V  subcommands (e.g., /W = wvar/V = vvar ), where wvar  and vvar  are the names of the variables in the data file corresponding to W   and V  . The /Y , /X , /W , /Z , /V , and /Q  subcommands each allow only one variable, and a variable can  be listed in one and only one of the yvar , xvar , mvlist , wvar , zvar , vvar , and qvar  arguments. For instance, a variable cannot be listed as both W   in  wvar  and  M   in mvlist . However, both would have to appear in varlist . Variable names listed in the varlist , yvar , xvar , mvlist , wvar , zvar , vvar , and qvar arguments  must match the case (i.e., uppercase, lowercase, or combinations thereof) of the variabl es in the dataset. So “ATTITUDE,” “Attitude”, and “AttiTuDe” are different variables according to PROCESS. Thus, /y = ATTITUDE   will produce an error even if “Attitude” exists in your data file. In addition, the potential for errors at execution is increased when variable names are more than eight characters in length. Thus, the user is advised to reduce all long variable names in the data set and that are to be used in a PROCESS command down to eight characters at maximum. Although PROCESS has a number of error trapping routines built in, it will not catch all errors  produced by improper formatting of a PROCESS command, improper listing of variables and variable names, and so forth. Any errors it has trapped will be displayed in an errors section of the PROCESS output. Errors it has not successfully trapped will appear as a long list of SPSS execution errors that will be largely unintelligible. Multiple Mediators All mediation models (models 4 and higher) can have up to 10 mediators operating in  parallel  , with the exception of model 6, which is restricted to between 2 and 4 and models the mediators as operating in  serial  . Mediators operating in parallel are all modeled as affected by  xvar  and, in turn, affect  yvar , but they are not modeled to transmit their effects to any other mediators in the model. Mediators operating in serial are linked in a causal chain, with the first mediator affecting the second, the second the third, and so forth. The order of the mediators in mvlist  is not consequential to the estimation or the model except in model 6. In model 6, the first variable
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