1. Read in the SPSS data file into R. Please make sure the missing values are shown as NA in R. Provide your code to answer this question.
data <- import("cham_attachment.sav")
finaldat <- na.omit(data)
  1. Reverse the scoring for the items with asterisks in the scoring keys for attachment anxiety, reactive coping, and depression. Provide your code to answer this question.
finaldat$a22r <- 8-finaldat$a22
finaldat$pf7r <- 6-finaldat$pf7
finaldat$pf8r <- 6-finaldat$pf8
finaldat$pf9r <- 6-finaldat$pf9
finaldat$sds2r <- 5-finaldat$sds2
finaldat$sds5r <- 5-finaldat$sds5
finaldat$sds6r <- 5-finaldat$sds6
finaldat$sds11r <- 5-finaldat$sds11
finaldat$sds12r <- 5-finaldat$sds12
finaldat$sds14r <- 5-finaldat$sds14
finaldat$sds16r <- 5-finaldat$sds16
finaldat$sds17r <- 5-finaldat$sds17
finaldat$sds18r <- 5-finaldat$sds18
finaldat$sds20r <- 5-finaldat$sds20
  1. Compute the average item scores for attachment anxiety, reactive coping, and depression. Provide your code to answer this question.
aa_avg <- c("a2", "a4", "a6", "a8", "a10", "a12", "a14", "a16", "a18", "a20", "a22r", "a24", "a26", "a28", "a30", "a32", "a34", "a36")
rc_avg <-  c("pf7r","pf8r","pf9r")
dep_avg <- c("sds1", "sds2r", "sds3", "sds4", "sds5r", "sds6r", "sds7", "sds8", "sds9", "sds10", "sds11r", "sds12r", "sds13", "sds14r", "sds15", "sds16r", "sds17r", "sds18r", "sds19", "sds20r")
finaldat <- finaldat %>% dplyr::mutate(attachmentanxiety = rowMeans(finaldat[ ,aa_avg], na.rm=TRUE))
finaldat <- finaldat %>% dplyr::mutate(reactive = rowMeans(finaldat[ ,rc_avg], na.rm=TRUE))
finaldat <- finaldat %>% dplyr::mutate(depression = rowMeans(finaldat[ ,dep_avg], na.rm=TRUE))
  1. Estimate the mediation model using the PROCESS macro syntax. Provide your code to answer this question. When estimating the model, also ask for bootstrap confidence interval. Use bootstrap sample = 2000, seed = 32861. Provide your code to answer this question.
source("process.R")
## 
## ********************* PROCESS for R Version 4.3.1 ********************* 
##  
##            Written by Andrew F. Hayes, Ph.D.  www.afhayes.com              
##    Documentation available in Hayes (2022). www.guilford.com/p/hayes3   
##  
## *********************************************************************** 
##  
## PROCESS is now ready for use.
## Copyright 2020-2023 by Andrew F. Hayes ALL RIGHTS RESERVED
## Workshop schedule at http://haskayne.ucalgary.ca/CCRAM
## 
mediation <- process(data = finaldat, y = "attachmentanxiety", x = "depression" , m = "reactive", 
        model = 4, effsize = 1, total = 1, stand = 1, 
        boot = 2000, modelbt = 1, seed = 32861 )
## 
## ********************* PROCESS for R Version 4.3.1 ********************* 
##  
##            Written by Andrew F. Hayes, Ph.D.  www.afhayes.com              
##    Documentation available in Hayes (2022). www.guilford.com/p/hayes3   
##  
## *********************************************************************** 
##                          
## Model : 4                
##     Y : attachmentanxiety
##     X : depression       
##     M : reactive         
## 
## Sample size: 185
## 
## Custom seed: 32861
## 
## 
## *********************************************************************** 
## Outcome Variable: reactive
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.3140    0.0986    0.7433   20.0146    1.0000  183.0000    0.0000
## 
## Model: 
##                coeff        se         t         p      LLCI      ULCI
## constant      4.4318    0.3069   14.4397    0.0000    3.8262    5.0373
## depression   -0.7492    0.1675   -4.4738    0.0000   -1.0796   -0.4188
## 
## Standardized coefficients:
##                coeff
## depression   -0.3140
## 
## *********************************************************************** 
## Outcome Variable: attachmentanxiety
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.3514    0.1235    0.6395   12.8230    2.0000  182.0000    0.0000
## 
## Model: 
##                coeff        se         t         p      LLCI      ULCI
## constant      3.0274    0.4164    7.2706    0.0000    2.2059    3.8490
## depression    0.6059    0.1636    3.7034    0.0003    0.2831    0.9287
## reactive     -0.1451    0.0686   -2.1166    0.0357   -0.2804   -0.0098
## 
## Standardized coefficients:
##                coeff
## depression    0.2707
## reactive     -0.1547
## 
## ************************ TOTAL EFFECT MODEL *************************** 
## Outcome Variable: attachmentanxiety
## 
## Model Summary: 
##           R      R-sq       MSE         F       df1       df2         p
##      0.3193    0.1019    0.6516   20.7711    1.0000  183.0000    0.0000
## 
## Model: 
##                coeff        se         t         p      LLCI      ULCI
## constant      2.3843    0.2874    8.2966    0.0000    1.8173    2.9513
## depression    0.7146    0.1568    4.5575    0.0000    0.4053    1.0240
## 
## Standardized coefficients:
##                coeff
## depression    0.3193
## 
## *********************************************************************** 
## Bootstrapping progress:
## 
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## 
## ************ TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y ************
## 
## Total effect of X on Y:
##      effect        se         t         p      LLCI      ULCI      c_cs
##      0.7146    0.1568    4.5575    0.0000    0.4053    1.0240    0.3193
## 
## Direct effect of X on Y:
##      effect        se         t         p      LLCI      ULCI     c'_cs
##      0.6059    0.1636    3.7034    0.0003    0.2831    0.9287    0.2707
## 
## Indirect effect(s) of X on Y:
##             Effect    BootSE  BootLLCI  BootULCI
## reactive    0.1087    0.0624   -0.0064    0.2383
## 
## Completely standardized indirect effect(s) of X on Y:
##             Effect    BootSE  BootLLCI  BootULCI
## reactive    0.0486    0.0273   -0.0028    0.1046
## 
## ********** BOOTSTRAP RESULTS FOR REGRESSION MODEL PARAMETERS **********
## 
## Outcome variable: reactive
## 
##                Coeff  BootMean    BootSE  BootLLCI  BootULCI
## constant      4.4318    4.4370    0.2930    3.8578    5.0013
## depression   -0.7492   -0.7519    0.1575   -1.0647   -0.4425
## ----------
## Outcome variable: attachmentanxiety
## 
##                Coeff  BootMean    BootSE  BootLLCI  BootULCI
## constant      3.0274    3.0299    0.4395    2.1433    3.8507
## depression    0.6059    0.6056    0.1607    0.2904    0.9208
## reactive     -0.1451   -0.1455    0.0755   -0.2815    0.0109
## 
## ******************** ANALYSIS NOTES AND ERRORS ************************ 
## 
## Level of confidence for all confidence intervals in output: 95
## 
## Number of bootstraps for percentile bootstrap confidence intervals: 2000
  1. What are the values of unstandardized \(\hat{a}\) , \(\hat{b}\), and \(\hat{c}\)? \(\hat{a}\) = -0.62 \(\hat{b}\) = -0.16 \(\hat{c}\) = 0.59
  2. What is the 95% confidence interval of the (unstandardized) mediation effect? Is the mediation effect significant at \(\alpha\) = .05? The 95% confidence interval is (-0.27, -0.58) and is significant at \(\alpha\) =.05.
  3. Calculate these effect sizes for mediation effect.
    1. The proportion of mediation effect to total effect between X to Y = 0.687.
    2. \(\upsilon\) = 0.00185
describe(finaldat$depression)
describe(finaldat$attachmentanxiety)

\(\upsilon\) = (-0.62)2-(.122) * ((-.16)2-(.052)) * ((0.03)2/(0.062)) ((0.384-0.015) * (0.026-0.003) * ((0.0009)/(0.0036)))

(0.37)*(0.02)*(0.25)
## [1] 0.00185
  1. Write a paragraph to report your results. In this mediation model, the researchers hypothesized that the relationship between attachment anxiety and depression would be mediated by reactive coping. Mediation analysis revealed a significant positive relationship between depression and reactive coping (unstandardized \(\hat{a}\) = -0.62, standardized \(\hat{a}\) = -0.29, p<0.005). There was also a significant negative relationship between attachment anxiety and reactive coping (unstandardized \(\hat{b}\) = -0.16, standardized \(\hat{b}\) = -0.18, p<0.005). Lastly, there was a significant relationship between depression and attachment anxiety (unstandardized \(\hat{c}\) = 0.59, standardized \(\hat{c}\) = 0.30, p<0.005). The mediation effect was not significant at 0.1014 (CI = 0.0254, 0.1890; \(\upsilon\) = 0.00185).
ndf <- create_node_df(
  n         = 3,
  label     = c( "Depression","Reactive Coping", "Attachment Anxiety"),
  shape     = rep("rectangle", 3),
  style     = "empty",
  fontsize  = 6,
  fixedsize = TRUE,
  height    = .5,
  width     = .75,
  color     = "gray80",
  x         = c(1, 2, 3),
  y         = c(1, 2, 1)
)
edf <- create_edge_df(
  from     = c(1, 1, 2),
  to       = c(2, 3, 3),
  label    = c("-0.62", "0.59", "-0.16"),
  fontsize = 6,
  minlen   = 1,
  color    = "gray80",
  )
graph <- create_graph(
  nodes_df = ndf,
  edges_df = edf
  )
graph %>%
  render_graph()