This is computer code for analyzing the Brief Mood Introspection Scale (BMIS) data files for the R Statistical Environment open source software

Mayer, John D.

English

UNH Scholars' Repository

University of New HampshireUniversity of New Hampshire Scholars' RepositoryUNH Personality Lab Research Institutes, Centers and Programs1-1-1988Brief Mood Introspection Scale (BMIS): Open-Source CodeJohn D. MayerUniversity of New Hampshire, Durham, jack.mayer@unh.eduFollow this and additional works at: https://scholars.unh.edu/personality_labPart of the Personality and Social Contexts Commons, and the Social Psychology CommonsThis Software Code is brought to you for free and open access by the Research Institutes, Centers and Programs at University of New HampshireScholars' Repository. It has been accepted for inclusion in UNH Personality Lab by an authorized administrator of University of New HampshireScholars' Repository. For more information, please contact nicole.hentz@unh.edu.Recommended CitationThe authors give their permission for general research use. Please, though, credit the original article as the source for the scale. Theproper APA citation is: Mayer, J. D., & Gaschke, Y. N. (1988). The experience and meta-experience of mood. Journal of Personalityand Social Psychology, 55, 102-111.######################################## PART 1 Basics and Preliminaries######################################## PART 1 Basics and Preliminaries######################################## PART 1 Basics and Preliminaries######################################## PART 1 Basics and Preliminaries#               SOME BEGINNING FILES, DOCUMENTATION, STARTING COMMANDS#               SOME BEGINNING FILES, DOCUMENTATION, STARTING COMMANDS#               SOME BEGINNING FILES, DOCUMENTATION, STARTING COMMANDS#               SOME BEGINNING FILES, DOCUMENTATION, STARTING COMMANDS# NOTE BENE: You will need to change the commented line below that begins "# setwd" to indicate # a valid directory in which you would like to work (if you have not done so already). # To do so, remove the number sign and insert the revised computer address of the working directory# of your choice.setwd ("C:/Users/jdmayer/Box Sync/SFW/BMIS/2018-07-20-BMISAnly")######################## We'll redirect this week's work to an output file called "outputfile.txt"######################## You'll still see output in the console, but it will also go to that output file.#the output file ensures all the output will be preservedsink("outputfile.txt")#               TABLE OF DATA FILES FOR THIS CODE#               TABLE OF DATA FILES FOR THIS CODE#               TABLE OF DATA FILES FOR THIS CODE#               TABLE OF DATA FILES FOR THIS CODE# Throughout this code I will be manipulating data and creating new data "objects"# That said, a few files will be employed repeatedly. These are: # Data file name      Data file description# BMIS1998R.txt       starting file from Mayer & Gaschke 1999, 465 cases# dtorig              the object (aka, data.frame), or (in this case) matrix that BMIS1998R.txt is read into # dtfactr             the dtitms file with missing data removed (i.e.,all participants with  #                     complete responses for the 16 items), and ready for correlational/factor analysis#               LOAD PACKAGES USED IN THIS CODE#               LOAD PACKAGES USED IN THIS CODE#               LOAD PACKAGES USED IN THIS CODE#               LOAD PACKAGES USED IN THIS CODE# A list of the packages to find, copy and load in # order to carry out what we need to do follows# "psych"       The psych package allows us a convenient means of carrying out basic exploratory factor analyses# "lavaan"      Lavaan allows for confirmatory factor analysisinstall.packages("SparseM")library(SparseM)install.packages("car")library(car)install.packages("psych")library(psych)install.packages("lavaan")library(lavaan)install.packages("GPArotation")library(GPArotation)#                READ IN DATA FILE AND LOOK AT IT#                READ IN DATA FILE AND LOOK AT IT#                READ IN DATA FILE AND LOOK AT IT#                READ IN DATA FILE AND LOOK AT IT#                READ IN DATA FILE AND LOOK AT IT# read in data filedtorig <- read.table("BMIS1998R.txt", header = TRUE, sep="")#see the beginning of the filehead (dtorig)#see the end of the filetail (dtorig)#                SUMMARIZE THE DATA#                SUMMARIZE THE DATA#                SUMMARIZE THE DATA#                SUMMARIZE THE DATA#find some descriptive statistics for the datasummary (dtorig)#                BUILD SOME VARIATIONS OF THE DATA FILE#                BUILD SOME VARIATIONS OF THE DATA FILE#                BUILD SOME VARIATIONS OF THE DATA FILE#                BUILD SOME VARIATIONS OF THE DATA FILE# set up the "dtritems" file--a copy of dtorig that will hold re-codeditemsdtritems <-(dtorig)#                CREATE REVERSE-SCORED VERSIONS OF THE 16 MOOD ITEMS#                CREATE REVERSE-SCORED VERSIONS OF THE 16 MOOD ITEMS#                CREATE REVERSE-SCORED VERSIONS OF THE 16 MOOD ITEMS#                CREATE REVERSE-SCORED VERSIONS OF THE 16 MOOD ITEMS#                CREATE REVERSE-SCORED VERSIONS OF THE 16 MOOD ITEMS#                CREATE REVERSE-SCORED VERSIONS OF THE 16 MOOD ITEMS# start by copying lively into its own vector (a column)vlively <-dtorig[,'lively']# look at the resulthead (vlively)# reverse code it and look at it to make sure it is right  rlively = recode(vlively, '1=4; 2=3; 3=2; 4=1')head (rlively)# now add the new vector into a new datafile# the dollar sign in the next command is shorthand for a column in dt2dtritems$rlively <- rlively   # repeat process for remaining 15 adjectivesvhappy <-dtorig[,'happy']vsad <-dtorig[,'sad']vtired <-dtorig[,'tired']vcaring <-dtorig[,'caring']vcontent <-dtorig[,'content']vgloomy <-dtorig[,'gloomy']vjittery <-dtorig[,'jittery']vdrowsy <-dtorig[,'drowsy']vgrouchy <-dtorig[,'grouchy']vpeppy <-dtorig[,'peppy']vnervous <-dtorig[,'nervous']vcalm <-dtorig[,'calm']vloving <-dtorig[,'loving']vfedup <-dtorig[,'fedup']vactive <-dtorig[,'active']rhappy = recode(vhappy, '1=4; 2=3; 3=2; 4=1')rsad = recode(vsad, '1=4; 2=3; 3=2; 4=1')rtired = recode(vtired, '1=4; 2=3; 3=2; 4=1')rcaring = recode(vcaring, '1=4; 2=3; 3=2; 4=1')rcontent = recode(vcontent, '1=4; 2=3; 3=2; 4=1')rgloomy = recode(vgloomy, '1=4; 2=3; 3=2; 4=1')rjittery = recode(vjittery, '1=4; 2=3; 3=2; 4=1')rdrowsy = recode(vdrowsy, '1=4; 2=3; 3=2; 4=1')rgrouchy = recode(vgrouchy, '1=4; 2=3; 3=2; 4=1')rpeppy = recode(vpeppy, '1=4; 2=3; 3=2; 4=1')rnervous = recode(vnervous, '1=4; 2=3; 3=2; 4=1')rcalm = recode(vcalm, '1=4; 2=3; 3=2; 4=1')rloving = recode(vloving, '1=4; 2=3; 3=2; 4=1')rfedup = recode(vfedup, '1=4; 2=3; 3=2; 4=1')ractive = recode(vactive, '1=4; 2=3; 3=2; 4=1')# now add the columns into the new data.frame (object) dtritems# and check the resultdtritems$rhappy <- rhappydtritems$rsad <- rsaddtritems$rtired <- rtireddtritems$rcaring <- rcaringdtritems$rcontent <- rcontentdtritems$rgloomy <- rgloomydtritems$rjittery <- rjitterydtritems$rdrowsy <- rdrowsydtritems$rgrouchy <- rgrouchydtritems$rpeppy <- rpeppydtritems$rnervous <- rnervousdtritems$rcalm <- rcalmdtritems$rloving <- rlovingdtritems$rfedup <- rfedupdtritems$ractive <- ractivehead (dtritems)tail (dtritems)#                                          HANDLE MISSING DATA  #                                          HANDLE MISSING DATA  #                                          HANDLE MISSING DATA  #                                          HANDLE MISSING DATA  #                                          HANDLE MISSING DATA  #                                          HANDLE MISSING DATA  # This creates a new version of the datafile, dtfactr in which only the test items are included# and only those with no missing data# Omit subjno, age, sex, and overall (latter because all the data is missing)dttemp1 <-subset(dtorig, select=-c(group, subjno, age, sex, overall))head (dttemp1)# Now ready to create dtfactr--basically, a file of complete items, byremoving the remaining cases with missing data and describe itdtfactr <- na.omit(dttemp1)describe(dtfactr)ct <-(fa(dtfactr,2))# If I use "summary" to summarize the data, I notice it lacks an overall N, so I'll switch to "describe" from psychsummary (dtfactr)describe(dtfactr)# The describe function (from "psych" has a specific argument for handling missing data. So, I could# use the "describe" function itself to see what the original (and reverse-coded) data looks like# when excluding the missing data. describe (dtritems, na.rm = TRUE, check=TRUE)############################################################# PART 2 Correlations and Reliabilities ############################################################# PART 2 Correlations and Reliabilities ############################################################# PART 2 Correlations and Reliabilities #                        CORRELATIONAL ANALYSES#                        CORRELATIONAL ANALYSES#                        CORRELATIONAL ANALYSES#                        CORRELATIONAL ANALYSES# Using what we have learned, let's conduct some correlational analyses# First, I'll copy four variables--the targeted items--into a data.frame using the select command# I make sure one is reverse-scored. I want to double-check that it correlates r = -1.o with its original version# the new data object will be "dt4corrs" or, data "for" correlationsdt4corrs <-subset(dtfactr, select=c(happy, lively, active, rlively))describe (dt4corrs)cor (dt4corrs)# Or, you can use the function from the psych package--"use" is a missing values option# Remember that R is case sensitivelowerCor(dt4corrs, digits=2, use="pairwise")#                        RELIABILITY ANALYSES#                        RELIABILITY ANALYSES#                        RELIABILITY ANALYSES#                        RELIABILITY ANALYSES#Now, let's find the reliability of the pleasant-unpleasant scale. # According to Mayer & Gashke (1988), the scale is composed of# active, calm caring, content, happy, lively, loving and peppy, and (reversed)# drowsy, fedup, gloomy, grouchy, jittery, nervous, sad, and tired. The reliability is supposed# to be .83 according to the article. Is it?# To find out, we'll first set up a file with the targeted items.plsunp <-subset(dtritems, select=c(active, calm, caring, content, happy, lively, loving,                                   peppy, rdrowsy, rfedup, rgloomy, rgrouchy, rjittery, rnervous, rsad, rtired))head (plsunp)#and now, the alphaalpha(plsunp)# Note 1: The default is to use pairwise correlations when missing data is present# Note 2: Although we created reverse-scored versions of the items ourselves, in the alpha# procedure you can use the "keys" argument to reverse-key items like this:# First, you set up a new data file with the 16 original items.plsunp2 <-subset(dtritems, select=c('lively', 'happy', 'sad', 'tired','caring',                                     'content', 'gloomy', 'jittery', 'drowsy', 'grouchy',                                     'peppy', 'nervous', 'calm', 'loving', 'fedup', 'active'))# Second, set up a vector of columns to be reversed:reversethese <- c('drowsy', 'fedup', 'gloomy', 'grouchy', 'jittery', 'nervous', 'sad', 'tired')# Third, run alpha with the additional argument as shown:alpha(plsunp2, keys=reversethese)####################################################################### PART 3 Saving a handy file####################################################################### PART 3 Saving a handy file####################################################################### PART 3 Saving a handy file#                        OUTPUT FILES WE WOULD LIKE TO KEEP#                        OUTPUT FILES WE WOULD LIKE TO KEEP#                        OUTPUT FILES WE WOULD LIKE TO KEEP#                        OUTPUT FILES WE WOULD LIKE TO KEEP# direct output to a file in the working directory, then read it back in again and check ithead (dtritems)write.table(dtritems, "dtritems_BMIS_N=465.txt", append=FALSE)write.table(dtritems, "dtritems_BMIS_N=465.txt", append=FALSE)checkfile <- read.table("dtritems_BMIS_N=465.txt", header = TRUE, sep="")head (checkfile) ################################################################# PART2 Factor Analysis################################################################# PART2 Factor Analysis################################################################# PART2 Factor Analysis################################################################# PART2 Factor Analysis# Here, I'll demonstrate a few prelimnaries recommended by William Revelle, the author of "Psych"# that definitely have a fun aspect to them.# 1. pairs.panels creates a rather weird talbe with the correlations in the upper triangular portion# of the matrix and thumbnail scatterplots of each correlation in the lower triangular portion# 2. lowerCor is just the correlation matrix (I would generally have looked at this early-on)# 3. corPLot creates a heat-map believe it or not of the correlations (the most fun)# Revelle also suggests iclust, a cluster analysis, but we are doing factor analysis here, and that is# a different approach so I don't include it here, although if you areinterested it is iclust(r.mat=dtfactr)# Honorable mention, but more complex to implement/explain right away,is a bifactor model via the # coefficient omega approach "> omega(dtfactr)" that checks for a hierarchical model.pairs.panels(dtfactr)lowerCor(dtfactr)corPlot(dtfactr)# finally, a parallel plot to illustrate the possible number of factorsfa.parallel(dtfactr[1:6], main="Parallel analysis of BMIS")#                                                   EXPLORATORY FACTORANALYSIS#                                                   EXPLORATORY FACTORANALYSIS#                                                   EXPLORATORY FACTORANALYSIS#                                                   EXPLORATORY FACTORANALYSIS#                                                   EXPLORATORY FACTORANALYSIS#                                                   EXPLORATORY FACTORANALYSIS# The basic factor analysis program for R is called factanal# and produces a maximum likelihood factor analysis# First, we'll need a file of the original items (no reverse scoring, and nothing but the items themselves)# We have created it above: dtfactr #                            ATTEMPT AT ORIGINAL 1998 PRINCIPLE AXIS EXTRACTION#                            ATTEMPT AT ORIGINAL 1998 PRINCIPLE AXIS EXTRACTION#                            ATTEMPT AT ORIGINAL 1998 PRINCIPLE AXIS EXTRACTION#                            ATTEMPT AT ORIGINAL 1998 PRINCIPLE AXIS EXTRACTION#                            ATTEMPT AT ORIGINAL 1998 PRINCIPLE AXIS EXTRACTION# Checking the original report (and the recovered data)# Checking the original report (and the recovered data)# Checking the original report (and the recovered data)# To match the original 1988 paper, we need a principle axis extraction#fa is the factor analysis program developed by Bill Revelle#check out this principal axis factoring and compare to SPSSpa <- fa(dtfactr, 2, fm="pa", rotate="none")print (pa)plot(pa)# Is it similar to the original report?# To check the eigenvalues (helpful for a scree plot), # we call on the nFactors program (loaded as a part of psych), # and ask for the eigenvaluesev <-eigen(cor(dtfactr))ev#the first set correspond to the eigenvalues of the factors# A circumplex representation# A circumplex representation# A circumplex representation# A circumplex representation# For fun, let's take the loadings and see if they fit a circumplex structure--# a simple structure around a circlecircfact <-(fa(dtfactr,2))circfactplot(circfact, title="circumplex Structure")ct <-circ.tests(circfact, loading = TRUE)ct# check out the plot that appears (click on the plot tab in R Studio)#                            PRINCIPLE AXIS EXTRACTIONS#                            PRINCIPLE AXIS EXTRACTIONS#                            PRINCIPLE AXIS EXTRACTIONS#                            PRINCIPLE AXIS EXTRACTIONS# The 2-factor shows the JPSP solution. The 3 and 4 factor shows a couple other solutions# we looked at at the timepa1 <- fa(dtfactr, 1, fm="pa", rotate="none")print (pa1)pa2 <- fa(dtfactr, 2, fm="pa", rotate="none")print (pa2)pa3 <- fa(dtfactr, 3, fm="pa", rotate="none")print (pa3)pa4 <- fa(dtfactr, 4, fm="pa", rotate="none")print (pa4)#                            MAXIMUM LIKELIHOOD#                            MAXIMUM LIKELIHOOD#                            MAXIMUM LIKELIHOOD#                            MAXIMUM LIKELIHOOD      #                            MAXIMUM LIKELIHOOD #nowadays, maximum likelihood often is conducted. Here is an ML analysis for 2, 3, and 4 factorsml1 <- fa(dtfactr, 1, fm="ml", rotate="none")print (ml1)ml2 <- fa(dtfactr, 2, fm="ml", rotate="none")print (ml2)ml3 <- fa(dtfactr, 3, fm="ml", rotate="none")print (ml3)ml4 <- fa(dtfactr, 4, fm="ml", rotate="none")print (ml4)ml5 <- fa(dtfactr, 5, fm="ml", rotate="none")print (ml5)# End of code for exploratory fa#                             CATEGORICAL DATA VERSION--MAXIMUM LIKELIHOOD#                             CATEGORICAL DATA VERSION--MAXIMUM LIKELIHOOD#                             CATEGORICAL DATA VERSION--MAXIMUM LIKELIHOOD#                             CATEGORICAL DATA VERSION--MAXIMUM LIKELIHOOD#                             OPTIONALfacat1 = fa.poly(dtfactr, fm="wls", nfactors=1, rotate="oblimin")facat1facat2 = fa.poly(dtfactr, fm="wls", nfactors=2, rotate="oblimin")facat2facat3 = fa.poly(dtfactr, fm="wls", nfactors=3, rotate="oblimin")facat3facat4 = fa.poly(dtfactr, fm="wls", nfactors=4, rotate="oblimin")facat4facat5 = fa.poly(dtfactr, fm="wls", nfactors=5, rotate="oblimin")facat5#                     A GOOD "EXPRESS" SOLUTION IN LAVAAN BEGINNING WITH THE SCALES IN JPSP 1998#                     A GOOD "EXPRESS" SOLUTION IN LAVAAN BEGINNING WITH THE SCALES IN JPSP 1998#                     A GOOD "EXPRESS" SOLUTION IN LAVAAN BEGINNING WITH THE SCALES IN JPSP 1998# If we use a simplified model, keeping items loading on one scale, this works# These assignments are based on placing items on the factor on which they load highest,# and that meet the criterion of > .35.# It omits items that load near-equally on both scales. For details see the accompanying # "Technical Lab Documentation" document, Table 4.2####  STEP 1####  STEP 1####  STEP 1####  STEP 1twofact.model <- 'f1 =~ lively + happy + content + peppy + activef2 =~ gloomy + jittery + grouchy + fedup + sad'#then you fit your model in a particular way (using confirmatory factor analysis)fit <- cfa(twofact.model, data = dtfactr)#then you take a look at the resultssummary (fit, fit.measures=TRUE, modindices = TRUE)# For a better fit, I'll try a three-factor model based on the three-factor ML solution# All items on a scale (a) load highest on the scale (b) > |.35|, (c) < |.40| on any other# scale (See Technical-Lab-Documentation-BMIS-Analyses-2016-09-05-1631)####  STEP 2####  STEP 2####  STEP 2####  STEP 2threefact.model <- 'f1 =~ lively + happy + tired + drowsy + peppy + activef2 =~ sad + content + gloomy + jittery + grouchy + nervous + calm + fedupf3 =~ caring + loving' #then you fit your model in a particular way (using confirmatory factor analysis)fit <- cfa(threefact.model, data = dtfactr)#then you take a look at the resultssummary (fit, fit.measures=TRUE, modindices = TRUE)# Next approach: Drop problematic items# two factor model--dropping the third factor--without "content" (identified through MIs) and happy on both factorstwofact.model <- 'f1 =~ lively + happy + peppy + activef2 =~ gloomy + jittery + grouchy + fedup + sad + happy'#then you fit your model in a particular way (using confirmatory factor analysis)fit <- cfa(twofact.model, data = dtfactr)#then you take a look at the resultssummary (fit, fit.measures=TRUE, modindices = TRUE)# Much better fit: Do we still have a good alpha?f1 <-subset(dtritems, select=c('lively', 'happy', 'peppy','active'))f2 <-subset(dtritems, select=c('sad', 'gloomy', 'jittery', 'grouchy', 'fedup', 'rhappy'))alpha(f1)alpha(f2)sink()

Brief Mood Introspection Scale (BMIS): Open-Source Code

University of New Hampshire

https://scholars.unh.edu/cgi/viewcontent.cgi?article=1015&context=personality_lab

Brief Mood Introspection Scale (BMIS): Open-Source Code

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