xxUsing R to score personality scales

Using R to score personality scales∗

William Revelle Northwestern University

February 25, 2013

∗Part of a set of tutorials for the psych package.

Contents

1 Overview for the impatient 2 An example x2.1 Getting the data xx2.1.1 Copy the data from another program using the copy and paste commands of your operating system xx2.1.2 An example data set 3 Scoring scales: an example x3.1 Long output x3.2 Get the actual scores for analysis. 4 The example, continued 5 Even more analysis x5.1 Exploring a real data set

Abstract

The psych package (Revelle,x2013) was developed to perform most basic psychometric functions using R (R Development Core Team,x2012) One frequently called upon is the need to take a set of items (e.g., a questionnaire) and score one or more scales on that questionnaire. Scores for subsequent analysis, reliabilities and intercorrelations are easily done using the score.items function.

Suppose you have given a questionnaire with some items (n) to some participants (N). You would like to create scale scores for each person on k different scales. This may be done using the psych package in R. The following assumes that you have installed R and downloaded the psych package.

1 Overview for the impatient Remember, before using psych you must make it active:

    xxlibrary(psych)

Enter the data into a spreadsheet (Excel or Numbers) or a text file using a text editor (Word, Pages, BBEdit). The first line of the file should include names for the variables (e.g., Q1, Q2, ... Qn).

Copy the data to the clipboard (using the normal copy command for your spreadsheet or word processor).

Read the data into R using the read.clipboard command. (Depending upon your data file, this might need to be read.clipboard.csv (for comma separated data fields) or read.clipboard.tab (for tab separated data fields).

Construct a set of scoring keys for the scales you want to score. This is simply the item numbers that go into each scale. A negative sign implies that the item will be reverse scored.

Use the score.items function to score the scales.

Use the output for score.items for further analysis.

2 An example Suppose we have 12 items for 20 subjects. The items represent 4 different scales: Positive Energetic Arousal (EAp), Negative Energetic Arousal (EAn), Tense Arousal (TAp) and negative Tense Arousal (TAn, also known as being relaxed). These four scales can also be thought of a forming two higher order constructs, Energetic Arousal (EA) and Tense Arousal (TA). EA is just EAp - EAn, and similarly TA is just TAp - TAn.

2.1 Getting the data There are of course many ways to enter data into R. Reading from a local file using read.table is perhaps the most preferred. You first need to find the file and then read it. This can be done with the file.choose and read.table functions:

    xxfile.namex<-xfile.choose()xxmy.datax<-xread.table(file.name)

file.choose opens a search window on your system just like any open file command does. It doesn’t actually read the file, it just finds the file. The read command is also necessary.

2.1.1 Copy the data from another program using the copy and paste commands of your operating system However, many users will enter their data in a text editor or spreadsheet program and then want to copy and paste into R. This may be done by using read.table and specifying the input file as “clipboard" (PCs) or “pipe(pbpaste)" (Macs). Alternatively, the read.clipboard set of functions are perhaps more user friendly:

read.clipboard is the base function for reading data from the clipboard.

read.clipboard.csv for reading text that is comma delimited.

read.clipboard.tab for reading text that is tab delimited (e.g., copied directly from an Excel file). read.clipboard.lower for reading input of a lower triangular matrix with or without

a diagonal. The resulting object is a square matrix.

read.clipboard.upper for reading input of an upper triangular matrix.

read.clipboard.fwf for reading in fixed width fields (some very old data sets)

For example, given a data set copied to the clipboard from a spreadsheet, just enter the command

        xx>xmy.datax<-xread.clipboard()

This will work if every data field has a value and even missing data are given some values (e.g., NA or -999). If the data were entered in a spreadsheet and the missing values were just empty cells, then the data should be read in as a tab delimited or by using the

     read.clipboard.tab function.

        xx>xmy.datax<-xread.clipboard(sep="\t")xxx#definexthextabxoption,xor
     xx>xmy.tab.datax<-xread.clipboard.tab()xxx#justxusexthexalternativexfunction

For the case of data in fixed width fields (some old data sets tend to have this format), copy to the clipboard and then specify the width of each field (in the example below, the first variable is 5 columns, the second is 2 columns, the next 5 are 1 column the last 4 are 3 columns).

        xx>xmy.datax<-xread.clipboard.fwf(widths=c(5,2,rep(1,5),rep(3,4))

2.1.2 An example data set Consider the data in Tablex1. Read them into the clipboard and go. (These data are the first 20 cases from the msq data set in the psych package).

Tablex1: A sample data file with 12 items for 20 subjects.

xxxxactivexalertxarousedxsleepyxtiredxdrowsyxanxiousxjitteryxnervousxcalmxrelaxedxat-ease
xx1xxxxxxx1xxxxx1xxxxxxx1xxxxxx1xxxxx1xxxxxx1xxxxxxx1xxxxxxx1xxxxxxx1xxxx1xxxxxxx1xxxxxxx1
xx2xxxxxxx1xxxxx1xxxxxxx0xxxxxx1xxxxx1xxxxxx1xxxxxxx0xxxxxxx0xxxxxxx0xxxx1xxxxxxx1xxxxxxx1
xx3xxxxxxx1xxxxx0xxxxxxx0xxxxxx0xxxxx1xxxxxx0xxxxxxx0xxxxxxx0xxxxxxx0xxxx1xxxxxxx2xxxxxxx2
xx4xxxxxxx1xxxxx1xxxxxxx1xxxxxx1xxxxx1xxxxxx1xxxxxxx1xxxxxxx3xxxxxxx2xxxx1xxxxxxx2xxxxxxx1
xx5xxxxxxx2xxxxx1xxxxxxx2xxxxxx1xxxxx1xxxxxx1xxxxxxNAxxxxxxx1xxxxxxx0xxxx3xxxxxxx3xxxxxxx3
xx6xxxxxxx2xxxxx1xxxxxxx1xxxxxx2xxxxx2xxxxxx2xxxxxxNAxxxxxxx0xxxxxxx0xxxx2xxxxxxx2xxxxxxx1
xx7xxxxxxx0xxxxx1xxxxxxx0xxxxxx2xxxxx3xxxxxx3xxxxxxNAxxxxxxx0xxxxxxx0xxxx2xxxxxxx2xxxxxxx1
xx8xxxxxxx0xxxxx0xxxxxxx0xxxxxx1xxxxx2xxxxxx1xxxxxxNAxxxxxxx0xxxxxxx0xxxx1xxxxxxx2xxxxxxx0
xx9xxxxxxx1xxxxx0xxxxxxx1xxxxxx2xxxxx0xxxxxx2xxxxxxNAxxxxxxx1xxxxxxx0xxxx0xxxxxxx2xxxxxxx2
xx10xxxxxx0xxxxx2xxxxxxx0xxxxxx2xxxxx2xxxxxx2xxxxxxNAxxxxxxx1xxxxxxx0xxxx2xxxxxxx2xxxxxxx1
xx11xxxxxx0xxxxx0xxxxxxx0xxxxxx3xxxxx2xxxxxx2xxxxxxNAxxxxxxx0xxxxxxx0xxxx2xxxxxxx2xxxxxxx2
xx12xxxxxx1xxxxx1xxxxxxx0xxxxxx1xxxxx1xxxxxx1xxxxxxNAxxxxxxx1xxxxxxx0xxxx1xxxxxxx1xxxxxxx0
xx13xxxxxx0xxxxx0xxxxxxx0xxxxxx3xxxxx3xxxxxx2xxxxxxNAxxxxxxx1xxxxxxx0xxxx0xxxxxxx2xxxxxxx0
xx14xxxxxx2xxxxx1xxxxxxx1xxxxxx1xxxxx0xxxxxx0xxxxxxNAxxxxxxx0xxxxxxx0xxxx2xxxxxxx2xxxxxxx1
xx15xxxxxx0xxxxx2xxxxxxx0xxxxxx0xxxxx2xxxxxx1xxxxxxNAxxxxxxx0xxxxxxx0xxxx3xxxxxxx3xxxxxxx3
xx16xxxxxx0xxxxx0xxxxxxx0xxxxxx3xxxxx3xxxxxx3xxxxxxNAxxxxxxx1xxxxxxx0xxxx1xxxxxxx1xxxxxxx1

xx17xxxxxx0xxxxx1xxxxxxx1xxxxxx1xxxxx1xxxxxx1xxxxxxNAxxxxxxx0xxxxxxx0xxxx1xxxxxxx1xxxxxxx1
xx18xxxxxx3xxxxx2xxxxxxx0xxxxxx2xxxxx2xxxxxx3xxxxxxNAxxxxxxx0xxxxxxx0xxxx3xxxxxxx3xxxxxxx3
xx19xxxxxx0xxxxx0xxxxxxx0xxxxxx3xxxxx3xxxxxx2xxxxxxNAxxxxxxx0xxxxxxx0xxxx2xxxxxxx1xxxxxxx0

xx20xxxxxx0xxxxx1xxxxxxx0xxxxxx1xxxxx2xxxxxx1xxxxxxNAxxxxxxx0xxxxxxx0xxxx3xxxxxxx2xxxxxxx2xx

   xxlibrary(psych)xxmy.datax<-xread.clipboard.tab()xx#tabxdelimitedxdataxfromxaxspreadsheetxor

xxmy.data<-xread.clipboard()xxx#dataxfromxaxtextxeditorxwithxspacesxbetweenxthexfields.

xxdescribe(my.data)xx#xtoxmakexsurexyouxgotxthexrightxdataxin.xx

   xx>xdescribe(my.data)xx#xtoxmakexsurexyouxgotxthexrightxdataxin.
xxxxxxxxxxvarxxnxmeanxxxsdxmedianxtrimmedxxmadxminxmaxxrangexxskewxkurtosisxxxse
xxactivexxxx1x20x0.75x0.91xxxx0.5xxxx0.62x0.74xxx0xxx3xxxxx3xx0.87xxxx-0.37x0.20
xxalertxxxxx2x20x0.80x0.70xxxx1.0xxxx0.75x0.74xxx0xxx2xxxxx2xx0.25xxxx-1.06x0.16
xxarousedxxx3x20x0.40x0.60xxxx0.0xxxx0.31x0.00xxx0xxx2xxxxx2xx1.06xxxx-0.01x0.13
xxsleepyxxxx4x20x1.55x0.94xxxx1.0xxxx1.56x1.48xxx0xxx3xxxxx3xx0.22xxxx-1.10x0.21
xxtiredxxxxx5x20x1.65x0.93xxxx2.0xxxx1.69x1.48xxx0xxx3xxxxx3x-0.05xxxx-1.06x0.21
xxdrowsyxxxx6x20x1.50x0.89xxxx1.0xxxx1.50x1.48xxx0xxx3xxxxx3xx0.21xxxx-0.89x0.20
xxanxiousxxx7xx4x0.50x0.58xxxx0.5xxxx0.50x0.74xxx0xxx1xxxxx1xx0.00xxxx-2.44x0.29
xxjitteryxxx8x20x0.50x0.76xxxx0.0xxxx0.38x0.00xxx0xxx3xxxxx3xx1.70xxxxx3.00x0.17
xxnervousxxx9x20x0.15x0.49xxxx0.0xxxx0.00x0.00xxx0xxx2xxxxx2xx2.94xxxxx7.68x0.11
xxcalmxxxxx10x20x1.60x0.94xxxx1.5xxxx1.62x0.74xxx0xxx3xxxxx3xx0.09xxxx-1.10x0.21
xxrelaxedxx11x20x1.85x0.67xxxx2.0xxxx1.81x0.00xxx1xxx3xxxxx2xx0.15xxxx-0.93x0.15

xxat.easexx12x20x1.30x0.98xxxx1.0xxxx1.25x1.48xxx0xxx3xxxxx3xx0.38xxxx-0.96x0.22xx

3 Scoring scales: an example To score particular items on particular scales, we must create a set of scoring keys. These simply tell us which items go on which scales. Note that we can have scales with overlapping items.

Two things to note. The number of variables is the total number of variables (columns) in the data file. You do not need to include all of these items in the scoring keys, but you need to say how many there are. For the keys, items are scored either +1, -1 or 0 (not scored). Just specify the items to score and their direction.

   xxmy.keysx<-xmake.keys(nvars=12,list(EA=c(1:3,-4,-5,-6),TA=c(7:9,-10,-11,-12),

xxxxxxxxxxxxxxxxxxxxxxEApx=1:3,EAn=4:6,TApx=7:9,TAn=10:12))xxmy.scalesx<-xscore.items(my.keys,my.data)
xxmy.scalesxxx#showxthexoutputxxmy.scoresx<-xmy.scales$scores
   xxProducesxthisxoutput:xx>xmy.keysx<-xmake.keys(nvars=12,list(EA=c(1:3,-4,-5,-6),TA=c(7:9,-10,-11,-12),

xx+xxxxxxxxxxxxxxxxxxxxxEApx=1:3,EAn=4:6,TApx=7:9,TAn=10:12))

xx>xmy.scalesx<-xscore.items(my.keys,my.data)xx>xmy.scalesxxx#showxthexoutput
xxCall:xscore.items(keysx=xmy.keys,xitemsx=xmy.data)xxxx(Unstandardized)xAlpha:

xxxxxxxxxxEAxxxTAxxEApxxEAnxxTApxxTAnxxalphax0.77x0.73x0.57x0.86x0.78x0.82xxxxAveragexitemxcorrelation:
xxxxxxxxxxxxxxEAxxxTAxEApxxEAnxxTApxTAnxxaverage.rx0.36x0.31x0.3x0.68x0.54x0.6
xxxxxGuttmanx6*xreliability:xxxxxxxxxxxxxEAxxxTAxxEApxxEAnxxTApxTAn
xxLambda.6x0.92x0.91x0.78x0.94x0.89x0.9xxxxScalexintercorrelationsxcorrectedxforxattenuation

xxxrawxcorrelationsxbelowxthexdiagonal,xalphaxonxthexdiagonal

xxxcorrectedxcorrelationsxabovexthexdiagonal:xxxxxxxxxEAxxxxxTAxxxEApxxxxEAnxxxTApxxxTAn
xxEAxxx0.77x-0.215xx1.15x-1.102xx0.21xx0.38xxTAxx-0.16xx0.728x-0.47xx0.032xx0.84x-1.15
xxEApxx0.76x-0.301xx0.57x-0.569xx0.29xx0.73xxEAnx-0.90xx0.026x-0.40xx0.863x-0.12x-0.11
xxTApxx0.16xx0.630xx0.19x-0.097xx0.78x-0.24xxTAnxx0.30x-0.885xx0.50x-0.091x-0.20xx0.82
xxxxxInxorderxtoxseexthexitemxbyxscalexloadingsxandxfrequencyxcountsxofxthexdata
xxxprintxwithxthexshortxoptionx=xFALSExx

Two things to notice about this output is a) the message about how to get more information (item by scale correlations and frequency counts) and b) that the correlation matrix between the six scales has the raw correlations below the diagonal, alpha reliabilities on the diagonal, and correlations adjusted for reliability above the diagonal. Because EAp and EAn are both part of EA, they correlate with the total more than would be expected given their reliability. Hence the impossible values greater than |0.0|.

3.1 Long output To get the scale correlations corrected for item overlap and scale reliability, we print the object that we found, but ask for long output.

    xxprint(my.scales,short=FALSE)

    xxxxCall:xscore.items(keysx=xmy.keys,xitemsx=xmy.data)xxxx(Unstandardized)xAlpha:
xxxxxxxxxxEAxxxTAxxEApxxEAnxxTApxxTAnxxalphax0.77x0.73x0.57x0.86x0.78x0.82xxxxAveragexitemxcorrelation:
xxxxxxxxxxxxxxEAxxxTAxEApxxEAnxxTApxTAnxxaverage.rx0.36x0.31x0.3x0.68x0.54x0.6xxxxxGuttmanx6*xreliability:
xxxxxxxxxxxxxEAxxxTAxxEApxxEAnxxTApxTAnxxLambda.6x0.92x0.91x0.78x0.94x0.89x0.9xx
xxScalexintercorrelationsxcorrectedxforxattenuationxxxrawxcorrelationsxbelowxthexdiagonal,xalphaxonxthexdiagonal
xxxcorrectedxcorrelationsxabovexthexdiagonal:xxxxxxxxxEAxxxxxTAxxxEApxxxxEAnxxxTApxxxTAn
xxEAxxx0.77x-0.215xx1.15x-1.102xx0.21xx0.38xxTAxx-0.16xx0.728x-0.47xx0.032xx0.84x-1.15
xxEApxx0.76x-0.301xx0.57x-0.569xx0.29xx0.73xxEAnx-0.90xx0.026x-0.40xx0.863x-0.12x-0.11
xxTApxx0.16xx0.630xx0.19x-0.097xx0.78x-0.24xxTAnxx0.30x-0.885xx0.50x-0.091x-0.20xx0.82xxxxItemxbyxscalexcorrelations:
xxxcorrectedxforxitemxoverlapxandxscalexreliabilityxxxxxxxxxxxxxEAxxxxTAxxxEApxxxEAnxxxTApxxxTAn
xxactivexxx0.55x-0.29xx0.75x-0.30xx0.06xx0.40xxalertxxxx0.40x-0.42xx0.58x-0.20xx0.07xx0.57
xxarousedxx0.57xx0.06xx0.60x-0.43xx0.40xx0.17xxsleepyxx-0.79xx0.20x-0.40xx0.86x-0.03x-0.27
xxtiredxxx-0.85x-0.08x-0.60xx0.82x-0.20x-0.02xxdrowsyxx-0.73x-0.05x-0.18xx0.90x-0.05xx0.04
xxanxiousxx0.03xx0.40xx0.24xx0.10xx0.77x-0.05xxjitteryxx0.12xx0.59xx0.17x-0.06xx0.86x-0.24
xxnervousxx0.27xx0.55xx0.23x-0.23xx0.91x-0.16xxcalmxxxxx0.17x-0.78xx0.45xx0.04x-0.30xx0.81
xxrelaxedxx0.26x-0.65xx0.48x-0.06x-0.06xx0.79xxat.easexx0.38x-0.74xx0.53x-0.21x-0.14xx0.85xx
xxNonxmissingxresponsexfrequencyxforxeachxitemxxxxxxxxxxxxx0xxxx1xxxx2xxxx3xmiss
xxactivexx0.50x0.30x0.15x0.05xx0.0xxalertxxx0.35x0.50x0.15x0.00xx0.0xxarousedx0.65x0.30x0.05x0.00xx0.0
xxsleepyxx0.10x0.45x0.25x0.20xx0.0xxtiredxxx0.10x0.35x0.35x0.20xx0.0xxdrowsyxx0.10x0.45x0.30x0.15xx0.0
xxanxiousx0.50x0.50x0.00x0.00xx0.8xxjitteryx0.60x0.35x0.00x0.05xx0.0xxnervousx0.90x0.05x0.05x0.00xx0.0
xxcalmxxxx0.10x0.40x0.30x0.20xx0.0xxrelaxedx0.00x0.30x0.55x0.15xx0.0xxat.easex0.20x0.45x0.20x0.15xx0.0

3.2 Get the actual scores for analysis.

Although we would probably not look at the raw scores, we can if we want by asking for the scores object which is part of the my.scales output. For printing purposes, we round them to two decimal places for compactness.

    xxmy.scoresx<-xmy.scales$scoresxxround(my.scores,2)
    xxxx>xround(my.scores,2)xxxxxxxEAxxxTAxxEApxxEAnxxTApxxTAnxx1xx1.50x1.50x1.00x1.00x1.00x1.00
xx2xx1.33x1.00x0.67x1.00x0.00x1.00xx3xx1.50x0.67x0.33x0.33x0.00x1.67xx4xx1.50x1.83x1.00x1.00x2.00x1.33
xx5xx1.83x0.25x1.67x1.00x0.50x3.00xx6xx1.17x0.75x1.33x2.00x0.17x1.67xx7xx0.33x0.75x0.33x2.67x0.17x1.67
xx8xx0.83x1.08x0.00x1.33x0.17x1.00xx9xx1.17x1.08x0.67x1.33x0.50x1.33xx10x0.83x0.92x0.67x2.00x0.50x1.67
xx11x0.33x0.58x0.00x2.33x0.17x2.00xx12x1.33x1.42x0.67x1.00x0.50x0.67xx13x0.17x1.42x0.00x2.67x0.50x0.67
xx14x2.00x0.75x1.33x0.33x0.17x1.67xx15x1.33x0.08x0.67x1.00x0.17x3.00xx16x0.00x1.25x0.00x3.00x0.50x1.00
xx17x1.33x1.08x0.67x1.00x0.17x1.00xx18x1.17x0.08x1.67x2.33x0.17x3.00xx19x0.17x1.08x0.00x2.67x0.17x1.00
xx20x1.00x0.42x0.33x1.33x0.17x2.33xx

4 The example, continued Once you have the results, you should probably want to describe them and also show a graphic of the scatterplot using the pairs.panels function (Figurex1).

   xxdescribe(my.scores)xxpairs.panels(my.scores}
   xxP>xdescribe(my.scores)xxxxxxvarxxnxmeanxxxsdxmedianxtrimmedxxmadxxminxxmaxxrangexxskewxkurtosisxxxse

xxEAxxxx1x20x1.04x0.57xxx1.17xxxx1.05x0.49x0.00x2.00xx2.00x-0.38xxxx-1.05x0.13
xxTAxxxx2x20x0.90x0.47xxx0.96xxxx0.91x0.43x0.08x1.83xx1.75x-0.07xxxx-0.81x0.11
xxEApxxx3x20x0.65x0.55xxx0.67xxxx0.60x0.49x0.00x1.67xx1.67xx0.42xxxx-1.01x0.12
xxEAnxxx4x20x1.57x0.82xxx1.33xxxx1.56x0.74x0.33x3.00xx2.67xx0.27xxxx-1.35x0.18
xxTApxxx5x20x0.38x0.45xxx0.17xxxx0.29x0.12x0.00x2.00xx2.00xx2.34xxxxx5.62x0.10

PIC Figurex1: A simple scatter plot matrix shows the histograms for the variables on the diagonal, the correlations above the diagonal, and the scatter plots below the diagonal. The best fitting loess regression is shown as the red line.

5 Even more analysis Far more analyses could be done with these data, but the basic scale scoring techniques is a start. Download the vignette for using psych for even more guidance.

http://cran.r-project.org/web/packages/psych/vignettes/overview.pdf. On a Mac,

this is also available in the vignettes list in the help menu.

In addition, look at the examples in the help for score.items.

5.1 Exploring a real data set .

The 12 mood items for 20 subjects were taken from the much larger data set, msq in the psych package. That data set has 92 variables for 3896 subjects. We can repeat our analysis of EA and TA on that data set.

First we get the data for the items that match our small example. Then we describe the data, and finally, find the 6 scales as we did before.

    xxselectx<-xcolnames(my.data)xxselect[12]x<-x'at-ease'xxsmall.msqx<-xmsq[select]
xxdescribe(small.msq)xxmsq.scalesx<-xscore.items(my.keys,small.msq)xxmsq.scalesxxx#showxthexoutputxx

    xxxxxxxxxvarxxxxnxmeanxxxsdxmedianxtrimmedxxmadxminxmaxxrangexxskewxkurtosisxxxse
xxactivexxxx1x3890x1.03x0.93xxxxxx1xxxx0.95x1.48xxx0xxx3xxxxx3xx0.47xxxx-0.76x0.01
xxalertxxxxx2x3885x1.15x0.91xxxxxx1xxxx1.09x1.48xxx0xxx3xxxxx3xx0.33xxxx-0.76x0.01
xxarousedxxx3x3890x0.71x0.85xxxxxx0xxxx0.59x0.00xxx0xxx3xxxxx3xx0.95xxxx-0.04x0.01
xxsleepyxxxx4x3880x1.25x1.05xxxxxx1xxxx1.18x1.48xxx0xxx3xxxxx3xx0.40xxxx-1.04x0.02
xxtiredxxxxx5x3886x1.39x1.04xxxxxx1xxxx1.36x1.48xxx0xxx3xxxxx3xx0.22xxxx-1.10x0.02
xxdrowsyxxxx6x3884x1.16x1.03xxxxxx1xxxx1.08x1.48xxx0xxx3xxxxx3xx0.46xxxx-0.93x0.02
xxanxiousxxx7x2047x0.67x0.86xxxxxx0xxxx0.54x0.00xxx0xxx3xxxxx3xx1.09xxxxx0.26x0.02
xxjitteryxxx8x3890x0.59x0.80xxxxxx0xxxx0.45x0.00xxx0xxx3xxxxx3xx1.24xxxxx0.81x0.01
xxnervousxxx9x3879x0.35x0.65xxxxxx0xxxx0.22x0.00xxx0xxx3xxxxx3xx1.93xxxxx3.47x0.01
xxcalmxxxxx10x3814x1.55x0.92xxxxxx2xxxx1.56x1.48xxx0xxx3xxxxx3x-0.01xxxx-0.83x0.01
xxrelaxedxx11x3889x1.68x0.88xxxxxx2xxxx1.72x1.48xxx0xxx3xxxxx3x-0.17xxxx-0.68x0.01

xxat-easexx12x3879x1.59x0.92xxxxxx2xxxx1.61x1.48xxx0xxx3xxxxx3x-0.09xxxx-0.83x0.01xx
xxCall:xscore.items(keysx=xmy.keys,xitemsx=xsmall.msq)xxxx(Unstandardized)xAlpha:
xxxxxxxxxxEAxxxTAxxEApxxEAnxxTApxTAnxxalphax0.87x0.75x0.81x0.93x0.64x0.8xxxxAveragexitemxcorrelation:
xxxxxxxxxxxxxxEAxxxTAxxEApxxEAnxxTApxxTAnxxaverage.rx0.54x0.34x0.58x0.81x0.37x0.57xx
xxxGuttmanx6*xreliability:xxxxxxxxxxxxEAxxxTAxxEApxEAnxxTApxxTAnxxLambda.6x0.9x0.77x0.76x0.9x0.59x0.74xx
xxScalexintercorrelationsxcorrectedxforxattenuationxxxrawxcorrelationsxbelowxthexdiagonal,xalphaxonxthexdiagonal
xxxcorrectedxcorrelationsxabovexthexdiagonal:xxxxxxxxxxEAxxxxxxTAxxxxEApxxxxEAnxxxxTApxxxxTAn
xxEAxxx0.874x-0.0207xx1.004x-1.006xx0.218xx0.168xxTAxx-0.017xx0.7515x-0.011xx0.024xx1.096x-1.140
xxEApxx0.842x-0.0084xx0.806x-0.618xx0.360xx0.246xxEAnx-0.906xx0.0197x-0.534xx0.927x-0.067x-0.076
xxTApxx0.163xx0.7590xx0.258x-0.052xx0.638x-0.512xxTAnxx0.141x-0.8837xx0.198x-0.065x-0.366xx0.800xxxx

References

xxxR Development Core Team (2012). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.

xxxRevelle, W. (2013). psych: Procedures for Personality and Psychological Research. Northwestern University, Evanston, http://cran.r-project.org/web/packages/psych/. R package version 1.3.1.

Index

describe, 7

file.choose, 2

pairs.panels, 7

psych, 1, 2, 7

R function

xxxxdescribe, 7 xxxxfile.choose, 2 xxxxpairs.panels, 7 xxxxpsych package xxxxxxxxdescribe, 7 xxxxxxxxpairs.panels, 7 xxxxxxxxread.clipboard, 2 xxxxxxxxread.clipboard.csv, 3 xxxxxxxxread.clipboard.fwf, 3 xxxxxxxxread.clipboard.lower, 3 xxxxxxxxread.clipboard.tab, 3 xxxxxxxxread.clipboard.upper, 3 xxxxxxxxscore.items, 1, 2, 7 xxxxread.clipboard, 2 xxxxread.clipboard.csv, 3 xxxxread.clipboard.fwf, 3 xxxxread.clipboard.lower, 3 xxxxread.clipboard.tab, 3 xxxxread.clipboard.upper, 3 xxxxread.table, 2 xxxxscore.items, 1, 2, 7 R package xxxxpsych, 1, 2, 7 read.clipboard, 2 read.clipboard.csv, 3 read.clipboard.fwf, 3 read.clipboard.lower, 3 read.clipboard.tab, 3 read.clipboard.upper, 3 read.table, 2

score.items, 1, 2, 7