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Summary Psychometrics SPSS and R cheat sheet - All procedures step by step

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Vak
Psychometrie (6462PS004Y)

Instelling
Universiteit Leiden (UL)

This cheat sheet/summary contains all the steps you need to perform in SPSS and R for the Psychometrics course (year 2, block 1). It is the only necessary document you need to take with you in the practical exam, and it contains both all procedures (written as: ANALYZE > DESCRIPTIVE STATISTICS &...

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Geupload op 4 november 2023
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Type Samenvatting

SPSS & R Cheat Sheet
Psychometrics - Year 2, Block 1

Scaling & Norming Test Scores
Computing a mean/sum variable (for all items on a test): TRANSFORM > COMPUTE VARIABLE > type in the formula for
mean/sum: MEAN.n (item 1 TO item 10) or SUM.n (item 1 TO item 10)
.n = minimum number of items people must answer to be included
Request mean, standard deviation etc.: ANALYZE > DESCRIPTIVE STATISTICS > DESCRIPTIVES > request mean/standard
deviation/etc.

Compute Z-scores: ANALYZE > DESCRIPTIVE STATISTICS > DESCRIPTIVES > add all variables > tick “Save standardized
values as variables”
Compute T-score: TRANSFORM > COMPUTE VARIABLE > type formula for T-score: (Z-score*10)+50
Compute Percentile Rank: TRANSFORM > RANK CASES > add variables
 RANK TYPES… > untick “Rank” > tick “Fractional rank as %”
 TIES… > select “High”

Create norm table: ANALYZE > REPORTS > CASE SUMMARIES
 Add scale (raw) score in “Grouping Variables”
 Add norm scores (Z, T, %) in “Variables”
 Uncheck “Display cases”
 Statistics… > uncheck “Number of cases” > add “Mean” to “Cell statistics”

Reliability
Split-half method
1. Order items based on mean (low-> high): ANALYZE > DESCRIPTIVE STATISTICS > DESCRIPTIVES >
drag items > OPTIONS… > click “Ascending means”
2. Decide which items go in which split
3. Compute Split-Half Reliability: ANALYZE > SCALE > RELIABILITY > add items in “ITEMS” box (in the
previously chosen order)
 MODEL > select “Split-Half” (SPSS will split it itself at the middle)
 STATISTICS > “DESCRIPTIVES FOR…” > tick “Scale”
- for the value of Split-Half Reliability in output: we look at “Spearman-Brown coefficient” (equal: if we have 2 equal
halves; unequal: if we have unequal halves)

Cronbach’s Alpha: ANALYZE > SCALE > RELIABILITY > Add items in “ITEMS” box (no order needed)
 MODEL > select “Alpha”
 STATISTICS… > “DESCRIPTIVES FOR…” > tick only “Scale if item deleted”
- items that contribute to reliability: when deleted, Alpha becomes LOWER than original
- items that impair reliability: when deleted, Alpha becomes HIGHER than original
Check if item really impairs reliability: ANALYZE > SCALE > RELIABILITY > remove item that impairs > re-establish
reliability (check if new Alpha matches previous “Alpha if item deleted”)

, Validity
MTMM Matrix
1. Calculate Total Scores: TRANSFORM > COMPUTE VARIABLE > use function SUM (item1 TO item2)
(e.g. if you have 3 traits to measure – learning potential, IQ, personality – and 2 instruments to measure each – multiple choice &
observations => you have 3 x 2 = 6 total scores to compute)
MC OBS
LP IQ PS LP IQ PS

2. Determine the correlations between each of the measures above: ANALYZE > CORRELATE > BIVARIATE > add
total scores in order (e.g. LPMC, IQMC, PSMC, LPOBS, IQOBS, PSOBS - as seen above)
 To make only correlations appear in the table: (in Output) DOUBLE CLICK > PIVOT > PIVOTING TRAYS >
drag “Statistics” into “Layer” table

! The resulting table only shows monotrait-heteromethod, heterotrait-monomethod and heterotrait-
heteromethod correlations!

3. Find out the reliability of each measure => monomethod-monotrait correlations (the diagonal of MTMM):
ANALYZE > SCALE > RELIABILITY > drag into “ITEMS” all single items for first trait/instrument measure (not the
previously calculated Total Score, but all single sub-items!) -> repeat for all measures
- resulting Cronbach’s Alpha is = Rxx / monotrait-monomethod correlation

PCA
Preliminary check for PCA (quickly creating Histograms): ANALYZE > DESCRIPTIVES > FREQUENCIES > add all variables
 untick “Display frequency tables”
 CHARTS… > tick “Histograms” & “Show normal curve”
- inspection: for PCA we need n≥ 300

PCA: ANALYZE > DIMENSION REDUCTION > FACTOR > add all variables
 DESCRIPTIVES… > tick “KMO & Bartlett’s test of sphericity” (check KMO >.70, Bartlett - sig.)
 EXTRACTION…
 (every time) tick “Scree plot”
 (if you know how many components you want) tick “Fixed number of factors”
 ROTATION…
 (every time) tick “Loading Plot”
 (when you want a rotation) tick “Varimax rotation”

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