1. Advanced Data Preparation
Prepared by: Prof. Dr Bahaman Abu Samah Department of Professional Development and Continuing Education Faculty of Educational Studies Universiti Putra Malaysia Serdang

2. Data Preparation 1 2 3 4 Factor Analysis Reliability
Prepare your data before embarking on the actual statistical analyses
Factor Analysis
Reliability
Data Transformation
Normality Test 1 2 3 4
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3. Factor Analysis
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4. Factor Analysis
Factor analysis can be used to reduce a large number of variables into a smaller and more manageable number of factors.
In factor analysis, multiple observed variables that have similar patterns of responses are grouped into factors because they are all associated with a latent (i.e. not directly measured) variable.
Identify a structure (or factors or dimensions) that underlies the relationship among a set of observed variables/items/ indicators
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5. 1 2 3 Steps in Factor Analysis Assess Appropriateness of Data
Factor Extraction
Factor Rotation and Interpretation 1 2 3
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6. Assess Appropriateness of FA
Assess Appropriateness of FA
Bartlett’s test of sphericity
Assess the factorability of the correlation matrix
Bartlett’s test must be significant (p < α)
Kaiser-Meyer-Olkin (KMO)
A measure of sampling adequacy
KMO ranges between 0 – 1
KMO > .7
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7. 
Factor Extraction
Determine the smallest number of factors that best represent the inter-relationships among the set of variables/items/indicators
The most commonly used approach is Principal Component Analysis
By default, Kaiser’s criterion (eigenvalue > 1) is used in SPSS to decide on the number of factors extracted
Catell’s scree plot can also be used to decide on the number of factors
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8. 
Factor Rotation
Rotation will produce pattern of the factor loadings that is easier to interpret
Two approaches to rotation: 1. Orthogonal – uncorrelated 2. Oblique – correlated
Tabachnick and Fidell propose the orthogonal approach as results are easier to interpret
Varimax is the most commonly used orthogonal technique in SPSS
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9. SPSS Procedures
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10. SPSS Procedures
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11. SPSS Results
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12. Reliability Test
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13. Reliability Test Reliability relates to the quality of instrument
In its everyday sense, reliability is the "consistency" or "repeatability" of the study instrument
The extent to which a measure or instrument will yield the same score when administered at different times, locations, or populations
The most commonly use measure of reliability is Cronbach Alpha; a measure of internal consistency of a study instrument
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14. Decision Criteria - Reliability
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15. SPSS Procedures
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17. SPSS Results
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18. Data Transformation
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19. Data Transformation
Data transformation is the process of converting data or information from one format to another
In SPSS, the frequently used data transformations:
Compute
Recode IF
Count 1 2 3 4
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20. Compute
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21. Compute  Create a new variable based on existing variable/s
Compute mean summated scores for:
Variable Formula (Numeric expression)
mHM = Mean (F13, F18, F19, F111, F112, F117)
mKH = Mean (F214, F221 to F224)
mSK = Mean (F32, F34, F35, F37, F310)
mSD = Mean (F311 to F313)
mTJ = Mean (F43, F45, F48 to F410)
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22. Compute indices for dimensions and overall (IHI):
Variable Formula (Numeric expression)
indexHM = (mHM-1)/5*100
indexKH = (mKH-1)/5*100
indexSK = (mSK-1)/5*100
indexSD = (mSD-1)/5*100
indexTJ = (mTJ-1)/5*100 IHI = (indexHM + indexKH + indexSK indexSD + indexTJ)/5
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23. SPSS Procedure
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24. SPSS Results
Displayed in SPSS Data Editor
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25. Recode
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26. Recode  Recode into same variable Recode into different variable
Two major functions of RECODE: 1. Transform data into categories 2. Change value
Two types of RECODE:
Recode into same variable
Override the existing values
Recode into different variable
Retain the existing variable and values; create a new variable
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27. Transform Data into Categories
Categorize the earlier computed indices into LEVELS indexHM  indexHM_CAT indexKH  indexKH_CAT indexSK  indexSK_CAT indexSD  indexSD_CAT indexTJ  indexTJ_CAT IHI  IHI_CAT
Four levels: 1. Cemerlang ≥ Baik 70 – Sederhana 50 – Lemah < 50
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28. Assign Value labels
VALUE LABELS indexHM_CAT indexKH_CAT indexSK_CAT indexSD_CAT indexTJ_CAT IHI_CAT 1 "Cemerlang" 2 "Baik"
3 "Memuaskan", 4 "Lemah".
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29. SPSS Procedures
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31. SPSS Results
Displayed in SPSS Data Editor
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32. Test of Normality
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33. Test of Normality
One of the major assumption for parametric statistics is data in the population must be normally distributed
Use Exploratory Data Analysis (EDA) in SPSS
Three most frequently used statistics to test for the assumption of normality: 1. Kolmogorov-Smirnov 2. Shapiro-Wilk 3. Skewness
In addition, may use 1) Normal Q-Q plot ) Detrended Normal Q-Q plot
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34. Decision Criteria
Kolmogorov-Smirnov & Shapiro-Wilk Normal sig (p) > α (.05) X Normal sig (p) < α (.05)
Skewness Normal -1 to or -2 to +2
George, D and Mallery, P (2005) and Pallant (2001)
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35. SPSS Procedures
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36. SPSS Results
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