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

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 Next ►

Factor Analysis Next ►

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 Next ►

1 2 3 Steps in Factor Analysis Assess Appropriateness of Data Factor Extraction Factor Rotation and Interpretation 1 2 3 Next ►

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 Next ►

 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 Next ►

 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 Next ►

SPSS Procedures Next ►

SPSS Procedures Next ►

SPSS Results Next ►

Reliability Test Next ►

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 Next ►

Decision Criteria - Reliability Next ►

SPSS Procedures Next ►

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SPSS Results Next ►

Data Transformation Next ►

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 Next ►

Compute Next ►

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) Next ►

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 Next ►

SPSS Procedure Next ►

SPSS Results Displayed in SPSS Data Editor Next ►

Recode Next ►

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 Next ►

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 ≥ 90 2. Baik 70 – 89.99 3. Sederhana 50 – 69.99 4. Lemah < 50 Next ►

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". EXECUTE.

SPSS Procedures Next ►

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SPSS Results Displayed in SPSS Data Editor Next ►

Test of Normality Next ►

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 2) Detrended Normal Q-Q plot Next ►

Decision Criteria Kolmogorov-Smirnov & Shapiro-Wilk Normal sig (p) > α (.05) X Normal sig (p) < α (.05) Skewness Normal -1 to +1 or -2 to +2 George, D and Mallery, P (2005) and Pallant (2001) Next ►

SPSS Procedures Next ►

SPSS Results ◄ END ►