Dr. Richard E. Cleveland, PhD

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Construct Measurement Using Factor Analysis: Creating & Validating Survey Protocols Dr. Richard E. Cleveland, PhD College of Education Counselor Education Program Assistant Professor, Leadership, Technology & Human Development Food World Member, 2014-present February 2015 GSU Research Committee Brown Bag Presentation Title: “Construct Measurement Using Factor Analysis: Creating & Validating Survey Protocols”

Keep it Brief Richard… Research Interests [NOTE: not an exhaustive listing] Validating Protocols for “New” Populations Considering Assumptions

Helping Students Flourish Resiliency Positive Psychology (Lopez et al., 2009) Spirituality Professional Recognition (ACA, ASCA, CACREP) Conceptualizing Spirituality Internal & Possibly Secular (Noddings, 2006) Religious (Fowler, 1981) constructivist (Phillips, 1995) Spirituality & General Well-Being Developmental/Psychological (Kim & Esquivel, 2011)

Holder, Coleman & Wallace (2010) Correlations between Spirituality, Religiosity & Happiness in Children 3 Happiness (FACES, OHQ-SF, SHS) 1 Spirituality (SWBQ) 1 Religiosity (PBS) 1 Temperament (EAS)

So What Did Richard Do? “New” Populations “New” Latent Variables Instruments Created/Administered with Adult Samples (spirituality, subjective well-being, temperament, etc.) Instruments Created/Administered with English Samples (mindfulness, e.g., CAMM-K) “New” Latent Variables School Counselor PK-12 CGCP Implementation

Assumptions Richard Made EFA versus CFA PAF versus PCA Skewness & Kurtosis Parameters Parallel Analysis

EFA versus CFA Confirmatory Factor Analysis (CFA) investigates hypotheses about identified factors and their relationships with each other. Exploring a construct (e.g., latent variable) and the contributing factor(s) within requires EFA. (Field, 2009; Nunnally & Bernstein, 1994; Pedhazur & Schmelkin, 1991; Pett et al., 2003)

PAF vs. PCA Underlying Mathematics (Fabrigar & Wegener, 2012; Field, 2009; Tabachnick & Fidell, 2007) PAF correlations among variables PCA reducing variables to a smaller set Variance (Fabrigar & Wegener, 2012; Tabachnick & Fidell, 2007) PAF analyzing shared variance only PCA no distinction between common/unique variance Theory (Fabrigar & Wegener, 2012; Gall, Gall & Borg, 2007) PAF parameter estimates generalized beyond sample PCA parameters fit to sampling at core level

Skewness & Kurtosis EFA Parameters Skewness <│2│ Kurtosis <│7│ (Fabrigar & Wegener, 2012)

Parallel Analysis Determining the appropriate number of factors Random data generated in a parallel (similar) model Non trivial components in the model influence both raw & random data Eigenvalues: Raw > Random SPSS Syntax O’Connor (2000) (Fabrigar & Wegener, 2012; Fabrigar, Wegener, MacCallum, & Strahan, 1999; Hayton, Allen, & Scarpello, 2004; O’Connor, 2000)

Thank You rcleveland@georgiasouthern.edu @RichieKinz http://richardcleveland.me