1. Applied Quantitative Analysis and Practices LECTURE#17 By Dr. Osman Sadiq Paracha

2. Previous Lecture Summary Data Transformation issues Reliability Cronbach’s alpha

3. Reliability The ability of the measure to produce the same results under the same conditions. Test-Retest Reliability The ability of a measure to produce consistent results when the same entities are tested at two different points in time.

4. Cronbach’s alpha assessing scale reliability

5. Cronbach’s alpha  Cronbach's alpha is an index of reliability associated with the variation accounted for by the true score of the "underlying construct."  Allows a researcher to measure the internal consistency of scale items, based on the average inter-item correlation  Indicates the extent to which the items in your questionnaire are related to each other  Indicates whether a scale is unidimensional or multidimensional

6. Interpreting scale reliability  The higher the score, the more reliable the generated scale is  A score of.70 or greater is generally considered to be acceptable.90 or > = high reliability.80-.89 = good reliability.70-79 = acceptable reliability.65-.69 = marginal reliability  lower thresholds are sometimes used in the literature.

7. Validity Whether an instrument measures what it set out to measure. Content validity Evidence that the content of a test corresponds to the content of the construct it was designed to cover Construct validity Construct validity involves adoption of complex statistical methods to validate the constructs making it preferable over other types of validity.

8. Exploratory Factor Analysis

9. Exploratory factor analysis... is an interdependence technique whose primary purpose is to define the underlying structure among the variables in the analysis. Exploratory Factor Analysis Defined

11. Exploratory Factor Analysis... Examines the interrelationships among a large number of variables and then attempts to explain them in terms of their common underlying dimensions. Examines the interrelationships among a large number of variables and then attempts to explain them in terms of their common underlying dimensions. These common underlying dimensions are referred to as factors. These common underlying dimensions are referred to as factors. A summarization and data reduction technique that does not have independent and dependent variables, but is an interdependence technique in which all variables are considered simultaneously. A summarization and data reduction technique that does not have independent and dependent variables, but is an interdependence technique in which all variables are considered simultaneously. What is Exploratory Factor Analysis?

13. Correlation Matrix for Store Image Elements

14. Correlation Matrix of Variables After Grouping Using Factor Analysis Shaded areas represent variables likely to be grouped together by factor analysis.

15. Application of Factor Analysis to a Fast-Food Restaurant Service Quality Food Quality FactorsVariables Waiting Time Cleanliness Friendly Employees Taste Temperature Freshness

16. Factor Analysis Decision Process Stage 1: Objectives of Factor Analysis Stage 2: Designing a Factor Analysis Stage 3: Assumptions in Factor Analysis Stage 4: Deriving Factors and Assessing Overall Fit Stage 5: Interpreting the Factors Stage 6: Validation of Factor Analysis Stage 7: Additional uses of Factor Analysis Results

17. Stage 1: Objectives of Factor Analysis 1.Is the objective exploratory or confirmatory? 2.Specify the unit of analysis. 3.Data summarization and/or reduction? 4.Using factor analysis with other techniques.

18. Factor Analysis Outcomes 1.Data summarization = derives underlying dimensions that, when interpreted and understood, describe the data in a much smaller number of concepts than the original individual variables. 2.Data reduction = extends the process of data summarization by deriving an empirical value (factor score or summated scale) for each dimension (factor) and then substituting this value for the original values.

19. Lecture Summary Factor Analysis Why Factor analysis? Types of Factor analysis Stages of Factor analysis Stage 1: Objectives