Lunchtime Seminar. Risper Awuor, Ph.D. Department of Graduate Educational and Leadership. January 30, 2013

Transcription

1 Lunchtime Seminar Risper Awuor, Ph.D. Department of Graduate Educational and Leadership January 30, 2013

2 Scales of Measurement Nominal Ordinal Interval Ratio

3 Scales of Measurement Nominal names assigned as labels Ordinal numbers assigned represent rank order

4 Scales of Measurement (cont d) Interval numbers assigned represent rank order as well as equivalent intervals. The central tendency of a variable measured at the interval level can be represented by its mode, its median, or its arithmetic mean; the mean gives the most information.

5 Scales of Measurement (cont d) Ratio - numbers assigned to objects have all the features of interval measurement Numbers have meaningful ratios between arbitrary pairs of numbers All measurements are relative to absolute zero. Zero is not arbitrary

6 Types of Data Analysis Procedures Descriptive Correlation Canonical Correlation Regression Logistic Regression

7 Types of Data Analysis Procedures T-tests ANOVA Discriminant Analysis Structural Equation Modeling (SEM) Hierarchical Linear Models (HLM)

8 Regression Regression requires that the dependent variable be interval or ratio and the independent variables be dichotomous. GPA" is an interval level variable, which satisfies the level of measurement requirements for regression analysis as a dependent variable. "Sex" [Gender] is a dichotomous or dummy-coded nominal variable which may be included in multiple regression analysis. Family type may be dummy coded

9 Logistic Regression Applied when the dependent variable is dichotomous. In health research Sick/Well Could be applied in education if the research is interested in Pass/Fail; Graduated/Did not graduate Marketing research Will buy/will not buy

10 T-tests Applied when comparing two means Include: o One sample o Dependent Sample o Independent Sample

11 Analysis of Variance One Way ANOVA Two Way ANOVA Multivariate ANOVA Post-hoc Procedures

12 ANOVA One group on one variable with three levels or more Three groups on one variable

13 Two Way ANOVA Two Groups or two treatments Several correlated dependent variables The dependent variables MUST be sharing a common conceptual meaning o o E.g. Self Concept(physical, emotional, social, academic) Teacher Effectiveness (Speaker credibility, reading, comprehension)

14 Another Example Comparing two methods of teachings Take for example second grade reading. Differential effect of the method is better understood if reading achievement is split into: Syllabication Blending Sound discrimination Vocabulary Comprehension Reading rate

15 Example Two methods of teaching grade six mathematics Split them into levels, e.g. o Computation o Concepts o application

16 Discriminant Analysis Used to: o Describe major differences in MANOVA o Classify subjects into groups on the basis of measurements PASW only allows for descriptive discriminant analysis SAS DISCRIM is used for classification It is a mathematical maximization procedure

17 Example Rhode Scholars classified according to parents level of education Dependent variables were Vocational Personality Variables e.g.: Realistic Conventional Enterprising Sociability, etc. How do the groups of scholars based on education of parents differ with respect to personality variables?

18 SEM More complex Analysis procedure Employs LISREL or SAS Technique combines elements of multiple regression, factor analysis and simultaneous equation modeling. Sometimes causal modeling all competing models are run against each other simultaneously.

19 HLM Hierarchical data Longitudinal studies Nested data o Individual and community o Students and School o Students and Parents o Students and classroom

20 Why HLM Allows for dealing with how policies affect student outcomes, for example.

21 Assignment Majorie is a teacher at Luke High School where there are three streams in each form. The three form two streams are taught Science by three different teachers. Majorie decided to conduct a study to determine if the form two students performance in science varies by teacher. 1. What type of data does Majorie need for the study? 2. What procedure should Majorie employ in the data analysis? 3. After the study, Majorie wonders whether only the Teacher would be responsible for observed variability, if any. What should she do to determine how much variability in the student performance in science is explained by Teacher? 4. Majorie continues to wonder about the relationship between performance in Science and other variables that have been included in the study. Do these variables have direct or indirect effect on performance? What should she do to answer these questions?