1. 1

2.  DATA ANALYSIS  PROCESSING DATA  Editing Data  Process for coding 2

3.  DATA ANALYSIS  PROCESSING DATA  Editing Data  Process for coding 3

4.  Ways to use/organize/manipulate data in order to reach research conclusions. 4

5. 1. EDITING DATA 2. CODING DATA 3. DEVELOPING A FRAME OF ANALYSIS 4. ANALYSING DATA 5

6.  Data Cleaning  Checking the completed instruments; to identify and minimize  errors  incompleteness  inconsistencies  misclassification  etc. (illegible writing) 6

7. 2 Considerations for Coding:  Measurement of a variable (scale?, structure – open/closed ended?).  Communication of findings about a variable (measurement scale?, type of statisitical procedures?) (e.g., Ratio scale – mean, mode, median) 7

8.  For analysis using computer, data must be coded in numerical values.  The coding of raw data involves 4 steps:  Developing a code book (master-code book)  Pre-testing the book  Coding the data; and  Verifying the coded data. 8

9.  Develop from beginning of research and evolve continuously to end.  Frame of analysis:  Identify variable to analyse  Determine method to analyse  Determine cross-tabulations needed  Determine which variable to combine for constructing major concepts or develop indices  Identify which variable for which statistical procedures 9

10. 10

11. 1. UNIVARIATE ANALYSIS 2. BIVARIATE ANALYSIS 3. MULTIVARIATE ANALYSIS 11

12.  Is the examination of the distribution of cases on only one variable at a time. Distributions Central tendency Dispersion  Can be generated thro’ Descriptive statistics in the SPSS.  Purpose of univariate analysis is purely descriptive. 12

13.  The full original data usually difficult to interpret.  Data reduction is the process of summarizing the original data to make them more manageable; while maintaning the original data as much as possible. 13

14.  Attribute of each each case under study in terms of the variable in question.  Reporting marginals  E.g., how many respondents, what % of them fall under a certain variable.  500 of 1000 FEM students have CGPA = 3.5 & above.  50% of 1000 FEM students. 14

15.  Shows the number of cases having each of the attributes of a given variable. 15

16.  Reporting summary  In term of averages  Mode (most frequent attribute)  Mean (arithmetic mean)  Median (middle attribute) 16

17. MeasureLevel of Measurement Examples ModeNominalEye color, party affiliation MedianOrdinalRank in class, birth order MeanInterval & ratioSpeed of response, age in years 17

18.  Spread of raw data/info of a variable.  Detailed information of distribution of a variable. Range (simplest measure) Percentile Standard deviation (more sophisticated) 18

19.  Range: distance separating the highest from the lowest value. (e.g., the respondents mean age is 22.75 with a range from 20 to 26). 19

20.  A number or score indicating rank by telling what percentage of those being measured fell below that particular score.  e.g., scored 75 th percentile, means 75% of the other people scored below your score and 25% scored at or above your score. 20

21.  Is a measure of the average amount the scores in a distribution deviate from average (mean) of the distribution.  Observation near mean, small SD. Observation far from mean, large SD. 21

22.  Focuses on the relationships/association between two variables.  Among the many measures of bivariate association are eta, gamma, lambda, Pearson’s r, Kendall’s tau, and Spearman’s rho. 22

23.  Is a method of analyzing the simultaneous relationships among several variables and may be used to understand the relationship between two variables more fully.  e.g., multiple regression, factor analysis, path analysis, discriminant analysis. 23