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

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Lec 9

LECTURE 111Outline DATA ANALYSISPROCESSING DATAEditing Data Process for coding

2Outline DATA ANALYSISPROCESSING DATAEditing Data Process for coding

3DATA ANALYSISWays to use/organize/manipulate data in order to reach research conclusions.

4PROCESSING DATAEDITING DATACODING DATADEVELOPING A FRAME OF ANALYSISANALYSING DATA5Editing Data Data Cleaning

Checking the completed instruments; to identify and minimize

errors incompleteness inconsistencies misclassification etc. (illegible writing)

6Coding Data2 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)

7Process for coding: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 bookCoding the data; and Verifying the coded data.

8Developing A Frame of AnalysisDevelop from beginning of research and evolve continuously to end.

Frame of analysis:Identify variable to analyseDetermine method to analyseDetermine cross-tabulations needed Determine which variable to combine for constructing major concepts or develop indices Identify which variable for which statistical procedures

9Analyzing Data10LEVEL OF ANALYSISUNIVARIATE ANALYSISBIVARIATE ANALYSISMULTIVARIATE ANALYSIS

11UNIVARIATE ANALYSISIs the examination of the distribution of cases on only one variable at a time.DistributionsCentral tendencyDispersionCan be generated thro Descriptive statistics in the SPSS.Purpose of univariate analysis is purely descriptive.

12The 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.13DistributionsAttribute of each each case under study in terms of the variable in question.Reporting marginalsE.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.14Frequency DistributionShows the number of cases having each of the attributes of a given variable.15Central TendencyReporting summaryIn term of averagesMode (most frequent attribute)Mean (arithmetic mean)Median (middle attribute)

16Which measure of Central Tendency to use?MeasureLevel of MeasurementExamplesModeNominalEye color, party affiliationMedianOrdinalRank in class, birth orderMeanInterval & ratioSpeed of response, age in years17DispersionSpread of raw data/info of a variable.Detailed information of distribution of a variable.Range (simplest measure)PercentileStandard deviation (more sophisticated)

18Range: distance separating the highest from the lowest value. (e.g., the respondents mean age is 22.75 with a range from 20 to 26).

19PercentileA number or score indicating rank by telling what percentage of those being measured fell below that particular score.e.g., scored 75th percentile, means 75% of the other people scored below your score and 25% scored at or above your score.20Standard DeviationIs 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.

21BIVARIATE ANALYSISFocuses on the relationships/association between two variables.Among the many measures of bivariate association are eta, gamma, lambda, Pearsons r, Kendalls tau, and Spearmans rho.

22MULTIVARIATE ANALYSISIs 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