1

UNIT 13:DATA ANALYSIS

2

A. Editing, Coding and Computer Entry

• Editing in field i.e after completion of each interview/questionnaire.

• Editing again for any errors not detected in the field.

3

• Coding of questionnaires i.e. converting data to numerical codes. This will allow for statistical analysis.

• Entering data into the computer. Research variables and their numeric values are entered into the computer.

4

B. Research Variables• To carry out statistical analysis, concepts

have to be operationalized. These concepts have to be defined as variables.

• It is important to explicitly set out the variables of your study and how they will be measured. In any one study, some variables will be nominal, ordinal, interval and ratio.

5

• The level at which variables are measured is a very important determinant of how the research data will be analyzed i.e. statistical analysis techniques.

• It is useful to identify research variables at the proposal preparation stage

6

C. Descriptive Statistics• Summarizing data using descriptive

statistics. This enables a researcher to describe the distribution of the research variables using a few indices or statistics.

• Descriptive statistics give a good quick picture of how the variables behaved i.e. their distribution.

7

• In starting your analysis, compute descriptive statistics for all your variables. This will serve as yet another quality check on your data.

• Descriptive statistics are of various types

- Measures of central tendencyMode, median, mean.

8

- Measures of variability: This is the

dispersion of measures around the central

score e.g. the mean. Typical measures of

variability are the range and standard

deviation.

9

- Frequency distribution: This gives a picture of the shape of a variable i.e. how it is distributed.

* Frequency tables

* Histograms

* Frequency polygrams

* Bar charts

10

- Percentages: Proportions of sub-groups

to the total group. They range from 0% to

100%. Percentages are important where

there is need to compare groups that differ

in size.

11

D. Relationships• There are statistics that measure the

relationships between two or more variables.

• Correlation co-efficientCo-movement of variables

12

• Chi-square statisticsdependence or independence of variables.

• Regression co-efficientDetermining an equation that explains how variables are related.

13

E. Inferential Statistics• The ultimate purpose of research is to be

able to generalize the results from samples to populations.

• Hypothesis testing techniques are used to generalize from the sample to the population. Generalizing from a sample to a population is statistical inference.

14

• Commonly used statistical procedures in

statistical inference include correlation,

regression (simple and multiple),

chi-square test and analysis of variance

(for experimental data).