data analyis plan

1. 1
DATA PROCESSING &
ANALYSIS PLAN
OLUSINA BAMIWUYE (PH.D)

2. Learning Objectives
 Identify important issues related to quality
control and processing of data.
 Describe how data can be best be
analysed and interpreted based on the
objectives and variables of the study.
 Prepare a plan for the processing and
analysis of data (including data master
sheets and dummy tables) for the research
proposal you are developing.
2

3. 3
NATURE OF STATISTICAL
DATA
 Statistics refers to a body of logic or
techniques for collecting, organizing,
analyzing and interpreting statistical data.
 Statistical data are facts expressed either
in quantitative or qualitative form

4. 4
Types of Data
 Primary and Secondary data
- Primary data: Data originally collected in
the process of any statistical inquiry
- Secondary data: Data collected by other
individual/people/organization
 Demographers prefer primary source to
secondary source.

5. VARIABLES
Quantitative Vs Qualitative
 Any phenomenon with difference in
magnitude
Types of Variables
 Quantitative – numbers, percent, means..
 Qualitative- explore why?, how?
 Quantitative: Weight, Height, BMI, SBP, DSP,
age, age at first marriage, CEB etc.
 Qualitative: Reason for not using contraceptive
method;
5

6. PURPOSE OF DATA
ANALYSIS
 Provide answers to research questions being
studied
- Distributional Characteristics of data
- Variance in the data
- Differences within the data
- Relationships between/among variables

7. Why Analysis Plan? (1)
 Such a plan helps the researcher assure
that at the end of the study:
 all the information (s)he needs has indeed
been collected, and in a standardised way;
 (s)he has not collected unnecessary data
which will never be analysed.
 Provides you with better insight into the
feasibility of the analysis to be performed
as well as the resources that are required.7

8. Why Analysis Plan? (2)
 Provides an important review of the
appropriateness of the data collection
tools for collecting the data you need.
8

9. Guide in Preparing Analysis Plan
 The plan for data processing and analysis
must be made after careful consideration
of the objectives of the study as well as of
the tools developed to meet the objectives.
 The procedures for the analysis of data
collected through qualitative and
quantitative techniques are quite different.
9

10. What should the plan include?
When making a plan for data processing
and analysis the following issues should
be considered:
 Sorting data,
 Performing quality-control checks,
 Data processing, and
 Data analysis.
10

11. Sorting of Data (1)
 An appropriate system for sorting the data
is important for facilitating subsequent
processing and analysis.
 If you have different study populations (for
example village health workers, village
health committees and the general
population), you obviously would number
the questionnaires separately.
11

12. Data Sorting (2)
In a comparative study it is best to sort the
data right after collection into the two or three
groups that you will be comparing during
data analysis. eg:
 Users and Non-Users of Family Planning
 rural and urban samples
 in a case-control study obviously the cases
are to be compared with the controls.
Ensure you number the questionnaires in
each of these categories separately 12

13. Quality Control Checks
For completeness and internal consistency:
 On the Spot Field Editing before processing
 Office Editing
 A decision to exclude data of doubtful quality is
ethically correct and it testifies to the scientific
integrity of the researcher.
 keep track of any questions you had to exclude
because of incompleteness or inconsistency in
the answers, and discuss it in your final report.
13

14. Data Processing
Stages of Data Processing:
 Categorising
 Coding
 Data entry
 Data validation
Decision must be made on each stage
outlined above before analysis of data 14

15. Data Analysis
 Descriptive:describes the problem under study.
 Analytic: Groups are compared to determine
differences, or explore relationships between
variables.
 A descriptive cross-tabulation would, for
example, relate smoking behaviour to sex or
occupational background
 An analytic cross-tabulation serves to
investigate relationship between variables 15

16. Construction of Dummy Tables
 When the plan for data analysis is being
developed the data, of course, is not yet
available. However, in order to visualise how
the data can be organised and summarised it
is useful at this stage to construct DUMMY
TABLES.
 A DUMMY TABLE contains all elements of a
real table, except that the cells are still empty.
16

17. Dummy Frequency Table of Percentage
Distribution of Respondents by Age
Age Frequency Percentage
15-19
20-24
25+
Total 100.0
17

18. Dummy Crosstab: Residence and
Contraceptive use
RESIDENCE Currently
using any
method of
contraceptive
N (%)
Not currently
using any
contraceptive
N (%)
Total
Urban
Semi-Urban
Rural
Total
Chi-square= ****; df= ********; p< ***** 18

19. Hints in Constructing Dummy
Crosstabs
 If a dependent and an independent variable are
cross-tabulated, the headings of the dependent
variable are usually placed horizontally
 All tables should have a clear title and clear
headings for all rows and columns.
 All tables should have a separate row and a
separate column for totals to enable you to
check if your totals are the same for all
variables and to make further analysis easier.
19

20. ANALYSIS PLAN FOR
QUALITATIVE DATA
 a decision on whether all or some parts of the
data should be processed by hand or computer;
 dummy tables for the description of the problem
guided by the objectives of the study;
 a decision on how qualitative data should be
analysed;
 an estimate of the total time needed for analysis
 an estimate of the total cost of the analysis.
20

21. 21
Quantitative Data Analysis
Software
 EPI-INFO
 SPSS
 SAS
 ISSA
 Statistica
 STATA etc

22. Qualitative Software
 Text Base Alpha
 Text Base Beta
 CDC EZ-Text
 Ethnograph
 Nnvivo
22

23. The use of ZY index table
Though a manual analysis of qualitative
data but the method is quite useful. It is a
way of summarizing the result of
qualitative data into tables by using
themes and sub-themes in a study without
attempting to use numbers or percents
(quantification)
23

24. Example of ZY Index Table
Key questions LGA 1
(MALE)
LGA 1
(FEMALE)
LGA 2
(MALE)
LGA 2
(FEMALE)
Major theme 1 ++ + - +
Major theme 2 - ++ + +
Major theme 3 ++ ++ ++ ++
Major theme 4 + + + -
Major theme 5 - ++ ++ +
- opinion not expressed at all
+ opinion expressed by not less than 2 respondents
++ opinion expressed by at least 3 respondents
24

25. Some Quick Guide in Choosing
Appropriate Statistics.
 Possible effect a single non-metric independent
variable (factor) on a metric dependent variable –
One Way Analysis of Variance.
 Simultaneous effects of ‘n’ factors (non-metric) on a
metric dependent variable – “n” way analysis of
variance.
 Possible effects of both metric and non-metric
independent variables on a single metric dependent
variable – analysis of covariance.
 Possible effect of one metric independent variable on
a single metric dependent variable – bivariate
regression analysis
25

26. Some Quick Guide in Choosing
Appropriate Statistics (2)
 Possible effects of two or more metric variables on
metric dependent variable – multiple regression
 Extent of relationship between 2 metric variables –
Pearson Correlation coefficient
 Possible effects of metric and non-metric dummy
variables on dichotomous dependent variable –
binary logistic regression analysis
 Possible effects of metric and non-metric dummy
variables on dependent variable with more than two
categories – multinomial logistic regression
analysis
26

27. Some Quick Guide in Choosing
appropriate Statistics (3)
 Possible association between two categorical
variables – chi square test
 Differences between a metric dependent
variable and a non-metric (2 categories) –
Independent T-test
 Test of differences between two metric
variables in a pre-post test design –paired t-
test.
27

28. Some Quick Guide in Choosing
Appropriate Statistics (4)
 Measuring the relative importance of the
metric independent variables on a
metric dependent variable – Beta
Coefficient
 Measuring the proportion of variations in
a metric dependent variable explained
by metric independent variables –
coefficient of multiple determinations
(R-Square).
28

29. 29
Possible association among three or more
categorical variables – loglinear analysis
Nonparametric alternative to ANOVA(K>2)
– Kruskal Wallis
Nonparametric alternative to independent T-
test – Mann Whitney
Nonparametric alternative to paired T-test -
Wilcoxon Sign-ranked test
Some Quick Guide in Choosing
appropriate Statistics (5)

30. 30
THANK YOU
FOR
LISTENING