Analysing & interpreting data

1. Analyzing and interpreting data

2. Things aren’t always what we think!
Six blind men go to observe an elephant. One feels the side and thinks the
elephant is like a wall. One feels the tusk and thinks the elephant is a like a
spear. One touches the squirming trunk and thinks the elephant is like a
snake. One feels the knee and thinks the elephant is like a tree. One
touches the ear, and thinks the elephant is like a fan. One grasps the tail and
thinks it is like a rope. They argue long and loud and though each was partly
in the right, all were in the wrong.
For a detailed version of this fable see:
http://www.wordinfo.info/words/index/info/view_unit/1/?letter=B&spage=3
Blind men and an elephant
- Indian fable

3. Data analysis and interpretation
• Think about analysis EARLY
• Start with a plan
• Code, enter, clean
• Analyze
• Interpret
• Reflect
–What did we learn?
–What conclusions can we draw?
–What are our recommendations?
–What are the limitations of our analysis?

5. Why do we need an analysis
plan?
• To make sure that the questions and data
collection instrument will get the information,
what we want
• Think about “report” when are designing data
collection instruments

6. Preparing Data for Analysis

7. Analysis
Analysis is a process, which enters into
research in one form or another form the
very beginging .
it consist of two large steps :-
gathering data and analysis of these data .

8. Key components of a data analysis
plan
• Purpose of the evaluation
• Questions
• What you hope to learn from the
question
• Analysis technique
• How data will be presented

9. STEPS IN PROCESSING OF
DATA
identifying the data structure
editing the data
classifying the data
 transcription of data
coding
tabulation of data

10. Identifying the data structure
It is a dynamic collection of related
variables and can be convinently
represented and state the preliminary
relation ship between variables /groups
of variables

11. Editing the data
Data is editing in the data instrument . it is a
processes of checking to detect and
correct of errors , means remove of
incompleteness or inconsistency in the
data . check by 3 steps :-
completeness
accuracy
uniformity

12. Classification of data
1. according to attributes :- descriptive ( such as
honesty , literacy ) and numerical ( weight,
height ) . descriptive cannot be measure in
quantitative study .
2. according to class interval :- statistics of
variables , which classify basic of class interval (
income , production , age ).

13. Transcription of data
long worksheets , sorting cards or sorting
strips could be used by the researcher to
manually transcript the responces .

14. Data Coding

15. Data Coding

16. Hand coding
Question 1 : Do you smoke? (circle 1)
YES NO No answer
// ///// /

17. Data entry by computer
• By Computer
– Excel (spreadsheet)
– Microsoft Access (database mngt)
– Quantitative analysis: SPSS (statistical
software)
– Qualitative analysis: Epi info (CDC data
management and analysis program:
www.cdc.gov/epiinfo); In ViVo, etc.

18. Data entry computer screen
Survey ID Q1 Do you
smoke
Q2 Age
001 1 24
002 1 18
003 2 36
004 2 48
005 1 26
Smoking: 1 (YES) 2 (NO)

19. tabulation of data

20. Describing the data
Descriptive statistics are used to
describe the basic features of data and
to provide simple summaries about the
sample and the measures used in the
study .

21. Common descriptive statistics
• Count (frequencies)
• Percentage
• Mean
• Mode
• Median
• Range
• Standard deviation
• Ranking

22. Drawing the inferences of data
It helps in drawing inferences from the
data . eg. finding the diferences ,
relationship, and association between
two more variables .
inferential stistical tests are Z-test , t- test
, ANOVA , chi -squire test etc.

23. Interpretation of the data
It refers to the critical examination of the
analyzed study results to draw
inferences and conclusion . it involves
a search for their meaning in relation to
the research problem, objectives ,
conceptual framework and hypotheses.

24. Strategies for effective interpretations
interpretations must be made in light of
research problem , objectives,conceptual
framework , hypotheses, and
assumptions .
critical examination of the each elements .
careful consideration and recognition of
the limitition .
each part , aspect and segment of the
analysed result must receive close
attentions, so that misrepresentation can
be avoided.

25. Comparison of range ,mean ,median of pretest and
post test knowledge score
n=30
knowledge score Range Mean Median
Pretest 6-20 11.5 11.83
Posttest 17-29 23.3 24.1

28. Hypothesis testing
• State the Null and Alternate hypotheses in –
sentences and
- equation form
• State the significance level
e.g α = 0.05
• State the number of tails( one or two) and the test
statistic ( z,t)
• For two sample indicate independent or paired

29. The Non-Zero Null Hypothesis
1.Usually the hypothesized
difference is zero, That is the
difference between the mean of
the sample and expected value
is zero
µ = M

30. Hypothesis testing
• When do you do paired t test test?
Does the average difference between the
two measures of the sample contradicct
null hypothesis
• When do you independent t test?
Does the difference between average
responses for sample one and sample
two contradict the null hypothesis?

31. Discussing limitations
Written reports:
• Be explicit about your limitations
Oral reports:
• Be prepared to discuss limitations
• Be honest about limitations
• Know the claims you cannot make
– Do not claim causation without a true
experimental design
– Do not generalize to the population without
random sample and quality administration
(e.g., <60% response rate on a survey)

32. Analyzing qualitative data
“Content analysis” steps:
1. Transcribe data (if audio taped)
2. Read transcripts
3. Highlight quotes and note why important
4. Code quotes according to margin notes
5. Sort quotes into coded groups (themes)
6. Interpret patterns in quotes
7. Describe these patterns

33. Hand coding
qualitative data

35. Example data set