This document discusses analyzing research data through descriptive and analytical statistics. Descriptive statistics summarize variables one by one through measures like frequency, percentage, mean, median and standard deviation depending on the variable level. Analytical statistics examine relationships between two or more variables. The document demonstrates analyzing a hypertension study dataset in SPSS, including checking normality distribution through histograms, Shapiro-Wilk test and Q-Q plots to determine appropriate tests. Frequency is used to describe categorical gender variable while numerical age is described through mean, standard deviation and histogram with normal curve fitting.

1. Analyzing Research Data
AMIT SHARMA
ASSOCIATE PROFESSOR
DEPT. OF PHARMACY PRACTICE
I.S.F. COLLEGE OF PHARMACY
GHAL KALAN, FEROZPUR G.T. ROAD
MOGA, 142001, PUNJAB

2. Analyzing Research Data
• Data analysis can be divided into two: Descriptive and analytical.
• In descriptive statistics, the main objective is to summaries the variables
concerned one by one.
• In analytical statistics the aim is to describe the relationship between
two variables or more

3. Descriptive Statistics
• The first thing to do before we analyze the data is to describe the
variables.
• When we are studying hypertension in one population, we should first
describe the population.
• We can describe the distribution of age, gender, race, education and
income level. (Demographic Profile of Population)

4. Descriptive Statistics
• We should include precision of the estimates, such as the standard
deviation, standard error or confidence interval.
• The estimates used to describe a variable depend on the level of its
measurement.
• For categorical measures, frequency or count with percentage we should
use

5. Descriptive Statistics
• E.g. distribution of gender, which is a nominal measurement, we could
describe the number of male and its percentage over total number.
• Usually in a variable with two categories data like this, describing one
category is enough because the other will complement the value.

6. Descriptive Statistics
• For numerical measures, it will depend on the Normality distribution
• If it is a normal distribution, we could use mean and its dispersion
values such as standard deviation.
• When data are not normally distributed, we should not use mean and
its product because mean is affected by extreme values.

7. Descriptive Statistics
• In such situation, median is a better alternative.
• We should not use standard deviation as it is derived from mean.
• We could use minimum-maximum value, range or inter-quartile range.
(IQR)

8. Descriptive Statistics
• To illustrate the points, in this topic we will be using a data
(Data of a study about hypertension among 150 adults.)

9. level of its measurement
How to describe a variable

10. Analytical tools for more effective research.
Data Collected
Data filled in
SPSS
Ready for
analysis ???
Check
Normality
distribution
Test for
Normal
distribution
Data
Normally distributed
Apply
Parametric
test
Data not normally distributed
apply
Non- Parametric
test

11. Describe Numerical Data
• For such study, normally the first thing to do is to describe the
demographic characteristics of the study samples.
• Let us start by analyzing the age distribution.

12. SPSS Analysis: Describe numerical data
1. Click Analyze
2. Click Descriptive Statistics
3. Click Explore
4. Move age to Dependent List box
5. Click Plots
6. Check Histogram
7. Click Continue
8. Click OK

13. Testing for Normality using SPSS-
It will help you to determine whether your data is normal or not.
 Click Analyze > Descriptive Statistics > Explore... on the top

14. Testing for Normality using SPSS-
It will help you to determine whether your data is normal or not.
 Click Analyze > Descriptive Statistics > Explore... on the top

15. Testing for Normality using SPSS
Leave the above options unchanged or Fill
Confidence interval as per your study.

16. Testing for Normality using SPSS
 Click Plot the button. Change the options so that you are presented with the following screen:
Click the Continue button.
Click OK the button.
Output:
SPSS outputs many table and graphs with this procedure.
One of the reasons for this is that the Explore... command is not
used only for the testing of normality but in describing data in
many different ways.

17. SPSS Output
• The results here show that the
mean (SD) is 37.6 (7.6) years
• median (IQR) is 37.5 (10) years
• Ranging from 18 to 56 years
old.
• Skewness and kurtosis are
−0.254 and −0.198,

18. Results of Normality using SPSS
 Shapiro-Wilk Test of Normality
• The above table presents the results from two well-known tests of normality.
• Shapiro-Wilk Test is more appropriate for small sample sizes (more than 50 samples) but can
also handle sample sizes as large as 2000.

19. Results of Normality using SPSS
 If the Sig. value of the Shapiro-Wilk Test is greater the 0.05 then the data is
normal.
 Other method is Normal Q-Q Plot

20. Results of Normality using SPSS
Histogram for Age
we might not be able to appreciate its
distribution. What we can do is to fit a distribution curve on it.
SPSS Analysis: Fitting normal curve
1. At the output window, double click on the graph to open up Chart
Editor
2. Check Show Distribution Curve
3. Close the Properties window (You can skip this step)
4. Close Chart editor

21. Results of Normality using SPSS
• The distribution is a symmetrical bell-
shaped, hence age is normally
distributed.
• Therefore, the age should be
described using mean and standard
deviation.

22. Results of Normality using SPSS
Histogram of Age with Normal curve
Positively
skewed distribution of
Glucose

23. Describe Categorical Data

24. Describe Categorical Data
SPSS Analysis: Frequency
1. Click Analyse
2. Click Descriptive Statistics
3. Move gender to Variable(s)
4. Click OK
Result: For categorical data, we use
Frequency.
Based on Gender table, female 55.3 %
(n = 83) of the study population.

25. Final Results: Table

26. Other tests for analysis
Normality distribution
Normal
Proceed with
analysis
•Correlation
•Regression
•Paired Samples t-test
•Independent Samples t-test
•ANOVA
Normality distribution
Not Normal
Proceed with analysis
(time bound studies)
Chi Square