The document discusses empirical research methods, detailing data collection techniques like observation and questionnaires, and distinguishes between quantitative and qualitative research. It covers sampling techniques, descriptive and inferential statistics, and tests such as t-tests and chi-square for analyzing data relationships. The content emphasizes the importance of sample size and representativeness in drawing valid conclusions from research.

1. Syed H. Qasim

2. In research, the word empirical refers to collection of
data using evidence that is collected through
observation, interview, questionnaire, checklist,
rating scale or by using some measuring tool and test
them to come up with conclusions.
1. Collection of Data 2. Testing of Data
1. Sampling Descriptive & Inferential
2. Tools Statistics

3. Empirical research can be conducted and analysed using
qualitative or quantitative methods.
Quantitative research: Quantitative research methods are used
to gather information through numerical data.
 Highly structured
 Larger Sample
 survey, questionnaire, scales ,etc
Qualitative research: Qualitative research methods are used to
gather non numerical data. It is used to find meanings, opinions,
or the underlying reasons from its subjects.
 unstructured or semi structured.
 sample size is usually small
 focus groups, observation, interviews, etc.

4. Population – Identifiable and well specified group of
individuals
 Finite
 Infinite

5. Representative of the population
A good sample
Large in size [30+]
Represent all the parts
M1
M0
As M1 approaches M0, estimate is better
Population Parameter
Sample Statistic

6. What is Probability?
Conditions
 Size of population
 Equal chance of being selected
 Sample size must be clearly specified
Simple Random Sampling
Stratified Random Sampling
Cluster or Area Sampling

7. Equal chance of being selected.
 Sampling without replacement
 Sampling with replacement
Types
 Fishbowl method
 Table of Random numbers
 Computer determined randomness

8. Gender
SES
Religion
One strata
Two strata Non overlapping &
Multiple strata Homogeneous
Proportionate stratified sampling
Disproportionate stratified sampling

9. Population Sample (5%)
Ph.D 5
PG 35
Random
UG 60
Total 2000 100
Sampling error is minimized
Time consuming
100
700
1200

10. Population Sample (5%)
Ph.D 25
PG 25
Random
UG 50
Total 2000 100
Common in Behavioural Research
Less Time consuming
100
700
1200

11. Larger geographical area is covered
Flexibility

12. No way of assessing the probability of elements of
population being included in the sample.
Quota Sampling
Purposive Sampling
Accidental Sampling
Systematic Sampling
Snowball Sampling

13. Quota Sampling
Similar to stratified random sampling but final
selection is not random but according to investigator
convenience.
Purposive Sampling
Investigator makes a judgment regarding selection.
Purpose should be solved
Accidental Sampling
Investigator select according to his convenience

14. Systematic Sample
Selecting every nth person from predetermined list
Snowball Sampling
Sociometric technique

15. Man-Made Tool/Test
Standardized Tool/Test
Interview
Observation
Questionnaire
Rating Scale

16. Descriptive statistics give information that
describes the data in some manner. A graphical
representation of data is another method of
descriptive statistics. Descriptive statistics do not,
allow us to reach conclusions regarding any
hypotheses we might have made. They are simply a
way to describe our data.
Example
Measures of Central Tendency – Mean, Median,
Mode
Measures of Variability – Range, Quartile, Standard
Deviation

18. Inferential statistics is to draw conclusions from a
sample and generalize them to the population. The
most common methodologies used are
z-test
t-tests
Analysis of Variance (ANOVA)
Chi-square
Correlation
Factor Analysis

19. A t-test is a type of inferential statistic used to
determine if there is a significant difference between
the means of two groups, which may be related in
certain features.
X1= Mean of first group
X2= Mean of Second group
S1= Standard deviation of one group
S2= Standard deviation of second group
N1=Total number of observations in one group
N2= Total number of observation in second group

20.  simple random sample, that the data is collected
from a representative, randomly selected portion
of the total population.
 the data, when plotted, results in a normal
distribution, bell-shaped distribution curve.
 large sample size is used.
 Homogeneous, or equal, variance exists when the
standard deviations of samples are approximately
equal.

21. Analysis of Variance (ANOVA) is a statistical
technique applied where more than two samples are
meant to be compared. It is also called F-test
One way analysis: When we are comparing more
than three groups based on one factor variable.
Two way analysis: When factor variables are more
than two.

22. Chi-square is a statistical test commonly used to
compare observed data with data we would expect to
obtain according to a specific hypothesis. It is used to
determine whether there is a significant association
between the two variables.
Example: In an election survey, voters might be
classified by gender (male or female) and voting
preference (Democrat, Republican, or Independent).
We could use a chi-square test for independence to
determine whether gender is related to voting
preference.

23. Correlation is the average relationship between two or
more variables. When the change in one variable makes
or causes a change in other variable then there is a
correlation between these two variables.

24. THANK YOU