2. Question 02: Answer the following short questions. Each question carries equal marks.
i. Evaluate the difference between deductive and inductive research approaches. Give
an example to justify the difference.
Answer:
1Following are the differences between inductive and deductive reasoning. both types of
reasoning are just the opposites.
Deductive reasoning makes logical conclusion from a major premise and a minor premise.
For example, the premise "Every A is B" could be followed by another premise, "This
C is A." Those statements would lead to the conclusion "This C is B.
Deductive reasoning allows them to apply the theories to specific situations.
Inductive reasoning makes broad generalizations from specific observations. Basically, there
is data, then conclusions are drawn from the data.
"In inductive inference, we go from the specific to the general. We make many
observations, discern a pattern, make a generalization, and infer an explanation or a
theory.
Inductive reasoning has its place in the scientific method. Scientists use it to form
hypotheses and theories.
Example for Inductive Reasoning:
"The coin I pulled from the bag is a penny. That coin is a penny. A third coin from
the bag is a penny. Therefore, all the coins in the bag are pennies."
"Harold is a grandfather. Harold is bald. Therefore, all grandfathers are bald." The
conclusion does not follow logically from the statements.
Even if all of the premises are true in a statement, inductive reasoning allows for the
conclusion to be false.
Example for Deductive reasoning:
"All men are mortal. Harold is a man. Therefore, Harold is mortal." For deductive
reasoning to be sound, the hypothesis must be correct. It is assumed that the premises,
"All men are mortal" and "Harold is a man" are true. Therefore, the conclusion is
logical and true.
In deductive reasoning, if something is true of a class of things in general, it is also
true for all members of that class.
3. Where it is required to apply stratified sampling and snowballing sampling technique?
Justify it with a suitable example.
Snowball sampling or chain sampling refers to a non-probability sampling technique in which
existing study subjects select future subjects from among their relationships. Thus the sample
group grows/expands like a rolling snowball. As the sample grows up, enough data is collected
to be useful for research. This sampling technique is often used in hidden populations, like drug
users who are difficult for researchers to access.
This non-probability sampling is different than other non-probability samplings like quota
sampling, Heterogeneity sampling, and many more because it does choose people on the basis of
some fixed criteria with the help of other similar people who fulfills the same criteria. For
instance, In quota sampling, we select people nonrandom according to some fixed quota. and in
Expert sampling, we collect a sample of people with known or demonstrable experience and
expertise in some areas. But in snowball sampling we begin by identifying an individual who
meets the criteria for inclusion in the study then we ask them to recommend others who they
know who also meet the criteria.
Stratified Random Sampling
The steps involved in stratified sampling are more detailed as they ensure the population is
studied in detail. All the different categories included in the population are considered. The aim
is to ensure that none of the categories or subgroups of the population are missed.
The steps of forming stratified random sampling are as follows:
1. Identify all the possible categories in which the complete population can be divided.
2. Form all the appropriate subgroups based on their corresponding categories.
3. Ensure that each subgroup consists of identical subjects. Such a subgroup is considered to
be homogeneous among itself. The subgroups formed under this process are called strata.
The basic quality of strata is that it consists of all the identical subjects present in the
population. Each stratum is different from other strata, but all strata are homogeneous
within themselves.
4. Now, for the final selection of the sample units, all strata are considered. The aim is to
incorporate subjects from different categories. Including all the strata assures that all the
categories are covered in the final sample.
5. From each stratum, a fixed number of subjects is chosen randomly. Thus, the random
selection of subjects takes place within each stratum.
6. The final sample consists of subjects of all the strata. Hence, the final sample is
heterogeneous and it is assured to consist of all the categories that were present in the
original population.
Thus, this method of sampling involves the concept of strata formation and then a random
selection from each stratum.
4. Question 04: Make the possible hypotheses for the following frameworks.
Model 1
Model 2
Model: 1
Solution:
Hypotheses
HA1:
The higher the Need for Achievement, the greater will be the level of motivation.
HA2:
The greater the Protestant work ethic values people hold, the greater will be their level of
motivation to work.
HA3:
The greater the motivation in individuals, the greater their level of job involvement.
Model:2
Hypothesis: 1
The greater the employees' job satisfaction, the greater their organizational commitment or vice
versa.
Rewards & Recognition Employee Engagement
Time Pressure
Transformational
Leadership
Employee Performance
Job
Commitment
5. Hypothesis: 2
The greater the employees' job satisfaction and commitment, the lower their turnover intention.
