1. Business Research Process (Step-6): Research Design
Chapter 7
Business Research Process (Step-6): Research Design
References:
Research Methods For Business (Uma Sekaran)
VU Book of BRM
Business Research Methods (William G. Zikmund)
Internet
Resource Person: Furqan-ul-haq Siddiqui

2. The Business Research Process
The Business Research Process
Problem definition
Theoretical
Framework
Variables
Identification and labelling
Generation of
Hypothesis
Observation
 Broad problem area 1
Preliminary Data Gathering  5 4 2 3
Scientific
Research
Design 6
Data Collection, analysis & interpretation NO
Deduction
Research Question Answered?
Decision
Making
Report
Presentation
Report
Writing
Yes

3. The Research Design
A research design is a master plan specifying the methods and procedures for collecting and analyzing the data.
A research design involves a series of rational decision-making choices depending upon the various options available to the researchers.
Broadly it is composed of different elements like: the purpose of the study, the unit of analysis, time dimension, mode of observation, sampling design, observation tools, data processing, and data analysis

4. Elements of Research Design:
the purpose of the study (exploratory, descriptive, and explanatory)
the unit of analysis (individuals, dyads, groups)
time dimension (Cross sectional & longitudinal)
Researcher Control of Variables (In an experiment, the researcher attempts to control and/or manipulate the variables in the study and with an ex post facto design, investigators have no control over the variables in the sense of being able to manipulate them. They can only report what has happened or what is happening.
Choice of Research Design: Mode of Observation (survey, experiment, content analysis, field observation, case study, focus group discussion.)
Sampling Design
Observation Tools
Field Data Collection
Data Processing and Data Analysis

5. 1. The Purpose of the Study
Exploratory Research
Descriptive Research
Explanatory Research
Here our focus is on whether our study is going to be a descriptive or explanatory.
If the research is concerned with finding out who, what, where, when, or how much, then the study is DESCRIPTIVE.
If it is concerned with learning why – that is how one variable produces changes in another or how one variable is related with another – it is EXPLANATORY.

6. Explanatory Research
Within the explanatory study, researcher may further decide about the type of investigation i.e. causal versus corelational.
Causal study
the study in which the researcher wants to delineate the cause of one or more problems.
Does smoking cause cancer?
Correlational study
It states the relationship between two variables, finding a correlation does not prove that one variable causes a change in another variable. .
There is a relationship between academic success and self-esteem, but it cannot prove that a change in the first variable causes a change in the second variable.

7. Causal Study
The study in which the researcher wants to determine the cause of one or more problems
E.g. High salaries cause motivation ?
To study whether variable X causes variable Y

8. Experimental Design
Experimental designs are set up to examine possible cause and effect relationship among variables,
In contrast to correlational studies, which examine the relationship among variable without necessarily trying to establish if one variable causes another.
Correlational studies are conducted in natural environmental with minimum interferance by the researcher)

9. Conditions of Causal Research
To establish that variable X causes variable Y, all three of the following conditions should met:
1. Both X and Y should covary i.e., when one goes up, the other should also simultaneously goes up (or down).
2. X (the presumed causal factor) should precede Y. In other words, there must be a time sequence in which the two occur.
3. No other factor should possibly cause the change in the dependent variable Y.

10. Experimental designs :Categories
Experiment done in an artificial or contrived environment, known as lab/controlled experiments.
Those done in the natural environment in which activities regularly take place, known as field experiment.

11. Lab/controlled Experiments
The controls and manipulations are best done in an artificial setting - the laboratory – where the causal effects can be tested.
When controls and manipulations are introduced to establish cause and effect relationships in an artificial setting, we have laboratory experimental designs, also known as lab experiments.
In Lab experiments, all other variables that might contaminate or confound the relationship have to be tightly controlled.
It is also necessary to manipulate the independent variable so that the extent of its causal effects can be established.
(Contaminate آلودہ) Make (Confounding Urdu Meanings: منتشر کرنا 
) impure by exposure to or addition of a poisonous or polluting substance.
Synonyms
pollute - defile - infect - taint - foul - soil

12. Control
When we postulate causal relationship between two variables in an organizational setting, several variables that might effect dependent variable have to be controlled.
If other factors effect on DV then it will not be possible to determine the extent to which Y occurred because of X. In such a case, it will not possible to determine the extent Y occurred only because of X, and to what extent Y was additionally influenced by the presence of the other factor
Therefore we try to control other factors. This would then allow us to say that variable X alone causes the dependent variable.

13. Example:
HRD Manager arranged a special training in creating web pages to the set of newly recruited secretaries that such training would cause them to function more effectively.
Some of the new secretaries might function more effectively than others possibly because they have had previous experience of creating web pages. Here in this example previous experience is the variable that needs to be controlled. This can done by excluding those secretaries from training who have already some experience of it.

14. Manipulation of Independent Variable
In order to examine the causal effects of an independent on a dependent variable, certain manipulation need to be tried.
Manipulation simply means that we create different levels of the independent variable to assess the impact on the dependent variable.

15. Example:
An entrepreneur is disappointed with the production level of dolls produced by his workers who are paid wages at an hourly rate. He wants to experiment whether the introduction of piece rate system will increase the production levels.
Piece rate (independent)-----> Production level (dependent)
Previous experience, gender, age (contaminating variables that need to be controlled)

16. Parts of Experiments Design:
We can divide the experiments into 5 parts. Not all experiments have all these parts, and some have all these parts plus others. The following five usually make up a true experiment.
1. Treatment or independent variable.
2. Dependent variable.
3. Pretest & Posttest.
4. Experimental group vs. Control group.
5. Controlling effects of other (extraneous) variables.

17. Treatment or independent variable: The experimenter has some degree of control over the independent variable. In experimental design the variable that can be manipulated to be whatever the experiment wishes
Dependent Variable: It is assumed that the changes in the dependent variable are consequence of changes in the independent variable.
Pretests and Posttests: Frequently a researcher measures thee dependent variable more than once during an experiment. The pretest is the measurement of the dependent variable prior to the introduction of the treatment. The posttest is the measurement of the dependent variable after thee treatment has been introduced into the experimental situation

18. Experimental and Control groups
Divide subjects into 2 or more groups for purposes of comparison.
A simple experiment has only 2 groups, only one of which receives the treatment.
Experimental group is the one that receives the treatment or in which treatment is present.
Group that does not receive the treatment is control group.
When X has many different values, more than one experimental group is used.

19. Example
Consider measuring the influence of a change in work situation, such as playing music during working hours, on employee productivity.
In the experimental condition (the treatment administered to the experimental group), music is played during working hours.
In control group (treatment not administered) no change in work situation.
Productivity in the two groups is compared at the end to determine the effect of X.

20. Several treatment levels
The music/productivity experiment with one experimental and on control group may not tell the researcher everything about the relationship
For understanding the functional nature of relationship between music and productivity at several treatment levels, additional experimental groups with music played for 2 hrs, only for 4 hrs, and only for 6 hrs.
Allows the experimenter to get a better idea about the impact of music on productivity.

21. Controlling the effects of extraneous variables.
These are variables other then IV that may bear any effect on the behavior of the subject being studied or things that influence your results, and are a source of error. These are the things we attempt to control. For instance, e.g. in law of demand, the relationship is between the price and demand but the extraneous variable such as income, taste and preferences, season and other will influence the demand for a project. This means the dependent variable demand is not only affected by its independent variable - price, but also the extraneous variable.

22. Ways of Controlling the effects of extraneous variables
Matching Groups
One way of controlling the contaminating or “nuisance” variable is to match various group by taking the confounding characteristics and deliberately spreading them across groups.

23. Example:
If there are 20 women among the 60 members, then each group will be assigned 5 women, so that the effects of gender are distributed across the four groups. Similarly, age and experience factors can be matched across the four groups such that each group has a similar mix of individuals in terms of gender, age and experience. Because the suspected contaminating factors are matched across the groups, we may take comfort in saying that variable X alone cause variable Y.

24. Randomization
Another way of controlling the contaminating variables is to assign the 60 members randomly to the four groups that is every member would have known and equal chance of being assigned to any of these four groups.

25. For instance
we might throw the names of the 60 members into hat and draw their names. The first 15 names may be assigned to the first group, the second 15 to the second group, and so on or the first person drawn might be assigned to the first group, the second person to the second group, and so on.
Thus, in randomization, both the process by which individuals drawn is random (i.e, everyone has known and equal chance of being drawn) and the assignment of the individual to any particular group is random ( each individual could be assigned to any one of the groups set up). By thus randomly assigning members to the group we would be distributing the confounding variables among the groups equally.

26. Advantages of Randomization
The difference between matching and randomization is that in the former case individuals are deliberately and consciously matched to control the differences among group members,
whereas in the latter we expect that the process of randomization will distribute the inequalities among the groups, based on the law of normal distribution

27. Comparison between Randomization & Matching
Comparison to randomization, matching might be less effective, because we may not know all the factors that could possibly contaminate the cause and effect relationship in any given situation, and hence fail to match some critical factors across all groups while conducting an experiment. Randomization, however, will take care of this, since all the known and unknown contaminating factors will be spread across all groups.

28. Researches containing above elements are called true experiments
Thus far, we have explained that for experimental research we need:
a hypothesis for a causal relationship;
a control group and a treatment group;
to eliminate confounding variables that might mess up the experiment and prevent displaying the causal relationship; and
Randomization & matching, in order to keep accidental differences from fouling things up.
Researches containing above elements are called true experiments

29. Quasi-experiment
The word "quasi" means as if or almost, so a quasi-experiment means almost a true experiment. Quasi-experiment is a research design having some but not all of the characteristics of a true experiment. The independent variable may not be manipulated by the researcher, treatment and control groups may not be randomized or matched, or there may be no control group

30. Example
Mobile phone usage: I have a hypothesis that females use more than males. I find a sample of 50 females and 50 males, then measure the usage in these groups. I find that males use significantly more than females. From this I can conclude that gender predicts, is associated with, or is consistent with males preferring mobiles more than females.
we could insert any independent variable in this example (e.g. age, income, height, etc).

31. Forms of Quasi-experiment Designs
One-shot Case Study Design: Also called the one-group posttest-only design, the one-shot case study design has only one group, a treatment, and a posttest. Because it is only one group, there is no random assignment. For example, a researcher shows a group of students a horror film, then measures their attitude with a questionnaire. A weakness of this design is that it is difficult to say for sure that the treatment caused the dependent variable. If subjects were the same before and after the treatment, the researcher would not know it.

32. One Group Pretest-posttest Design: This design has one group, a pretest, a treatment, and a posttest. It lacks a control group and random assignment. Continuing with the previous example, the researcher gives a group of students an attitude questionnaire to complete, shows a horror film, then has them complete the same questionnaire second time. This is an improvement over the one-shot case study because the researcher measures the dependent variable both before and after the treatment. But it lacks the control group for comparison. The researcher cannot know whether something other than the treatment occurred between the pretest and the posttest to cause the outcome.

33. Two Groups Posttest-only Design: It has two groups, a random assignment of subjects, a posttest, and a treatment. It has all parts of the classical design except a pretest. Continuing with our previous example, the researcher forms two groups through randomization process. He shows group a horror film to one group i.e. the experimental group. The other group is not shown any film. Both groups then complete the questionnaire. The random assignment reduces the chance that the groups differed before the treatment, but without a pretest, a researcher cannot be as certain that the groups began the same on the dependent variable.