chapter 6

RESEARCH DESIGN

The principal task of all science is to find a systematic explanation for the phenomena of the material universe. We derive such explanations and understandings from the data around us and by its analysis. Major task in every research activity is to find such interesting relationships between attributes that make up the raw data. Research design emphasizes techniques for an organized research process to achieve data collection, its development and techniques for data analysis. This chapter describes the research design and specific methodologies adopted.

Research design is often confused with the choice of research method – the decision to use either qualitative or quantitative methods. These decisions are part of the research design process but they are not the whole of it. It is easiest to think of research design as having two levels. The first level describes the logic of research, its framework or structure. It is at this level we come to know about the nature of the research whether it is exploratory, descriptive or explanatory. We also make decision on what type of study need to be taken forward like, whether to use a cross-sectional, a longitudinal, an experimental design or a case study oriented work plan. At the second level, it is about the “mechanics” of the research – what type of data is used; is it primary or secondary, qualitative or quantitative or a combination, what methods of data collections are employed, what sampling strategy is made used of, and so on. The first level is about designing the overall structure of the research and the second level concerns decisions about how to collect that evidence. Figure 6.1 shows the two levels of research design.

Level one defines the research problem. It also brief on how to use the data that are collected. It also deals with the sort of evidence that the researcher wants to prove. Deciding the logic and structure of the research is also included in the level one of research design.

Level two mentions the various methods involved in data collection along with the type of data that need to be selected. Data collection instruments that are needed and the sampling strategy designing also come in level two.

6.1 NEED FOR RESEARCH DESIGN

Research design is highly important because we rely on it to deliver the necessary evidence to answer the research problem as accurately and clearly as possible. A sound research design is a framework on which good quality research is built. It also facilitates the smooth sailing of the various research operations making the process efficient but with minimal expenditure. We can relate the research design to “constructing a house”. We never start building a house from scratch. We need to be thoroughly aware of the entire plan, the elevation, the electric, water lines, etc. to begin with. If we have a good and intact blueprint of the house, it is easy to build the house in a very cost effective manner. Applying research design in this scenario, we can say that it is the initial planning phase to create the blueprint. It is in accordance to this plan that the entire research activity runs.

Preparation of the research design should be done with great care as any error here may upset the entire project. Even though the importance of design is known, many a times the researchers find it very difficult to plan their work, so they just let it go with the flow. But they will face problems in between and may come up with unfavourable conclusions. Flaws in designing may even result in rendering the whole research exercise futile. Thus, a very appropriate and efficient research activity design needs to be developed before starting the work. You can consult your teachers, researchers from your own area, etc. to share your thoughts with, before making the design ready. When the ideas are arranged in an order, the flaws and inadequacies can be easily spotted. Also your coresearchers can offer you valuable comments and evaluation reports for your design activities. If this is not done properly, it may be difficult to provide a comprehensive review of the study.

6.2 FEATURES OF A GOOD DESIGN

A good research design is often characterized by various features such as flexibility, efficiency and cost effectiveness. The design should describe each level of work, thereby providing a comfort zone for researchers. The design which gives the smallest experimental error is supposed to be the best design in many investigations. A design that includes maximum possible outcomes, and also incorporates almost all scenarios/perspectives which can occur during the research can be seen as a well-formed design. For example, an organization is carrying out a survey for finding the effect of road widening on people. It should consider all aspects, not just the development it can cause to the area but also the scheme to relocate and rehabilitate those who lose land. It should also mention all sorts of barriers that can come across in their work. The protest from people, how to face a natural calamity, the extra costs that can occur and so on. A good design should incorporate all these key features (Fig. 6.2). The nature of the problem and the objective of the research hold the key while defining the features of a research. That is, the design suitable for one problem may not suit to another. Thus, it can be said like, research designs are unique.

Before finalizing a design, the following factors need to be well studied.

• Objective of the work
• Nature of research
• Skill set of researcher and his team
• Methods for obtaining information
• Time and money involved in the work

If a researcher is keen in formulating new idea, then the research must be so designed that it should have windows for considering all perspectives. The design should be very flexible; it should be able to incorporate and implement any new idea or remove a faulty one even in later stage of design. There are also types of research where studies of some already established facts and figures are being carried out. In such types of research, skill of the research team is very important. They need to get all information on the subject. Here the skills of the team are always under review. There are certain types of research which may produce a high impact on its finding. But the researcher should always design his/her work depending on the time and amount of money they can put into it for its successful completion. Else the project gets halt in between.

Validity is another key concept in assessing the quality of research. In other words, it refers to how well a research design measures and what it claims to measure. How well it gives us clear and unequivocal evidence with which to answer the research problem.

Internal validity in the context of research design refers to ability of the research to deliver credible evidence to address the research problem. In other words, the job of the research design is to ensure that the research has internal validity. In causal or explanatory research, for example, it is about the ability of the research design to allow us to make links or associations between variables, to rule out alternative explanations or rival hypotheses and to make inferences about causality. Internal validity is important when designing questions and questionnaires. In this context, internal validity refers to the ability of the questions to measure what it is we think they are measuring.

When a piece of research has external validity, it means that we can generalize from the research conducted among the samples (or in the specific setting) to the wider population (or setting).

Research design is important because we rely on it to deliver the evidence necessary to answer the research problem as accurately, clearly and unequivocally as possible. A sound research design is the framework on which good quality research is built. This summarizes the main points,

• Appropriateness to the research question
• Lack of bias
• Plan and strategy
• Precision, power and budget

Case Study

A good research should always provide an outcome which is well desirable to the society. One of the best features of a research design is to prevent the illicit usage of the outcome of a good research. So always a study on the by-products of the result is necessary. For example, the drug generated for painkiller, Morphine, has a great drawback. One of the derivatives of Morphine is Heroin, which is the most commonly found drug abuse. Same is the case with amphetamines, which was a drug used for treating depression and nasal congestion. But its derivatives are also used as a drug.

6.3 TYPES OF RESEARCH DESIGNS

There are several research designs and the researcher must decide in advance of collection and analysis of data as to which design would prove to be more appropriate for his research project. The various types of research designs include the following:

1. Exploratory research design
2. Conclusive research design
3. Experimental research design

There are also other research designs such as descriptive design, casual design, cross-sectional design, longitudinal design, action design, case study design and historic design. All these come under conclusive research design. The major category of research designs is explained in Fig. 6.3.

6.3.1 Exploratory Research Design

Exploratory research design is also called as formulative design. In this type of design, a working hypothesis is developed by keen investigation of the team from an operation point of view. From such investigations, new ideas and aspects are developed. As the name suggests, each idea thus evolved is studied in deep by exploring all the possibilities and a final conclusion is made (Fig. 6.4). There needs to have a great flexibility in the process as the research may start from a point and as time passes, it can get new objectives and scope which can lead to more desirable solutions.

In exploratory research design, the major ways by which the researcher explores a particular subject are either by

• Survey of related literature
• Survey-experienced researchers
• Insight ideas

Survey of literature is the most easy and simple method for formulating hypothesis. Literature survey helps the researcher to understand those theories which are already stated. He/She can also get an understanding on the evaluations done in theories on his/her area. Reading and reviewing other works help him/her to know, whether the work he/she is planning to do is new or not. Survey of literature should not go very vast and outside the scope of the subject.

Conducting a survey on the experienced researchers in the same field, the new researchers can get many ideas and can get to know the hardships that the earlier had to face in the past. The major issue here is to spot the people in their area. Once they have the people, he/she can schedule an official meeting and can get to know them in detail and regarding the work they have done. It will be always good to prepare a list of questions that the researcher needs to ask. If the researcher can share these questions to the interviewee, then it will be great as he/she can be well prepared and it will help the researcher in obtaining more knowledge. Such an interview experience may enable the researcher to define the problem more concisely and help in the formulation of the research hypothesis.

There are situations where the researcher needs to make some decisions based on the studies and other literatures available. He/She needs to choose a particular path for his/her further work. But sometimes there may not be a fully proven theory for him/her to select the path. In such cases, he/she needs to go along with his/her insight views and stick to it and move along. Mostly, the researcher never blindly believes his/her heart, it will always be a logical decision. A hard heart is needed to jump a broad river.

Case Study

Consider a researcher who wants to study on migratory birds. He/She needs to first observe a whole year to find out which time of the year does migration happens. This gives him/her a primary knowledge on, what are the conditions required for migration. He/She has to then find out the general species or family of the birds who migrates in group. From that he/she focusses on those birds, which migrate during a particular season in a year. Again he/she can explore on what are the special characteristics of those birds. That is, from a general point, he/she explores the situation and gets to a specific conclusion.

6.3.2 Conclusive Research Design

Conclusive research is more likely to use statistical tests, advanced analytical techniques and larger sample sizes, compared with exploratory studies. It provides information to manager for making a correct decision. It consists of formal research procedures which clearly defines the goals and needs. Unlike other research methods, a questionnaire is designed in conjunction with a sampling plan. Various research designs such as descriptive, casual, cross-sectional, longitudinal come under conclusive research. Figure 6.5 shows the types of conclusive research design.

Malhotra and Birks (2000) divided conclusive research design into further two categories: descriptive research which is used to describe some functions or characteristics and causal research which is used to research cause and effect relationships.

Descriptive research

Descriptive research studies are those studies which are concerned with describing the characteristics of a particular individual, or of a group. Studies concerned with specific predictions, with narration of facts and characteristics concerning individual, group or situation are all examples of descriptive research studies. Most of the social research comes under this category.

There are certain requirements to carry out descriptive research. The researcher should be able to clearly define his population and must define what amount of data is he expecting and the adequate methods for measuring. Since it is a descriptive study, the whole aim is to obtain a well-defined step-by-step procedure; hence, more careful planning is required in this type of research design. There should be certain points where more focus need to be given.

Primary need is to keep track on the various objectives. The objectives need to be studied precisely to make sure that the data collected are sufficient and relevant. Selecting the method by which data can be obtained is the secondary concern. Several methods such as observation, questionnaires, interviewing and examination of records can be done to collect the data. One major issue that needs to be answered is the identification and avoidance of biased data, only then reliable data can be obtained.

In most of the studies, researchers take a sample population which could be easily tackled with minimum effort that yields maximum information. The researcher may not be able to do a field visit and collect data most of the time. So it will be a good idea to have a field officer who takes care of the data collection, which in turn helps to provide us with valid and error-free data source.

The data collected must be processed and analyzed. This includes steps such as coding the interview replies, observations, tabulating the data and performing several statistical computations. To the extent possible, the processing and analyzing procedure should be planned in detail before actual work is started. This will prove economical in the sense that the researcher may avoid unnecessary labour such as preparing tables for which he/she later finds and he/she has no use or on the other hand. He/She can redo some tables because he/she failed to include relevant data. Coding should be done carefully to avoid error in coding and for this purpose the reliability of coders needs to be checked. Probability and sampling analysis may be used as well. The appropriate statistical operations, along with the use of appropriate tests of significance, should be carried out to safeguard the drawing of conclusions concerning the study.

Finally, there comes the question of reporting the findings. This is the task of communicating the findings to others and the researcher must do it in an efficient manner. The layout of the report needs to be well planned so that all things relating to the research study may be well presented in simple and effective style.

Casual research

The aim of causal research is to provide explanations. It is also known as explanatory research for that reason. For example, it might be used to find out why people buy brand A and not brand B or why some people are in favour of capital punishment and others are not. It can be used to rule out rival explanations and come to a conclusion.

It may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y”. This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Causal research designs assist researchers in understanding the link between variables and the process of eliminating possibilities that are found to be ambiguous. Not all relationships are casual. The possibility always exists that, by sheer coincidence, two unrelated events appear to be related, so we can never always trust the outcome of a casual research. This is also one of the major reasons why this research is not practised all over for sensitive subjects. Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment.

Cross-sectional design

Cross-sectional research design allows you to collect data from a cross-section of a population at one point in time. A single cross-sectional design involves only one wave or round of data collection; data are collected from a sample on one occasion only. A repeated cross-sectional design involves conducting more than one wave of (more or less) the same research with an independent or fresh sample each time. The use of an independent sample at each round of data collection is what distinguishes repeated cross-sectional design from longitudinal research. In longitudinal research, data are collected from the same sample on more than one occasion.

For example, a cross-sectional design can be used to provide data for an exploratory or descriptive research enquiry – to understand the health information needs of older people. It can also be used to look for and examine relationships between variables; to test out ideas and hypotheses; to help decide which explanation or theory best fits with the data; and to help establish causal direction but not to prove cause. For example, it might be used to determine what factors are involved in the decision to take out critical illness benefit insurance, and the relationship between the factors.

Cross-sectional studies provide a clear “snapshot” of the outcome and characteristics associated with it, at a specific point in time. Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects or phenomena. Cross-sectional studies are capable of using data from a large number of subjects. One of the major disadvantages is that, as it provides a snapshot of analysis there is always the possibility that a study could have differing results if another time-frame had been chosen (Fig. 6.6).

Longitudinal design

Longitudinal research involves collecting data from same sample (example individuals or organizations) on more than one occasion. The number and frequency of the snapshots or data collection points depend largely on the research objectives. For example, if the purpose of the research is to look at the immediate, short-term impact of an advertising campaign. Then a relatively small number of data collection points, fairly closely spaced in time, may be required. To examine the longer term impact of advertising on a brand may require a relatively large number of data collection points over many years.

The main application of longitudinal design is to monitor changes in the marketing or social environment, changes that occur in the normal course of things and events that are planned. For example, changes as a result of an advertising campaign, a new product launch or an election. Longitudinal design can be used to provide data for descriptive research enquiry. Although it cannot be used to prove cause, it can be used to

• Explore and examine relationships between variables
• Establish the time order of events or changes, and age or historical effects
• Help decide which explanation or theory best fits with the data
• Help establish causal direction (rather than prove cause)

Table 6.1 shows the differences between longitudinal and cross-sectional study.

 

Action research design

Action research is a type of qualitative research that seeks action to improve practice and study the effects of the action that was taken. It can be seen as a cyclic process. The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventional strategy. Then these interventions are carried out and the results are observed. If we obtain a positive curve for our studies, then same approach is carried out until the sufficient results are obtained. It is almost an iterative cycle. For a computer science student, they can easily relate this to an iterative waterfall model. It helps in deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

In this case, design is focussed on a solution-based approach than any theoretical science. There is no hidden control of the researcher. It is just the action of the researcher that gives a positive output and is taken as a step. There are various disadvantages for this type of research design. It is fully dependent on trial and error method. If the actions of the researcher are not in the correct direction, he/she may never get a valid conclusion. As it is not upholding any theoretical background, a roll back will not be always possible. Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively. Also the personal involvement of the researcher can bias the results.

Participatory action research (PAR) is a special kind of community-based action research in which there is collaboration between the study participants and the researcher in all steps of the study: determining the problem, the research methods to use, the analysis of data, and how the study results will be used. The participants and the researcher are co-researchers throughout the entire research study.

Case Study Design

A case study is an in-depth investigation of a “case” for exploratory, descriptive or explanatory research purposes, or a combination. A “case” might be, for example, a household, an organization, a situation, an event or an individual’s experience. Case study research may involve examining all aspects of a case – the case as a whole and its constituent parts. For example, a case study of a particular household may involve data collection from individual members; in an organization, the elements of the case might be departments and individuals within departments. A case study design might be made up of several case studies, not just one. A variety of methods of data collection can be used in a case study, including analysis of documents, observation and qualitative and quantitative interviewing.

Data may be collected in case studies through various means such as questionnaires, interviews, observations or written accounts by the subjects. Content analysis is used in evaluating the data from case studies. Content analysis involves the examination of communication messages.

One of the major disadvantages of case study is that they are time consuming and may be quite expensive. Additionally, subject drop-out may occur during this type of study. Whenever a study is carried out over an extended period, loss of subjects must be considered. A person may move from the locality or simply decide to discontinue participation in the study. If the criteria for selecting the case is just because it represents an unusual or unique phenomenon, then the interpretation of that study will only be applicable to that particular case.

Case Study

There are various treatments methods available to cure a disease. All of these methodologies have a common goal which is to prevent or treat the disease. Each one has different origin and different way of treatment. Here let us take the major branches Homoeopathy and Allopathy to have a comparative study.

Allopathy is drug-oriented methodology. It mainly depends on three things: hypothesis, experimentation and the outcome of the experiment. This methodology basically depends on experimentation. In this, doctors treat a disease based on symptoms not on the causes. But treatment in the case of Homoeopathy is in accordance with a case-based reasoning approach. The doctor treats for the cause and not for the disease. It is highly dependent on individuality. Two persons having the same disease are treated differently. The doctor makes a set of test cases, which are the primary questions to the patient. From the primary test cases, he/she builds a hypothesis by having an inference on the answers obtained. It helps the doctor to make a secondary set of questions; this continues until he gets the correct root cause of the disease.

Like we mentioned, case-based studies are time consuming but a very promising result is obtained. The validity of the result fully depends on the cases made and the inferences drawn through those case studies.

Historic research design

Leininger (1985) wrote that “Without a past, there is no meaning to the present, nor can we develop a sense of ourselves as individuals and as members of groups.” Historical studies concern the identification, location, evaluation and synthesis of data from the past. Historical research seeks not only to discover the events of the past but to relate these past happenings to the present and to the future. The process of historical research is basically the same as in many other types of scientific research. The problem area or area of interest is clearly identified and the literature is reviewed. Research questions are formulated. Finally, the data are collected and analyzed. Historic researchers need to have a bit more of curiosity, perseverance, tenacity and scepticism of a detective. The data for historical research are usually found in documents or in relics and artefacts. Documents may include a wide range of printed material. Relics and artefacts are items of physical evidence. The material may be found in libraries, archives or in personal collections

The sources of historical data are frequently referred to as primary and secondary sources. Primary sources are those that provide first-hand information or direct evidence. Secondary sources are second-hand information.

Primary sources should be used in historical research when possible. There are many examples of primary sources: oral histories, written records, diaries, eyewitnesses, pictorial sources and physical evidence. The data for historical research should be subjected to two types of evaluation. These evaluations are called external criticism and internal criticism. External criticism is concerned with the authenticity or genuineness of the data and should be considered first. Internal criticism examines the accuracy of the data and is considered after the data are considered to be genuine. While external criticism establishes the validity of the data, internal criticism establishes the reliability of the data. Internal criticism of historical data is more difficult to conduct than external criticism. In the case of a written document, internal criticism would evaluate the material contained in the document. Motives and possible biases of the author must be considered in trying to determine if the material is accurate.

Consider the example of a researcher who is working in the area of vaccination research. If the researcher is keen in finding the history of a particular drug, then he/she have to go through a lot of literature to find the information regarding it. The information such as the place where it was developed, the period in which it was created and so on needs to be known. While having a site visit to such a laboratory, he/she may get some personal diaries or records of the doctors who were involved in the process of drug development. It can be used as primary source of literature. Secondary literature can involve some information/articles wrote by some external agents regarding the drug development.

So far we have seen the various subcategories in conclusive research design.

Figure 6.7 details the major difference between exploratory and conclusive research design.

6.3.3 Experimental Research Design

Two identical samples or groups are recruited: one is known as the test group and the other is the control group. The test and control groups are matched on key criteria – in other words, the two are the same on all key characteristics. The independent variable – the one that is thought to cause or explain the change – is manipulated to see the effect that this change has on the dependent variable. This is referred to as the treatment. The treatment is applied to the test group but not to the control group. The purpose of the test group is to observe the effect of the treatment, whereas the purpose of the control group is to act as a comparison. Since the treatment is not applied to the control group, any changes that take place will not be due to the independent variable but to some other factor(s). The design of the experiment should be such that the effect of other factors is limited or controlled. Comparison of the test and control groups allows us to determine the extent of the change that is due to the independent variable only. This type of experimental design is called as the “after with a control group”. There are variations to this design: when the independent variable and the dependent variable are measured in both groups before the “treatment” takes place, the design is called as “before and after”; if a control group is used it is called, not surprisingly, as “before and after with a control”.

The main application of experimental research designs is to determine whether a causal relationship exists and the nature of the relationship, and to rule out the effects of other variables and to establish the time order or sequence of events (which is the cause and which is the effect). It is used in marketing experiments, for example, to make decisions about elements of the marketing mix, to evaluate effectiveness of advertisement A or B, or the weight of advertising spend, or the combination of media to be used in a campaign.

An experimental design was used to examine the effects of monetary incentives on response rates to a mail survey. More specifically, to examine the relative effectiveness of prepaid cash incentives, a cash prize and an equivalent value non-cash prize was introduced for increasing mail survey response rates.

Scientific control group

In the social sciences, control groups are the most important part of the experiment, because it is practically impossible to eliminate all of the confounding variables and bias. There are two main types of control, positive and negative, both providing researchers with ways of increasing the statistical validity of their data. Figure 6.8 shows the confounding effect on dependent and independent variables.

Positive scientific control groups are where the control group is expected to have a positive result, and allows the researcher to show that the set-up was capable of producing results. Positive scientific control groups reduce the chances of false negatives.

Negative scientific control is the process of using the control group to make sure that no confounding variable has affected the results, or to factor in any likely sources of bias. It uses a sample that is not expected to work. A negative control can also be a way of setting a baseline.

Research design principles

Let us now focus on the three basic principles of experimental research design.

1. Principle of replication

   According to the Principle of Replication, the experiment should be repeated more than once. By doing so, the accuracy of the experiments gets increased. This is just similar to the primary school level chemistry practicals, where we repeat the experiment many times to find the acid base balance. Conceptually replication does not present any difficulty, but there are some computational difficulties. But as this replication increases the accuracy of the study, this is widely accepted in almost all experimental research designs.

2. Principle of randomization

   It provides protection, when we conduct an experiment, against the effect of extraneous factors by randomization. In other words, this principle indicates that we should design or plan the experiment in such a way that the variations caused by extraneous factors can all be combined under the general heading of “chance”.

3. Principle of local control

   According to this principle, we should plan the experiment in such a manner that we can perform a two-way analysis of variance, in which the total variability of the data is divided into three components: experiment groups, extraneous factor and experimental error. Here the extraneous factor (the source of variability) is deliberately divided amongst all groups, so that the variation caused can be measured and can be eliminated from the experimental error.

6.4 INDUCTION AND DEDUCTION

In logic, the two methods of reasoning may be classified as deductive and inductive approaches. They are two different methods that can be utilized to arrive at a solution.

6.4.1 Deduction

In this method, we try to start reasoning from a generalized approach and then narrow our thinking down to a more specific one. This can also be referred to as top-down method. Here we start making a theory in our area and then mould it into a more specific hypothesis. Then we add on observations to this hypothesis. Finally, we test this hypothesis and arrive at a confirmation of our theory. Figure 6.9 shows the method flow of deduction.

In deduction, the conclusions drawn are necessary and true. Deduction can also be mentioned as a process of arriving at a conclusion based on situations that you know to be true.

An example for deduction logic is, “To earn a master’s degree, a student must have 32 credits. Tim has 40 credits, so Tim will earn a master’s degree.” Here from a generalized theory we are narrowing the concept to a specific point. The generalized fact is, to earn a master’s degree we need 32 credits. Then we observe a specific specimen, Tim. He is having 40 credits, so according to our hypothesis, we can arrive at a confirmation that Tim will earn master’s degree. This type of logical reasoning is termed as deduction.

6.4.2 Induction

This may be considered to be a method working in the opposite direction of deduction. That is, we go from a specific observation or from our own experience towards a generalized theory. Induction is based on situations that we assume to be true. So the conclusions drawn here are only probable and may not always be true. This is also called the bottom-up method. Figure 6.10 represents the method flow in inductive reasoning.

Here we make a specific observation, study it to see if there is a definite pattern to an observation, if yes, we form a tentative hypothesis based on our assumption and then end up making a generalized theory.

Consider an example, “This ball from the bag is red. That ball from the bag is red. A third ball from the bag is red. Therefore all the balls in the bag are red.” This statement is an example of inductive generalization, which uses evidence about a limited number of things to make an overall assumption of most things of that type. The authentication of this type of a statement depends on the number of things used to make the assumption and the total number of things.

Deductive method is mainly concerned by forming and testing of hypothesis with only a limited point of view, whereas inductive method is more permissive and indulgent.

Case Study: Maternal mortality rate in below poverty line (BPL) families

So far we have learned the various research design techniques. Now let us take a general case study and see how the process works. We can divide the whole process into 10 steps.

Step 1: Determine the key research questions or hypotheses. What do you need to know? What relationships are you interested in investigating?

For this, our answer is, the topic under study is, “Maternal mortality rate in below poverty line families”.

Step 2: Determine very clearly what your dependent variables are. It is always easier and less costly to investigate a one-to-one relationship. However, it is often the case that we want to know either how multiple causes lead to a single effect, or to multiple effects.

In this study, for example, we are looking at one dependent variable and one independent variable. Dependent variable is to reduce the maternal mortality rate. The independent variables are Women in BPL families.

Step 3: Identify crucial intervening or confounding variables. These are variables that may intrude themselves between your purported dependent and independent variables.

Here, below poverty vs above poverty, women marital status, age during pregnancy, poverty can be the confounding variables.

Step 4: Define and identify specific and measurable (can be qualitative or quantitative) indicators for the dependent variables.

Study on maternal mortality rate in below poverty line (BPL) families shows that the various reasons for this may be listed as, lack of proper nutrition to mother during the time of pregnancy, lack of proper antenatal checkups or hospital visits, more of home deliveries as compared to institutional deliveries, unforeseen pregnancy related complications that cannot be managed in a home set-up, lack of proper hygiene in the delivery area leading to infections and so on.

Step 5: Data sources to the research need to be determined.

In our project, participating women, earning member of the family, secondary members, the government record of past studies done in this area, non-participating members for comparative study, the village doctor and his/her care staff can be included as data source.

Step 6: Determine the methods that you need in order to gather the information and data required. It should also meet levels of rigour that will satisfy the intended audience(s) of the research.

Principle: Adopt methods that are as complex as is needed, but simple in implementation. In our case,

• Quantitative survey that allows us to make statistically valid comparisons between participants and non-participants on the dependent variable (Maternal Mortality Rate)
• Secondary data review (results of similar studies in similar contexts at different places)
• Key informant interviews (on socio-cultural and gender context along with family background)
• Semi-structured interviews with women and men (qualitative and participatory numbers)
• Focus group discussions
• Study on government aids and other socio welfare groups.

Step 7: Determine the overall research design strategy, longitudinal (data will be collected at least twice over some period of time) and cross-sectional (a single point in time). To a very large extent, this decision is determined by your actual research questions from step one. It can also be influenced by the resources you have available. It can also be influenced by a longer term evaluation strategy. In our case, we can have a cross-sectional research design.

Step 8: Determine the appropriate sampling population. Who or what is the largest population that you wish to be able to describe and/or account for in relation to your hypothesis? The key is to be very clear with ourselves and our stakeholders about who or what we are leaving out, and why and what population our research actually represents in terms of its findings.

Here the sample group can be taken to be those women who are expected to deliver in the next 6 months. The rate and number can differ according to the various data sources that we have taken in our previous steps.

Step 9: Select a sampling strategy for every level identified in step nine. There are basically two broad types of sample: probability and non-probability samples. Probability samples, also known as random samples, allow every analytical unit to have an equal chance of being selected. They allow you to generalize to a larger population. They are also best for avoiding researcher bias. Non-probability samples, also known as purposive samples, cannot, on their own, allow you to generalize to a wider group. They are more subject to researcher bias although this can be minimized through establishing strict, objective criteria for choosing data sources.

Step 10: Select a comparison group. Identify at least one comparison group – sometimes called a control group, and discuss the importance of the result and get to a conclusion and finally make the hypothesis.

EXERCISES

1. What is a research design and what are the types of basic research designs?
2. How can the basic research designs be compared and contrasted?
3. What are the major sources of errors in a research design?
4. How does the researcher co-ordinate the budgeting and scheduling aspects of a research project?
5. What elements make up the marketing research proposal?
6. What factors should the researcher consider while formulating a research design in international marketing research?
7. How can technology facilitate the research design process?
8. What ethical issues arise when selecting a research design?
9. Why is research design important?
10. What do the following terms mean: internal validity and external validity?
11. Describe what is meant by exploratory and descriptive research. Give examples of each type of enquiry.
12. What is the aim of causal research?
13. To make sound causal inferences, what sort of evidence must a research design provide?
14. 1. What is involved in a cross-sectional research design; give examples?
    2. What is involved in a longitudinal research design; give examples?
15. Describe the main stages in an experimental research design. Give an example of the application of an experimental design.
16. What is case study research? What methods of data collection are suited to a case study approach?
17. What type(s) of research design are suitable for a descriptive research enquiry?