Research Design

The research designer understandably cannot hold all his decisions in his head. Even if he could, he would have difficulty in understanding how these are inter-related. Therefore, he records his decisions on paper or record disc by using relevant symbols or concepts. Such a symbolic construction may be called the research design or model. A research design is a logical and systematic plan prepared for directing a research study. It specifies the objectives of the study, the methodology and techniques to be adopted for achieving the objectives. It constitutes the blue print for the plan is the overall scheme or program of research. A research design is the program that guides the investigator in the process of collecting, analyzing and interpreting observations. It provides a systematic plan of procedure for the researcher to follow elltiz, Jahoda and Destsch and Cook describe, “A research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to there search purpose with economy in procedure.” Components of Research Design:

It is important to be familiar with the important concepts relating to research design. They are: 1.Dependent and Independent variables:

A magnitude that varies is known as a variable. The concept may assume different quantitativevalues, like height, weight, income, etc. Qualitative variables are not quantifiable in the strictest sense of objectivity. However, the qualitative phenomena may also be quantified in terms of the presence or absence of the attribute considered. Phenomena that assumedifferentvalues quantitatively even in decimal points are known as “continuous variables. But, all variables need not be continuous. Values that can be expressed only in integer values are called” non-continuous variables. In statistical term, they are also known as „discrete variable. For example, age is a continuous variable;whereasthenumberofchildrenisanon-continuous variable.

When changes in one variable depends upon the changes in one or more other variables, it is known as a dependent or endogenous variable, and the variables that cause the changes in the dependent variable are known as the independent or explanatory or exogenous variables. For example, if demand depends upon price, then demand is a dependent variable, while price is the independent variable. And if, more variables determine demand, like income and prices of substitute commodity, then demand also depends upon them in addition to the own price. Then, demand is a dependent variable which is determined by the independent variables like own price, income and price of substitute. 2.Extraneous variable:

The independent variables which are not directly related to the purpose of the study but affect the dependent variable are known as extraneous variables. For instance, assume that a researcher wants to test the hypothesis that there is relationship between children’s school performance and their self-concepts, in which case the latter is an independent variable and the former, the dependent variable. In this context, intelligence may also influence the school performance. However, since it is not directly related to the purpose of the study undertaken by the researcher, it would be known as an extraneous variable. The influence caused by the extraneous variable on the dependent variable is technically called as an „experimental errors Therefore, a research study should always be framed in such a manner that the dependent variable completely influences the change in the independent variable and any other extraneous variable or variables.

3.Control:

One of the most important features of a good research design is to minimize the effect of extraneous variable. Technically, the term control is used when a researcher designs the studying such a manner that it minimizes the effects of extraneous independent variables. The term control is used in experimental research to reflect the restrain in experimental conditions.

4.Confounded relationship:

The relationship between dependent and independent variables is said to be confounded by an extraneous variable, when the dependent variable is not free from its effects.

Research hypothesis:

When a prediction or a hypothesized relationship is tested by adopting scientific methods, it is known as research hypothesis. The research hypothesis is a predictive statement which relates a dependent variable and an independent variable. Generally, aresearch hypothesis must consist of at least one dependent variable and one independent variable. Whereas, the relationships that are assumed but not be tested are predictive statements that are not to be objectively verified are not classified as research hypothesis.

Experimental and control groups:

When a group is exposed to usual conditions in an experimental hypothesis-testing research, it is known as „control group. On the other hand, when the group is exposed to certain new or special condition, it is known as an „experimental group. In the afore-mentioned example, the Group A can be called a control group and the Group B an experimental one. If both the groups A and B are exposed to some special feature, then both the groups may be called as „experimental groups. A research design may include only the experimental group or the both experimental and control groups together.

Treatments: Treatments are referred to the different conditions to which the experimental and control groups are subject to. In the example considered, the two treatments are the parents with regular earnings and those with no regular earnings. Likewise, if a research study attempts to examine through an experiment regarding the comparative impacts of three different types of fertilizers on the yield of rice crop, then the three types of fertilizers would be treated as the three treatments.

Experiment:

An experiment refers to the process of verifying the truth of a statistical hypothesis relating to a given research problem. For instance, experiment may be conducted to examine the yield of a certain new variety of rice crop developed. Further, Experiments may be categorized into two types namely, absolute experiment and comparative experiment. If a researcher wishes to determine the impact of a chemical fertilizer on the yield of a particular variety of rice crop, then it is known as absolute experiment. Meanwhile, if the researcher wishes to determine the impact of chemical fertilizer as compared to the impact of bio-fertilizer, then the experiment is known as a comparative experiment.

Experiment unit:

Experimental units refer to the predetermined plots, characteristics or the blocks, to which the different treatments are applied. It is worth mentioning here that such experimental units must be selected with great caution.

5. Explain the Sampling Process and briefly describe the methods of Sampling.

Sampling Procedure

The decision process of sampling is complicated one. The researcher has to first identify the limiting factor or factors and must judiciously balance the conflicting factors. The various criteria governing the choice of the sampling technique:

1. Purpose of the Survey: What does the researcher aim at? If he intends to generalize the findings based on the sample survey to the population, then an appropriate probability sampling method must be selected. The choice of a particular type of probability sampling depends on the geographical area of the survey and the size and the nature of the population under study.

2. Measurability: The application of statistical inference theory requires computation of the sampling error from the sample itself. Probability samples only allow such computation. Hence, where the research objective requires statistical inference, the sample should be drawn by applying simple random sampling method or stratified random sampling method, depending on whether the population is homogenous or heterogeneous.

3. Degree of Precision: Should the results of the survey be very precise, or even rough results could serve the purpose? The desired level of precision as one of the criteria of sampling method selection. Where a high degree of precision of results is desired, probability sampling should be used. Where even crude results would serve the purpose (E.g., marketing surveys, readership surveys etc) any convenient non-random sampling like quota sampling would be enough.

4. Information about Population: How much information is available about the population to be studied? Where no list of population and no information about its nature are available, it is difficult to apply a probability sampling method. Then exploratory study with non-probability sampling may be made to gain a better idea of population. After gaining sufficient knowledge about the population through the exploratory study, appropriate probability sampling design may be adopted.

5. The Nature of the Population: In terms of the variables to be studied, is the population homogenous or heterogeneous? In the case of a homogenous population, even a simple random sampling will give a representative sample. If the population is heterogeneous, stratified random sampling is appropriate.

6. Geographical Area of the Study and the Size of the Population: If the area covered by a survey is very large and the size of the population is quite large, multi-stage cluster sampling would be appropriate. But if the area and the size of the population are small, single stage probability sampling methods could be used.

7. Financial resources: If the available finance is limited, it may become necessary to choose a less costly sampling plan like multistage cluster sampling or even quota sampling as a compromise. However, if the objectives of the study and the desired level of precision cannot be attained within the stipulated budget, there is no alternative than to give up the proposed survey. Where the finance is not a constraint, a researcher can choose the most appropriate method of sampling that fits the research objective and the nature of population.

8. Time Limitation: The time limit within which the research project should be completed restricts the choice of a sampling method. Then, as a compromise, it may become necessary to choose less time consuming methods like simple random sampling instead of stratified sampling/sampling with probability proportional to size; multi-stage cluster sampling instead of single-stage sampling of elements. Of course, the precision has to be sacrificed to some extent.

9. Economy: It should be another criterion in choosing the sampling method. It means achieving the desired level of precision at minimum cost. A sample is economical if the precision per unit cost is high or the cost per unit of variance is low.

Methods of Sampling

Sampling techniques or methods may be classified into two generic types:

Probability or Random Sampling

Probability sampling is based on the theory of probability. It is also known as random sampling. It provides a known nonzero chance of selection for each population element. It is used when generalization is the objective of study, and a greater degree of accuracy of estimation of population parameters is required. The cost and time required is high hence the benefit derived from it should justify the costs.

The following are the types of probability sampling:

i) Simple Random Sampling: This sampling technique gives each element an equal and independent chance of being selected. An equal chance means equal probability of selection. An independent chance means that the draw of one element will not affect the chances of other elements being selected. The procedure of drawing a simple random sample consists of enumeration of all elements in the population. 1. Preparation of a List of all elements, giving them numbers in serial order 1, 2, B, and so on, and 2. Drawing sample numbers by using (a) lottery method, (b) a table of random numbers or (c) a computer.

Suitability: This type of sampling is suited for a small homogeneous population.

Advantages: The advantage of this is that it is one of the easiest methods, all the elements in the population have an equal chance of being selected, simple to understand, does not require prior knowledge of the true composition of the population.

Disadvantages: It is often impractical because of non-availability of population list or of difficulty in enumerating the population, does not ensure proportionate representation and it may be expensive in time and money. The amount of sampling error associated with any sample drawn can easily be computed. But it is greater than that in other probability samples of the same size, because it is less precise than other methods. ii) Stratified Random Sampling: This is an improved type of random or probability sampling. In this method, the population is sub-divided into homogenous groups or strata, and from each stratum, random sample is drawn. E.g., university students may be divided on the basis of discipline, and each discipline group may again be divided into juniors and seniors. Stratification is necessary for increasing a sample’s statistical efficiency, providing adequate data for analyzing the various sub-populations and applying different methods to different strata. The stratified random sampling is appropriate for a large heterogeneous population. Stratification process involves three major decisions. They are stratification base or bases, number of strata and strata sample sizes.

Stratified random sampling may be classified into:

a)Proportionate stratified sampling: This sampling involves drawing a sample from each stratum in proportion to the latter’s share in the total population. It gives proper representation to each stratum and its statistical efficiency is generally higher. This method is therefore very popular. E.g., if the Management Faculty of a University consists of the following specialization groups:

Specialization streamNo. of studentsProportion of each stream Production

Finance

Marketing

Rural development40

20

30

100.4

0.2

0.3

0.1

1001.0

Advantages: Stratified random sampling enhances the representativeness to each sample, gives higher statistical efficiency, easy to carry out, and gives a self-weighing sample.

Disadvantages: A prior knowledge of the composition of the population and the distribution of the population, it is very expensive in time and money and

identification of the strata may lead to classification of errors. b)Disproportionate stratified random sampling: This method does not give proportionate representation to strata. It necessarily involves giving over-representation to some strata and under-representation to others. The desirability of disproportionate sampling is usually determined by three factors, viz, (a) the sizes of strata, (b) internal variances among strata, and (c) sampling costs.

Suitability: This method is used when the population contains some small but important subgroups, when certain groups are quite heterogeneous, while others are homogeneous and when it is expected that there will be appreciable differences in the response rates of the subgroups in the population.

Advantages: The advantages of this type is it is less time consuming and facilitates giving appropriate weighing to particular groups which are small but more important.

Disadvantages: The disadvantage is that it does not give each stratum proportionate representation, requires prior knowledge of composition of the population, is subject to classification errors and its practical feasibility is doubtful.

iii) Systematic Random Sampling: This method of sampling is an alternative to random selection. It consists of taking kth item in the population after a random start with an item form 1 to k. It is also known as fixed interval method. E.g., 1st, 11th, 21st ……… Strictly speaking, this method of sampling is not a probability sampling. It possesses characteristics of randomness and some non-probability traits.

Suitability: Systematic selection can be applied to various populations such as students in a class, houses in a street, telephone directory etc.

Advantages: The advantages are it is simpler than random sampling, easy to use, easy to instruct, requires less time, it’s cheaper, easier to check,

sample is spread evenly over the population, and it is statistically more efficient.

Disadvantages: The disadvantages are it ignores all elements between two kth elements selected, each element does not have equal chance of being selected, and this method sometimes gives a biased sample.

Cluster Sampling

It means random selection of sampling units consisting of population elements. Each such sampling unit is a cluster of population elements. Then from each selected sampling unit, a sample of population elements is drawn by either simple random selection or stratified random selection. Where the population elements are scattered over a wide area and a list of populationelements is not readily available, the use of simple or stratified random sampling method would be too expensive and time-consuming. In such cases cluster sampling is usually adopted. The cluster sampling process involves: identify clusters, examine the nature of clusters, and determine the number of stages.

Suitability: The application of cluster sampling is extensive in farm management surveys, socio-economic surveys, rural credit surveys, demographic studies, ecological studies, public opinion polls, and large scale surveys of political and social behaviour, attitude surveys and so on.

Advantages: The advantages of this method is it is easier and more convenient, cost of this is much less, promotes the convenience of field work as it could be done in compact places, it does not require more time, units of study can be readily substituted for other units and it is more flexible.

Disadvantages: The cluster sizes may vary and this variation could increase the bias of the resulting sample. The sampling error in this method of sampling is greater and the adjacent units of study tend to have more similar characteristics than do units distantly apart.

Area sampling

This is an important form of cluster sampling. In larger field surveys cluster consisting of specific geographical areas like districts, talluks, villages or blocks in a city are randomly drawn. As the geographical areas are selected as sampling units in such cases, their sampling is called area sampling. It is not a separate method of sampling, but forms part of cluster sampling.

Multi-stage and sub-sampling

In multi-stage sampling method, sampling is carried out in two or more stages. The population is regarded as being composed of a number of second stage units and so forth. That is, at each stage, a sampling unit is a cluster of the sampling units of the subsequent stage. First, a sample of the first stage sampling units is drawn, then from each of the selected first stage sampling unit, a sample of the second stage sampling units is drawn. The procedure continues down to the final sampling units or population elements. Appropriate random sampling method is adopted at each stage. It is appropriate where the population is scattered over a wider geographical area and no frame or list is available for sampling. It is also useful when a survey has to be made within a limited time and cost budget. The major disadvantage is that the procedure of estimating sampling error and cost advantage is complicated. Sub-sampling is a part of multi-stage sampling process. In a multi-stage sampling, the sampling in second and subsequent stage frames is called sub-sampling. Sub-sampling balances the two conflicting effects of clustering i.e., cost and sampling errors.

Random Sampling with Probability Proportional to Size

The procedure of selecting clusters with probability Proportional to size (PPS) is widely used. If one primary cluster has twice as large a population as another, it is give twice the chance of being selected. If the same number of persons is then selected from each of the selected clusters, the overall probability of any person will be the same. Thus PPS is a better method for securing a representative sample of population elements in multi-stage cluster sampling.

Advantages: The advantages are clusters of various sizes get proportionate representation, PPS leads to greater precision than would a simple random sample of clusters and a constant sampling fraction at the second stage, equal-sized samples from each selected primary cluster are convenient for field work.

Disadvantages: PPS cannot be used if the sizes of the primary sampling clusters are not known.

Double Sampling and Multiphase Sampling

Double sampling refers to the subsection of the final sample form a pre-selected larger sample that provided information for improving the final selection. When the procedure is extended to more than two phases of selection, it is then, called multi-phase sampling. This is also known as sequential sampling, as sub-sampling is done from a main sample in phases. Double sampling or multiphase sampling is a compromise solution for a dilemma posed by undesirable extremes. “The statistics based on the sample of ‘n’ can be improved by using ancillary information from a wide base: but this is too costly to obtain from the entire population of N elements. Instead, information is obtained from a larger preliminary sample nL which includes the final sample n.

Replicated or Interpenetrating Sampling

It involves selection of a certain number of sub-samples rather than one full sample from a population. All the sub-samples should be drawn using the same sampling technique and each is a self-contained and adequate sample of the population. Replicated sampling can be used with any basic sampling technique: simple or stratified, single or multi-stage or single or multiphase sampling. It provides a simple means of calculating the sampling error. It is practical. The replicated samples can throw light on variable non-sampling errors. But disadvantage is that it limits the amount of stratification that can be employed.

Non-probability or Non Random Sampling

Non-probability sampling or non-random sampling is not based on the theory of probability. This sampling does not provide a chance of selection to each population element.

Advantages: The only merits of this type of sampling are simplicity, convenience and low cost.

Disadvantages: The demerits are it does not ensure a selection chance to each population unit. The selection probability sample may not be a representative one. The selection probability is unknown. It suffers from sampling bias which will distort results. The reasons for usage of this sampling are when there is no other feasible alternative due to non-availability of a list of population, when the study does not aim at generalizing the findings to the population, when the costs required for probability sampling may be too large, when probability sampling required more time, but the time constraints and the time limit for completing the study do not permit it. It may be classified into: Convenience or Accidental Sampling

It means selecting sample units in a just ‘hit and miss’ fashion E.g., interviewing people whom we happen to meet. This sampling also means selecting whatever sampling units are conveniently available, e.g., a teacher may select students in his class. This method is also known as accidental sampling because the respondents whom the researcher meets accidentally are included in the sample.

Suitability: Though this type of sampling has no status, it may be used for simple purposes such as testing ideas or gaining ideas or rough impression about a subject of interest.

Advantage: It is the cheapest and simplest, it does not require a list of population and it does not require any statistical expertise.

Disadvantage: The disadvantage is that it is highly biased because of researcher’s subjectivity, it is the least reliable sampling method and the findings cannot be generalized.

Purposive (or judgment) sampling

This method means deliberate selection of sample units that conform to some pre-determined criteria. This is also known as judgment sampling. This involves selection of cases which we judge as the most appropriate ones for the given study. It is based on the judgement of the researcher or some expert. It does not aim at securing a cross section of a population. The chance that a particular case be selected for the sample depends on the subjective judgement of the researcher.

Suitability: This is used when what is important is the typicality and specific relevance of the sampling units to the study and not their overall representativeness to the population.

Advantage: It is less costly and more convenient and guarantees inclusion of relevant elements in the sample.

Disadvantage: It is less efficient for generalizing, does not ensure the representativeness, requires more prior extensive information and does not lend itself for using inferential statistics.

Quota sampling

This is a form of convenient sampling involving selection of quota groups of accessible sampling units by traits such as sex, age, social class, etc. it is a method of stratified sampling in which the selection within strata is non-random. It is this Non-random element that constitutes its greatest weakness.

Suitability: It is used in studies like marketing surveys, opinion polls, and readership surveys which do not aim at precision, but to get quickly some crude results. Advantage: It is less costly, takes less time, non need for a list of population, and field work can easily be organized.

Disadvantage: It is impossible to estimate sampling error, strict control if field work is difficult, and subject to a higher degree of classification.

Snow-ball sampling

This is the colourful name for a technique of Building up a list or a sample of a special population by using an initial set of its members as informants. This sampling technique may also be used in socio-metric studies.

Suitability: It is very useful in studying social groups, informal groups in a formal organization, and diffusion of information among professional of various kinds.

Advantage: It is useful for smaller populations for which no frames are readily available.

Disadvantage: The disadvantage is that it does not allow the use of probability statistical methods. It is difficult to apply when the population is large. It does not ensure the inclusion of all the elements in the list.

6. What is a Research Report? What are the contents of Research Report?

Meaning of Research Report

Research report is a means for communicating research experience to others. A research report is a formal statement of the research process and it results. It narrates the problem studied, methods used for studying it and the findings and conclusions of the study.

Contents of the Research Report

The outline of a research report is given below:

I. Prefatory Items

• Title page

• Declaration

• Certificates

• Preface/acknowledgements

• Table of contents

• List of tables

• List of graphs/figures/charts

• Abstract or synopsis

II. Body of the Report

• Introduction

• Theoretical background of the topic

• Statement of the problem

• Review of literature

• The scope of the study

• The objectives of the study

• Hypothesis to be tested

• Definition of the concepts

• Models if any

• Design of the study

• Methodology

• Method of data collection

•Sources of data

• Sampling plan

• Data collection instruments

• Field work

• Data processing and analysis plan

• Overview of the report

• Limitation of the study

• Results: findings and discussions

• Summary, conclusions and recommendations

III. Reference Material

• Bibliography

• Appendix

• Copies of data collection instruments

• Technical details on sampling plan

• Complex tables

• Glossary of new terms used.