1. Dr. Unnikrishnan P.C. Professor, EEE
Research Methodology
Dr. Unnikrishnan P.C.
Professor, EEE

2. Dr. Unnikrishnan P.C.
BTech. : EEE, NSS College of Engineering,
MTech: Control & Instrumentation, IIT Bombay,
PhD. : EEE, Karpagam University, Coimbatore,

3. Dr. Unnikrishnan P.C.
: Assistant Professor and Associate Professor, Rajasthan Technical University, Kota, India
: Assistant Professor, Academic Coordinator, Registrar, Head of Section and Head of the Department at Colleges of Technology, Ministry of Manpower, Muscat, Sultanate of Oman.
Till Date : Professor, EEE, RSET

4. Module I
Research Design

5. Research Process
Step IV. Design Research

6. Step IV. Design Research
The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that your research problem determines the type of design you should use, not the other way around!

7. IV.(a) Preparing the research design:
Research design is the conceptual structure within which research would be conducted.
The function of research design is to provide complete guidelines for the collection of relevant information with minimal expenditure of time, effort and money.
The following points are to be considered while preparing a research design for a particular research problem-
Objectives of the research study
Method of data collection to be adopted
Source of information (Sample design)
Tool for data collection
Data analysis (Quantitative and qualitative)

8. IV(b). Determining sample design
A sample is segment of the population selected to represent the population as a whole.
Designing the sample calls for 3 decisions
The sample (who will be surveyed)
Sample size (how many people will be surveyed)
Sampling (how should the sample be chosen)
At random (probability sample)
Select people who are easier (non-probability sample)
The needs of the research project will determine which method is most effective..

9. sample design cont.
Samples can be either probability samples or non-probability samples.
Probability samples are those based on simple random sampling, systematic sampling, stratified sampling, cluster/area sampling
non-probability samples are those based on convenience sampling, judgement sampling and quota sampling techniques.

10. Different Research Designs
Research Design in Case of Exploratory Research Studies
Research Design in Case of Descriptive and Diagnostic Research Studies
Research Design in Case of Hypothesis Testing Research Studies

11. Research Design in Case of Exploratory Research Studies
The major emphasis in this research is on the discovery of ideas & insights
Three methods
The survey of concerning literature: Review hypothesis stated by earlier workers and deriving relevant hypothesis
The experience survey: Survey people who have had practical experience with the problem

12. Research Design in Case of Exploratory Research Studies……
c. Analysis of ‘insight-simulating’ examples Suitable in areas where there is little experience available One can mention few examples such as reaction of strangers or marginal individuals, reaction of individual from different social strata

13. Research Design in Case of Descriptive and Diagnostic Research Studies
Descriptive: Describing the characteristics of an individual or group Diagnostic: Frequency with which something occurs with something else
Formulate the objective
Design the method of data collection
Selecting the sample
Collecting the Data
Processing and analyzing Data
Reporting the findings

14. Research Design in Case of Hypothesis Testing Research Studies
Generally known as experimental studies where researcher tests the casual relationships between variables. So the design research means design of experiments. Experimental design originated from agriculture operations, we still use (in a technical sense) several terms in agriculture (such as treatment, yield, plot, block etc.)

15. Important Experimental Designs
Informal experimental design
Before and after without control design
After only with control design
Before & After with control Design
b. Formal experimental design
Completely Random Design (C.R. Design)
Randomized Block Design (R.B. Design)
Latin Square Design (L.S. Design)
Factorial Designs

16. Before and after without control design
Test Area:
𝐿𝑒𝑣𝑒𝑙 𝑜𝑓 𝑃ℎ𝑒𝑛𝑜𝑚𝑒𝑛𝑜𝑛 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐼𝑛𝑡𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝐿𝑒𝑣𝑒𝑙 𝑜𝑓 𝑃ℎ𝑒𝑛𝑜𝑚𝑒𝑛𝑜𝑛
𝑏𝑒𝑓𝑜𝑟𝑒 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 (𝑋) after 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 (𝑌)
Treatment Effect = (Y) – (X)

17. After only with control design
𝑇𝑒𝑠𝑡 𝐴𝑟𝑒𝑎 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐼𝑛𝑡𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝐿𝑒𝑣𝑒𝑙 𝑜𝑓 𝑃ℎ𝑒𝑛𝑜𝑚𝑒𝑛𝑜𝑛 after 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 (𝑌) 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝐴𝑟𝑒𝑎 𝐿𝑒𝑣𝑒𝑙 𝑜𝑓 𝑃ℎ𝑒𝑛𝑜𝑚𝑒𝑛𝑜𝑛 without 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 (𝑍) Treatment Effect = (Y) – (Z)

18. Before and After with control design
Test Area 𝐿𝑒𝑣𝑒𝑙 𝑜𝑓 𝑃ℎ𝑒𝑛𝑜𝑚𝑒𝑛𝑜𝑛 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐼𝑛𝑡𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝐿𝑒𝑣𝑒𝑙 𝑜𝑓 𝑃ℎ𝑒𝑛𝑜𝑚𝑒𝑛𝑜𝑛 𝑏𝑒𝑓𝑜𝑟𝑒 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 (𝑋) after 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 (𝑌) Control Area 𝐿𝑒𝑣𝑒𝑙 𝑜𝑓 𝑃ℎ𝑒𝑛𝑜𝑚𝑒𝑛𝑜𝑛 𝐿𝑒𝑣𝑒𝑙 𝑜𝑓 𝑃ℎ𝑒𝑛𝑜𝑚𝑒𝑛𝑜𝑛 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 (𝐴) without 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 (𝑍) Treatment Effect = (Y - X) – (Z- A)

19. Completely Randomized Design (C.R. Design)
The Completely Randomized (CR) design randomly divides the experimental units into t groups of size n and randomly assigns a treatment to each group.
Completely Randomized (CR) designs are used whenever the experimental units are homogeneous.
Limitations: Individual differences among those conducting the treatments are not eliminated

20. (C.R. Design)
Eg: Two group of students, one going for normal tuitions and another for specialized coaching classes
This design is quite common in studies concerning behavioral sciences

21. Randomized Block Design (R.B. Design)
Randomized Block Design differ from Completely
randomized designs in that the experimental units are grouped into blocks according to known or Suspected variation which is isolated by the blocks. Eg: In agricultural data variation such as fertility, sand, and wind gradients, or age and litter of animals can be isolated by appropriate blocking

22. R.B.Design……
Therefore, within each block, the conditions are as homogeneous as possible, but between blocks, large differences may exist.

23. Advantages of R.B.Design
1. Complete flexibility. Can have any number of treatments and blocks. 2. Provides more accurate results than the completely randomized design due to grouping. 3. Relatively easy statistical analysis even with missing data. 4. Allows calculation of unbiased error for specific treatments.

24. Disadvantages of R.B.Design
1. Not suitable for large numbers of treatments because blocks become too large.
2. Not suitable when complete block contains considerable variability.
3. Interactions between block and treatment effects increase error.

25. R.B.Design ….
The randomized complete block design is one of the most widely used designs. If it will control the variation in a particular experiment, there is no need to use a more complex design.

26. Latin Square Design (L.S.Design)
Frequently used in agricultural research
Eg:- An expt. Has to be made through with the effect of 5 different varieties of fertilizers on the yield of a certain crop is to be judged
Here two major extraneous factors (Blocking Factors) such as the varying soil fertility and varying seed in different blocks must be taken into consideration.

27. L.S. Design …….
Fertility Level
𝑿 𝟏 A B C D E
𝑿 𝟐
𝑿 𝟑
𝑿 𝟒
𝑿 𝟓
Seed Differences
In LSD, each fertilizer appear 5 times but is used only once in each row and each column. The two blocking factors are represented through rows and columns.
Advantage of LSD is that it enables differences in blocking factors to be eliminated in comparison to the effects of different varieties of fertilizers on the yield of the crop.

28. Disadvantages:
Interaction between treatment and blocking factors create considerable differences.

29. What is Sampling ?
Population (N) – A group that includes all the cases (individuals, objects, or groups) in which the researcher is interested. Sample (n)– A relatively small subset from a population. (n<N) Sampling Technique- Process of Sample Selection is called Sampling Technique

30. Census Vs sample Studies
In a sample survey, since we study only a subpart of the whole population, requires less money and less time. Considering Non-sampling errors the sample survey are much more accurate than those of census survey. In the case of Census survey sampling errors are absent. But it consumes more time and money. If population is not so large, a Census survey may provide better results than any sample survey.

31. Sampling errors
Sampling errors arise due to the fact that only a part of the population has been used to estimate population parameters and to draw inference about population. Sampling error can be measured for a given sample design and size. If we increase the sample size the precision can be improved. But increasing size of the sample has its own limitations.

32. Non-sampling errors
Non-sampling errors arise at the stage of collection and preparation of data and thus are present in both the sample survey as well as the census survey. Non-sampling errors can be reduced by defining the sampling units, frame and the population correctly and by employing efficient people in the investigations.

33. Sample Design
A sample design is a definite plan for obtaining a sample from a given population. Main steps of Sample Design 1. Objective 2. Population 3. Sampling Units and frames 4. Size of sample 5. Parameters of Interest 6. Data Collection 7. Non-respondents 8. Selection of proper sampling design 9. Organizing field work 10. Pilot Survey 11. Budgetary Constraints

34. Types of Sampling Designs
Non-Probability Sampling
(i) Convenience sampling (ii) Judgement sampling
(iii) Quota sampling techniques
2. Probability Sampling
(i) Simple Random Sampling
3. Complex Random Sampling
(i) Systematic Sampling (ii) Stratified Sampling
(iii) Cluster Sampling (iv) Multi-stage Sampling
(v) Sampling with Probability Proportional to Size
(Vi) Sequential Sampling

35. Systematic Sampling
In systematic sampling only the first unit is selected randomly and the remaining units of the sample are selected at fixed intervals. Systematic sampling is the most practical way of sampling is to be select every ith item on a list.

36. Systematic Sampling

37. Stratified sampling: the researcher divides the population into separate groups, called strata. Then, a probability sample (often a simple random sample ) is drawn from each group
Cluster sampling : used when heterogeneous groupings are evident in a population. It is often used in marketing research. In this technique, the total population is divided into these groups (or clusters) and a simple random sample of the groups is selected

38. STRATIFIED SAMPLING
Stratification: The elements in the population are divided into layers/groups/ strata based on their values on one/several auxiliary variables. The strata must be non-overlapping and together constitute the whole population.

39. Ex. Stratification of individuals by age group
Stratum
Age group 1
17 or younger 2
18-24 3
25-34 4
35-44 5
45-54 6
55-64 7
65 or older

40. Ex. Regional
stratification

41. Ex. Regional
stratification

42. Disproportionate Stratified Sample

43. WHY STRATIFY? Gain in precision Practical reasons
Administrative reasons

44. Simple random sampling (SRS) not always appropriate!
Cluster Sampling
Simple random sampling (SRS) not always appropriate!
Example
Population of N=324 households
Households arranged into 36 “villages” of 9 households each
Costly to travel between villages
Cheap to travel between households in a village
Taking a SRS of n=27 households is a “costly” strategy

45. Cluster sampling Example (cont.)
Each village is a primary sampling unit (PSU)
Each household in a village is a secondary sampling unit (SSU)
Take a sample of villages
Sample all households within the selected villages
This is one-stage cluster sampling.

46. Illustration: Estimation
Cluster sampling: 3 villages out of 36 selected using SRS.
Income from sale of goods recorded for each household, and totalled up for village.
230
360
180
Estimates: Mean village income is
Total income for area = x 36 = 9240

47. In practice…
Units in a cluster tend to be more similar to each other and different to units in other clusters
Cluster sampling often leads to less precise estimates than SRS
(opposite concept to stratification)
Trade-off between convenience and precision:
If cluster sampling cheap to do, could take larger sample to help improve precision.

48. Sampling Distribution
Suppose that we draw all possible samples of size n from a given population and we compute a statistic (e.g., a mean, proportion, standard deviation) for each sample. The probability distribution of this statistic is called a sampling distribution.

49. Sampling Distribution
Sampling distribution of the mean – A theoretical probability distribution of sample means that would be obtained by drawing from the population all possible samples of the same size.

50. Central Limit Theorem
No matter what we are measuring, the distribution of any measure across all possible samples we could take approximates a normal distribution, as long as the number of cases in each sample is about 30 or larger.
If we repeatedly drew samples from a population and calculated the mean of a variable or a percentage or, those sample means or percentages would be normally distributed.