Defining the Research Problem

Research Methodology Lecture 2

Defining the Research problem is the first step of Research Methodology or Research process:

Defining Formulating Designing Collecting Data research problemHypothesis sampling Analyzing Data technique Report Writing

Research problem arises only when:

• There must be group/individuals facing the problem to be researched. • There must be some objectives to be achieved from the solution of the problems • There may be alternative means of obtaining the objectives • Researcher must have some doubts about the relative efficacy of the alternatives

Points to be observed in selecting a Research Problem

• Subject on which research has been done should not be chosen • Controversial issues should be avoided • Narrow or too wide issues should be avoided • Research problem selected should be feasible within means available • Researcher should have some background information on the research problems Techniques of defining Research problem

1. Statement of the Problem in a general way 2. Deep understanding the nature of the problem 3. Surveying the available Literature 4. Developing the idea further through discussion 5. Finally, Rephrasing the Research Problem

Techniques Involved in Defining a Research Problem

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 1 of 17 Statement of the Problem First of all the problem should be stated in a general way

Understanding the nature of the problem

The researcher should be thoroughly knowlegible in the subject

The researcher should first discuss the problem with those who first raised the issue/ problem.

The researcher than should discuss the issue with the resource persons excelling in the subject

Surveying the available Literature

All available research concerning the problem at hand must necessarily be surveyed and examined before formulating the research problem.

This means, the researcher must be well conversant with available reports, records and literature.

Developing the idea through discussion

Discussion concerning a problem often produces useful information. People with rich experience are in a position to enlighten the researcher on different aspects of the proposal. It helps sharpen the focus on specific aspects of the research.

Rephrasing the Research Problem

Finally the researcher must rephrase the research problem into a working proposition. Once the nature of the problem has been clearly understood, literature has been reviewed, discussion over the problem has taken place, this rephrasing the research problem into analytical / operational terms become relatively easy.

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 2 of 17 Additional Points

• Technical terms and phrases with special meanings should be clearly defined for general readership. • Basic assumptions relating to the research problem should be clearly stated. • Aim or value of the research should be stated. • The suitability of the time period and the source of data availability should be considered. • The scope of investigation or the limits within which the problem will be studied need to be mentioned.

Example of a too broad /non-specific topic:

Why is labour productivity lower in Bangladesh compared to Vietnam

• Vague in terms of which sector • Vague in terms of time frame • Non-analytical – labour productivity depends on certain factors –

• Rephrasing:

• Factors responsible for productivity differentials in Bangladesh and Vietnam’s RMG sectors between 2005-10.

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 3 of 17 Research Design

• Research design is “decisions” regarding what, where, when, how much, by what means etc.

• It is management of conditions for collection of data, analysis of data and report preparation on the research problem.

Meaning of Research design

1. What is the study about? 2. Why the study is being undertaken? 3. Where will the study be carried out? 4. Where can the required data be found? 5. What will be the sample design 6. What period of time the study will include? 7. What type of data is required? 8. What techniques of data collection will be used? 9. How many items will be observed? 10. How will the data be analyzed? 11. In what format, the report will be prepared within given time and budget?

Components of Research Design

• From last two slides, it follows that Research Design has five components: 1. Problem formulation and objective 2. Sampling design: design which deals with method of selecting items to be observed in the given study. (3-6) 3. Observational design: design which relates to the conditions under which the observations are to be made on the selected items. (7- 8) 4. Statistical design: design that deals with how many items will be observed and how information collected will be analyzed. (9 - 10) 5. Operational design: design which deals with the techniques by which procedures specified in the sampling, observational and statistical designs can be carried out. Within given cost and time (11)

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 4 of 17 Research design must have:

1. Statement of the problem and objectives 2. Sources of information to be collected (Sampling designs) 3. Types of information to be collected (Observational designs) 4. Approach to be used for collecting and analyzing data (Statistical designs) 5. Estimates of time and cost for the research (Operational designs)

In Summary, Research design must have

• Clear Statement of the Research Problem and the objectives of research • Sampling design • Observational design • Statistical design • Operational design

Important Concepts Relating to Research Design

• Dependent and Independent Variables • Extraneous variable • Control variable • Research hypothesis • Testing significance of the result on Research hypothesis

Dependent and Independent variable

• Variable – A concept/ entity that can take different quantitative values is called a variable • Continuous vs. discrete variable • Example:

• Individual’s earning depends on his/her knowledge and skill

• Here knowledge and skill is independent variable • Individual’s earning is dependent variable

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 5 of 17 Extraneous variable

• Variables not related to the study but affect the dependent variable. • Example - Measuring Dependency of Rice yield to fertilizer doze in different districts.

• But soil types of different district would affect yield- Soil types are extraneous variables.

Control Variable

• Control variables are used to overcome the effects of extraneous variables.

• Example: BRRI’s sub-stations in different districts have experimental stations with normal soil types and temperature – These are used as control fields.

Research Hypothesis

• When a prediction or a hypothesized relationship is tested by scientific methods, it is termed as research hypothesis.

• The opposite of Research hypothesis is known as null hypothesis.

Example of Research and Null Hypothesis

Research Hypothesis: Paddy yield depends positively on fertilizer applied.

Null Hypothesis: Paddy yield has no relationship at all with fertilizer applied

Testing Significance of the Result

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 6 of 17 • Why done? • Because a sample is taken rather than whole population, therefore there is a need to test significance or confidence on the result.

Testing Significance on the Result

• How it is done? • Sample is said to be replica of the population. • Mean Height in cm

Population’s distribution

Sample’s distribution

2.5% Mean Height in cm Testing Significance on the Result

If sample mean is known and standard deviation is known, then 95% confidence interval can be calculated.

Stages of Research Design

• Problem formulation and objective framing • Sampling design • Observational design • Statistical design • Operational design

Formulating Research Problem and Objectives to be attained

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 7 of 17 • Survey of Literature

• Discussion with persons affected by the problem, Resource persons

Sampling Design : Relevant Terminology

• Universe / Population –entire research area

• Census – survey of entire population

• Sample Survey – surveying a part of the population • Sampling Frame - List of population from which sample will be drawn

Criteria of Sampling:

Aim should be to avoid Systematic bias.

Systematic Bias occurs when: • Inappropriate sampling Frame • Defecting Measuring device • Non-respondents • Indeterminacy Principle • Natural bias

Sampling Errors

• Sampling Errors is the difference between the sample estimate and the true population parameter.

• The sampling error can be found by subtracting the value of a parameter from the value of a statistic.

• Example : Sample height – Population Height of individuals

• Sampling error depends on sampling design.

Different Types of Sample Design

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 8 of 17 • There are basically 2 types of Sampling: • Probability sampling and Non-probability sampling:

• Probability sampling Random Sampling-

• EACH SAMPLING UNIT HAS EQUAL PROBABILITY

Non-probability sampling non-random sampling-

EACH SAMPLING UNIT HAS UNEQUAL PROBABILITY,

Unrestricted vs. restricted sampling

• When each sample element is drawn individually and directly from the population at large, then sample drawn is known as un-restricted sample.

CHART SHOWING BASIC SAMPLING DESIGNS

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 9 of 17 Representation Basis Element Probability Non-probability Selection Sampling Sampling Technique Unrestricted Simple Random Haphazard Sampling sampling/ Convenience sampling Restricted Purposive Sampling Stratified sampling (Researcher’s Sampling (Researcher’s individual individual judgment (Researcher’s judgment involved) individual involved) judgment involved)

Probability Sampling

• Known also as Random / chance sampling • Here every item of the universe has an equal chance of inclusion in the sample. • All possible samples have equal chance of inclusion

• Therefore sample has the same characteristics of the population- replica of the population.

• Errors of estimation or significance of the results can be measured.

Research Methodology Lecture 5 Procedure of selecting a random sample

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 10 of 17 • Suppose we have to randomly select 3 people from the class of 60 students. • ------• 1-60 numbers corresponding to the students are written in 60 pieces of paper • These are folded so that numbers are not seen • The paper slips are thoroughly mixed

Procedure of selecting a random sample • Then 1 piece of paper is selected without seeing the numbers written. The number is returned to the pool. • Then 2nd piece of paper is chosen and then the 3rd paper. If same no. is chosen, process is repeated. • Suppose 34, 03, 58 numbers/students are chosen • Each number and each possible sample (such as 01, 60, 45, or 43, 06,55 has equal chance of selection) (1/60 x 1/60 x 1/60)

Random Table 47 91 82 28 81 95 70 89 73 48

10 4 41 40 86 27 46 80 20 58

24 34 43 50 12 33 90 3 96 38 97 11 63 21 55 99 68 87 29 78 52 31 71 1 39 62 49 16 88 66 75 15 22 74 59 26 85 100 25 76 35 2 93 14 92 13 36 54 72 37 23 83 32 64 79 45 5 61 17 77 8 44 7 19 30 60 94 56 98 67 53 9 51 69 42 84 18 65 6 57

Systematic sample: mix of random and non-random sampling

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 11 of 17 • Supposing, we have to chose 4 students from 100 students. • First a number from the random table is selected • Then 25 is added to the number to select the next number. If total number exceeds 100 then move to the beginning.

Example Initial chosen number and then 25 is added successively.

18 43 68 93 80 05 30 55

Advantage and disadvantage of Systematic Sampling

Advantage • Spread over evenly over the entire population compared to random sample • Easier and less costlier method Disadvantage If any systematic bias on the ith item (e.g.., 25th item), it persists. Otherwise, systematic sample is considered equivalent to random sample,

Random Stratified Sampling

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 12 of 17 • Stratified sampling is used when the population is not homogenous.

• Under Stratified sampling, more pertinent information about the different homogenous stratum is obtained and • therefore better information for the whole population is obtained.

• However, various strata are to be formed in a way so as to ensure that elements are most homogenous within strata and most heterogeneous between different strata.

• Otherwise no advantage from stratification

• Here, Strata are purposively formed and involves past experience and personal judgment.

• Once strata are selected, selection of unit must be done on random basis • For better result, sampling units taken from each strata should be proportion to the size of the strata.

Cluster sampling

• If total area of interest is large enough, area can be divided into a number of non-overlapping areas and then to select a number of smaller areas called clusters.

• The samples are units in these small areas or clusters.

Example

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 13 of 17 • Salinity prone areas subdivided into different unions can be clusters.

• Farmers belonging to the union can be sampling units who could be interviewed.

Multi stage sampling- Two stage Sampling

• Suppose we want to measure efficiency of Nationalized Commercial Banks (NCBs) of Bangladesh:

• First stage is to randomly select some 2/3 divisions.

• Second we can select some districts randomly and interview all bank managers of nationalized banks in these districts. • This is two stage sampling. • Divisions • Districts • Sampling units are bank branches

Multi stage sampling -Three stage Sampling

• If instead of interviewing all bank managers in the districts, we go down one step and randomly select some towns in these districts. We then interview all bank managers of the towns. • Then this is a three-stage sampling.

• Divisions • Districts • Towns Sampling units are bank branches

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 14 of 17 Multi-stage Sampling with probability proportional to size

• Here Probability of inclusion of a cluster/ town is proportional to its size (in terms of bank branches).

Example: Taking a sample of 10 branches from a total of 500 branches spread over 15 towns so that probability of selecting a town is proportional to size of the town (measured in terms of branches)

• Number of bank branches in 15 towns are as follows:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

35 17 10 32 70 28 26 19 26 66 37 44 33 29 28

• Table

Please see Slide 86

Comparison with Alternative sample design: Simple Random Sample

Randomly choosing 10 branches from 500 branches

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 15 of 17 No assurance of rightly covering all divisions, districts, and towns.

Bigger towns might have lower samples. Complex/ Stratified sample is more justified.

Non-Probability Sampling

• Non probability sampling is one where there is no guarantee that sampling element has equal probability of being selected. • It is a kind of deliberate sampling.

• It is also known as purposive sampling. • Example: For examining extreme poverty level of draught prone area, some unions of Rangpur is purposively chosen and compared with a normal union of another district.

Limitation of Non-probability/ Purposive Sampling

• Researcher can purposively choose an area which best suits his point of view. • Element of human bias is always there • Used in small scale research. • Rarely used in large scale research

Conclusion

• Direct/ Simple Random sampling should be attempted as it has lowest bias and more importantly significance of the result/ confidence on the result can be estimated.

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 16 of 17 • If known characteristic of the population is known before hand and if random sampling is difficult and researcher has no bias towards a particular result then purposive sampling is suitable.

Please see Slide No. 92 to 101 for different Group Assignment

D:\Docs\2018-04-04\06c3322774b4181ad9b2a34e1fa5362a.doc Page 17 of 17