Research Design Decisions and be competent in the process of reliable data collection and analysis

RESEARCH DESIGN DECISIONS AND COMPETENT IN THE PROCESS OF RELIABLE DATA COLLECTION AND An Academic presentation by ANALYSIS Dr. Nancy Agens, Head, Technical Operations, Statswork Group

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TODAY'S DISCUSSION Outline of Topics Brief

Data Collection Tools

Data Collection Techniques And How To Choose

Data Analysis

One Effective Survey Questionnaires

Stratified Random Sampling

Brie Research Design may be described f as the researcher’s scheme of outlining

the flow of his project. It is based on research design, that the researcher goes about gathering data to answer his research question. If the idea is to complete a building, then it has to be decided whether it is going to be an apartment, stand-alone house or a shopping complex, who are its occupants? and what are the materials needed? Similarly, in research as well, the researcher chooses his data collection process based on his Research design decision. It enables the researcher to prioritize his work, create better questionnaires and arrive at conclusions with greater clarity.

Table 1 Evaluation Matrix: Matching Data Collection To Key Evaluation Questions Source: Peersman, (2014) Contd..

Table1 shows the type of questions and the data collection methods that were used for the same.

For instace, Key informant interviews and Project records were used for collecting information on the quality of the implementation.

Quantitative research design may be sub-divided into experimental, Quasiexperimental, Survey and Correlational, while, Qualitative research may be divided into Ethnography, Case study, Historical and Narrative.

Broadly, RD can be classified into Exploratory and Conclusive.

Contd..

Exploratory research is a research conducted for a problem that has not been studied more clearly, intended to establish priorities, develop operational definitions and improve the final research design.

Conclusive Research can be classified into descriptive and causal.

Descriptive research tries to answer questions such as what and How? While, Causal research tries to establish the cause-effect relationships among the variables of the research.

Data Collection Techniques And How To Choose One

Using a mix of both Qualitative and Quantitative methods can be most beneficial. The most widely used data collection techniques are Interviews and Questionnaires.

Interviews may be one to one or in groups.

The Questionnaire is developed with the research question in mind.

But it is very difficult to determine if the participant is lying or not. Hence reliability is a problem here.

Effective Survey Questionnaires

Ensure that that the length of the survey questionnaire does not run to more than five minutes.

Avoid complicating the Questionnaire by using questions which may refer to answers of previous questions. For instance, ‘If your answer was yes to Q. No 3 then…’.

Take care to see that the Questions don’t look biased. ‘You would not refer XYZ Baby oil to your friend. Would you?’

Ensure that you keep the Demographics in mind and use uncomplicated words.

Make sure that the questions do not carry conflicting ideas, such as ‘Which is the best and cheapest restaurant in town?’ The best restaurant need not be the cheapest.

Using Data Collection tools such as ‘Device Magic’ which helps you to pre fill form data. .

Data Collection Tools

‘Fulcrum’ allows for custom maps with geo location while ‘Fast Field’ enables exporting to word and pdf.

‘Magpi’ has features for interactive data collection. ‘Zapier’ helps automate the Data Collection process.

Probability and non-probability methods are used in Data Analysis.

DAT ANALYSI S

Probability sampling uses random or semi-random methods to select a sample from among the given population and it uses Statistical generalization with a margin for error as no sample will exactly reflect the population exactly.

Random Sampling uses a simple process where there is equal likelihood of every member from the sample being chosen.

Stratified Random Sampling Stratified Random Sampling uses a method of segregating the sample into mutually exclusive groups and then selecting simple random samples from a stratum.

Example: Strata1: Gender Male Female

Strata2: Income <1 lakh 1 to 2 lakhs 2-5lakhs

Strata3: Occupation Self-employed Clerical Professional

In the above sample we can choose females with income range of 1 to 2 lakhs using simple random sampling.

We are now able to make inferences across these 3 strata.

After stratifying the population, simple random sampling is used to generate the complete sample.

Table 3.

Some Methods Of Numerical AnalysisPri or Source: Peersman, (2014)

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