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

1. RESEARCH DESIGN DECISIONS AND COMPETENT IN THE PROCESS OF RELIABLE DATA COLLECTION ANDANALYSIS An Academic presentationby Dr. Nancy Agens, Head, Technical Operations, Statswork Group www.statswork.com Email:info@statswork.com

2. TODAY'SDISCUSSION Outline ofTopics Brief Data Collection Techniques And How To Choose One Effective SurveyQuestionnaires Data CollectionTools DataAnalysis Stratified RandomSampling

3. Brief Research Designmay be described as the researcher’s scheme of outlining the flow ofhisproject. It is based on research design, that the researchergoes about gathering data to answer hisresearchquestion. 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 materialsneeded? 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.

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

5. Table1 shows the type of questions and the data collectionmethods that were used for thesame. For instace, Key informant interviews and Project records were used for collecting information on the quality of theimplementation. Quantitative research design may be sub-divided into experimental, Quasi- experimental, Survey and Correlational, while, Qualitative research may be divided into Ethnography, Case study, Historical andNarrative. Broadly, RD can be classified into Exploratory andConclusive. Contd..

6. Exploratoryresearchis a research conducted for a problem that has not been studied more clearly, intended to establish priorities, develop operational definitions and improve the final researchdesign. Conclusive Research can be classified into descriptive andcausal. 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 theresearch.

7. Data Collection Techniques And How To ChooseOne Using a mix of both Qualitative andQuantitative methodscan be most beneficial.The most widely used data collection techniques are Interviews andQuestionnaires. Interviews may be one to one or ingroups. The Questionnaire is developed with the research question inmind. But it is very difficult to determine if the participant is lying or not. Hence reliability is a problemhere.

8. Effective SurveyQuestionnaires Ensure that that the length of the survey questionnaire does not run to more than fiveminutes. Avoid complicating the Questionnaire by using questions which may refer to answersof previous questions. For instance, ‘If your answer was yes to Q. No 3then…’. Take care to see that the Questions don’t look biased. ‘You would not refer XYZ Baby oilto your friend. Wouldyou?’ Ensure that you keep the Demographics in mind and use uncomplicatedwords. Make sure that the questions do not carry conflicting ideas, such as ‘Which is the bestand cheapest restaurant in town?’ The best restaurant need not be thecheapest.

9. Using Data Collection tools such as ‘Device Magic’ which helps you to pre fill form data.. ‘Fulcrum’ allows for custom maps with geo location while ‘Fast Field’ enables exporting to word andpdf. ‘Magpi’ has features for interactive data collection. ‘Zapier’ helps automate the Data Collectionprocess. Data CollectionTools

10. Probability and non-probability methods are used in DataAnalysis. 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 populationexactly. DATA ANALYSIS Random Sampling uses a simple process where there is equal likelihood of every member from the sample beingchosen.

11. Stratified RandomSampling Stratified Random Sampling uses a method of segregating the sample into mutuallyexclusive groups and then selecting simple random samples from astratum. Example: In the above sample we can choose females with income range of 1 to 2 lakhs usingsimple randomsampling. We are now able to make inferences across these 3strata. After stratifying the population, simple random sampling is used to generate the completesample.

12. Table 3.Some Methods Of Numerical AnalysisPrior Source: Peersman,(2014)

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