Questions
- Discuss Questionnaire as a technique of data collection. What are the characteristics of a good questionnaire? 20
- Explain the Probability Sampling strategies with examples. 10
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Model Solutions
1. Discuss Questionnaire as a technique of data collection. What are the characteristics of a good questionnaire? 20
Model Framework
Introduction
- Define Questionnaire: Questionnaire is a set of printed or written questions with a choice of answers, devised for the purposes of a survey or statistical study. It is used primarily as a quantitative data collection tool.
Main Body
- Characteristics of a good questionnaire:
- Unambiguous
- It should not have doubtful questions
- It should be appealing to the target audience
- It should be brief
- Aesthetically appealing
- Mode of administration conducive to the target audience (soft/hard copy)
- Coherent placement of questions
- It should be intriguing
- There shouldn’t be repetition
- Use of questionnaire in sociological research- explain with examples
- Goldthorpe and Lockwood affluent worker study
- Advantages
- Cheapest, fastest and relatively easiest method of quantitative data collection
- High validity and reliability in close ended questionnaire
- Flexibility in data collection
- Highly objective
- Limited need of experts
- Scope for generalisation of data.
- Limitations of using questionnaire for data collection
- Leading questions can influence the response of participant
- Social desirability bias
- Non-response bias
- Poor return of postal questionnaire
- Researchers bias → sequence and questions are decided by him/her based on own values
Note: Advantages and Limitations can be eliminated or explained briefly in 10 marker question, but they must be explained in 20 marker question.
Conclusion
- Hybrid of quantitative methods like questionnaire and quantitative method must be used for sociological research.
- Triangulation Method given by Norman K Denizen
- Socio logic by Micheal Mann
2. Explain the Probability Sampling strategies with examples. 10
Model Structure
Introduction
- Definition of Sampling
- Definition of Probability Sampling
Main Body
- Types of Probability Sampling with slight explanation - simple random, stratified random, random cluster and systematic sampling (You can use figures for explaining these types)
- Examples - Use probability sampling to collect data, even if you collect it from a smaller population.
- Benefits - cost effective, simple and straightforward.
- Limitations - reliability of sample, problem of Quantification.
Steps to conduct probability sampling
- Choose your population of interest carefully and then include them in the sample.
- Determine a suitable sample frame
- Select your sample and start your survey:
Conclusion
- Despite limitations, it provides cost effective and simple investigation of social issues.