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3rd/4th Sem Research Methodology Notes

Implications of Sample Design: Understanding the Consequences of Poor Sampling Techniques in Research

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Last updated: 27 February 2023

Sample design is a crucial aspect of conducting high-quality research. It involves selecting a representative subset of the population to participate in the research project. Poor sampling techniques can lead to biased and unreliable research findings. In this article, we will discuss the implications of sample design and the consequences of poor sampling techniques in research.

1. Biased Sample

A biased sample is a sample that does not accurately represent the population. A biased sample can lead to biased research findings, as the sample does not represent the entire population. Biased samples can be caused by non-random sampling techniques or by excluding certain groups from the sample.

2. Sampling Error

Sampling error is the degree to which the sample statistics differ from the population parameters. Sampling error can occur due to the random selection of individuals for the sample. Large samples generally have smaller sampling errors than small samples. The sampling error can be reduced by increasing the sample size.

3. Inadequate Sample Size

Inadequate sample size is a common problem in research. An inadequate sample size can lead to unreliable research findings, as the sample may not be representative of the population. A larger sample size increases the representativeness of the sample and the generalizability of the research findings.

4. Sampling Bias

Sampling bias is a systematic error that occurs when the sample does not accurately represent the population. Sampling bias can be caused by non-random sampling techniques or by excluding certain groups from the sample. Sampling bias can lead to biased research findings and may affect the generalizability of the research findings.

5. Non-Response Bias

Non-response bias occurs when individuals who do not respond to the survey or study are systematically different from those who do respond. Non-response bias can lead to biased research findings, as the sample may not represent the population accurately.

Conclusion

In conclusion, sample design is a crucial aspect of conducting high-quality research. Poor sampling techniques can lead to biased and unreliable research findings. Biased samples, sampling error, inadequate sample size, sampling bias, and non-response bias are common problems in research. By understanding the implications of sample design and the consequences of poor sampling techniques, researchers can select appropriate sampling techniques for their research project and produce high-quality research findings.

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Syllabus – Research Methodology

01 Introduction To Research Methodology

  1. Meaning and objectives of Research
  2. Types of Research
  3. Research Approaches
  4. Significance of Research
  5. Research methods vs Methodology
  6. Research Process
  7. Criteria of Good Research
  8. Problems faced by Researchers
  9. Techniques Involved in defining a problem

02 Research Design

  1. Meaning and Need for Research Design
  2. Features and important concepts relating to research design
  3. Different Research design
  4. Important Experimental Designs

03 Sample Design

  1. Introduction to Sample design
  2. Censure and sample survey
  3. Implications of Sample design
  4. Steps in sampling design
  5. Criteria for selecting a sampling procedure
  6. Characteristics of a good sample design
  7. Different types of Sample design
  8. Measurement Scales
  9. Important scaling Techniques

04 Methods of Data Collection

  1. Introduction
  2. Collection of Primary Data
  3. Collection through Questionnaire and schedule collection of secondary data
  4. Differences in Questionnaire and schedule
  5. Different methods to collect secondary data

05 Data Analysis Interpretation and Presentation Techniques

  1. Introduction
  2. Hypothesis Testing
  3. Basic concepts concerning Hypothesis Testing
  4. Procedure and flow diagram for Hypothesis Testing
  5. Test of Significance
  6. Chi-Square Analysis
  7. Report Presentation Techniques