Class 10_Sampling Concept

Page 3: Understanding Survey Error

• Total survey error = Random sampling error + Systematic error.

Page 4: Components of Total Error

• Total Error includes:

  • Nonresponse Bias

  • Population Specification Error

  • Sample Bias

  • Respondent Error

  • Measurement Error

  • Processing Error

  • Systematic Errors include Frame Error, Selection Error, Interviewer Error, Response Bias, Instrument Error.

Page 6: Understanding Sample Bias

• Definition of Sample Bias:

  • Tendency for sample results to deviate from the true population parameter.

• Types of Errors causing Sample Bias:

  • Sample selection error

  • Sample frame error

  • Population specification error.

Page 7: Sample Selection Error

• Occurs when respondents self-select into the sample.

• Example: Survey on social media may attract known respondents.

Page 8: Implications of Sample Selection

• Self-selected respondents may differ systematically from non-selected ones.

• Result: Non-representative sample.

Page 10: Sample Frame Error

• Arises from selecting wrong sub-population in sampling.

Page 11: Example of Sample Frame Error

• Using the telephone directory to study financial knowledge.

• Issue: Listed individuals may differ from those not listed.

Page 13: Population Specification Error

• Occurs when a researcher misidentifies the target population.

• Example: Who to survey regarding chocolate consumption in families.

Page 17: Stages of Marketing Research

• Steps include defining problems, planning research design, collecting data, analyzing results, and recommendations.

Page 18: Sampling Process Overview

• Steps to follow:

  1. Define Target Population

  2. Determine Sampling Frame

  3. Select Sample Technique

  4. Determine Sample Size

  5. Obtain Sample

  6. Collect Data.

Page 19: Target Population

• Definition: All cases meeting designated specifics, with examples provided.

Page 20: Sampling Process Steps

• Continued overview of the steps to follow in sampling.

Page 21: Sampling Frame Definition

• Defined as the practical representation of the population.

• Example lists: potential MBA aspirants, college students.

Page 22: Visual Representation

• Diagram showing relationship between population, sampling frame, and sample.

Page 25: Non-Probability Sampling

• Definition: Not every element has an equal opportunity to be selected (results in potential error).

• Example: Only surveying easily reachable individuals.

Page 26: Convenience Sampling

• Defined as using easily available subjects leading to possible sampling frame error.

Page 27: Judgment Sampling

• Definition: Researcher selects subjects based on personal judgment of representativeness.

Page 28: Quota Sampling

• Definition: Sample mirrors population characteristics to some extent

• Example: Ensuring a 50:50 gender ratio in the sample.

Page 29: Quota Sampling Example

• Quotas established for demographic representation.

• Combination of judgmental and convenience sampling.

Page 30: Snowball Sampling Definition

• Participants identify potential candidates for the study

• Often used for hidden populations (e.g., illegal activity research).

Page 31: Advantages and Disadvantages

• Non-Probability sampling pros: speed and cost efficiency.

• Cons: introduces bias and may not represent the population accurately.

Page 33: Simple Random Sample

• Each unit has known equal chance of selection.

• Example of ticket distribution illustrating random selection.

Page 34: Systematic Sample

• Every kth element chosen after a random start.

• Example through systematic interval selection.

Page 35: Stratified Sampling Definition

• Population divided into subsets, followed by random sampling from each subset.

• Example: Selecting a specific number from different class years.

Page 36: Cluster Sampling Definition

• Population grouped into clusters which are then sampled as a whole.

• Example: Randomly picking entire states for study.

Page 39: Key Differences in Sampling Methods

• Stratified Sampling aims for homogeneity; Cluster Sampling for heterogeneity.