Sampling

1. Dr S S N Raju Indukoori

2. Larger when
1) Dispersion is larger
2) Desired precision is high
3) Error is narrow
4) Confidence level is high
5) More number of sub groups

3. 1. Standard error
2. Confidence interval
3. Central limit theorem

4. 1) Random Sampling Error
2) Systematic/Non Random Sampling error

5. • Arise due to chance variation
• Can be minimized by increasing the
sample size

6. • Arise due to chance variation
• Can be minimized by increasing the
sample size

7. 1.Response Bias: Deliberate Falsification,
a. Unconscious misrepresentation
i. Acquiescence bias : Agreeable
ii. Extremity Bias : No extremes, only balance.
iii. Interviewer Bias : Influenced by Interviewers Presence
iv.Auspices Bias : Occasion, gathering.
b. Social Desirability Bias
2.Non Response Error: Refusal, No contact

8. 1.Data Processing
2.Sample Selection Error
3.Interviewer Error
4.Interviewer Cheating

9. 1.Classifying survey research methods
a. Structured disguised questions
b. Classifying surveys on a temporal basis
 Cross sectional
 Longitudinal
2.TQM
a. Quality Definition
b. Classifying internal and external customers
c. Implementing TQM

10. 1) Accuracy / Unbiased
2) Precise

11.  It is the degree to which the bias is absent.
 Bias is the variation in measure of some sample
elements
• compared to other elements or population is less: Under
estimation
• compared to population is more: Over estimation
 Increase in sample size may decrease systematic
variance as under estimation and over estimation will
offset each other.

12.  Extent of population representation by sampling.
 It is represented by sampling error / random sampling
error
 Sampling error reflects the influence of chance in drawing
the sample members.
 Sampling error is what is left after all known sources of
systematic variance have been accounted for
 Precision is measured by standard error of estimate or of
mean or of proportion.