1. Sampling Design and Analysis MTH 494 Ossam Chohan Assistant Professor CIIT Abbottabad

2. Review 2

3. 3

4. Lecture-4 4

5. The role of sampling in quantitative research Statistics is at the heart of quantitative research and sampling is a very important part of statistics. There is an old saying “Garbage in garbage out (G.I.G.O.).” For understanding G.I.G.O. in reference to statistics and sampling the reader can think of how a “garbage” sample would yield “garbage” statistical results. 5

6. The role of sampling in quantitative research For many research projects collecting data takes a large portion of the overall time of the project. – After collecting and entering the data using statistical software packages, such as SPSS or Minitab, the statistics can be calculated within minutes. – A very important fact though is that getting an answer and getting the right answer are not the same thing. Most evident when thinking of exams. Think about G.I.G.O. before deciding how to collect the data. 6

7. SAMPLING: REQUIREMENTS OF A GOOD SAMPLE SELECTION BIAS MEASUREMENT BIAS SAMPLING CONTROVERSY QUESTIONNAIRE DESIGN 7

8. REQUIREMENTS OF A GOOD SAMPLE Will reproduce characteristics of interest in the population as closely as possible Representative: each sampled unit will represent the characteristics of a known number of units in the population 8

9. DEFINITION OF TERMS Observation Unit: an object on which a measurement is taken sometimes called an Element Target Population: The complete collection of observations we want to study Sample: a subset of the population Sampled Population: The collection of all possible observation units that might have been chosen in a sample; the population from which a sample was taken

10. DEFINITION OF TERMS Sampling Unit : The unit we actually sample e.g households Sampling Frame: The list of sampling units 10

11. Types and Sources of Errors in Statistical Data

12. 12 Types of Errors In general, there are two types of errors: a.Non-sampling errors and b.Sampling errors. It is important for a researcher to be aware of these errors, in particular non-sampling errors, so that they can be either minimised or eliminated from the data collected.

13. 13 Non-sampling errors – These are errors that arise during the course of all data collection activities. – In summary, they have the following characteristics: exist in both sample surveys and censuses data. difficult to measure.

14. 14 Sources of non-sampling errors Non-sampling errors arise from: defects in the sampling frame. failure to identify the target population. non response. responses given by respondents. data processing and reporting, among others.

15. 15 Defects in the sampling frame This result in coverage errors. These occur when there is an omission, duplication or wrongful inclusion of units in the sampling frame. Omissions are referred to as ‘under coverage’ while duplications and wrongful inclusions are called ‘over coverage’. These errors are caused by defects such as inaccuracy, incompleteness, duplication, inadequacy and out of date sampling frames. Coverage errors may also occur in field operations, that is, when an enumerator misses several households or persons during the interviewing process.

16. 16 Failure to Identify Target Population This occurs when the target population is not clearly defined through the use of imprecise definitions or concepts or when the survey population does not reflect the target population due to an inadequate sampling frame and poor coverage rules.

17. 17 Response They result from the data that have been requested, provided, received or recorded incorrectly. They may occur as a result of inefficiencies with the questionnaire, the interviewer, the respondent or the survey process.

18. 18 a.Poor questionnaire design The content and wording of the questionnaire may be misleading and the layout of the questionnaire may make it difficult to accurately record responses. As a rule, questions in questionnaire should not be loaded, double-barrelled, misleading or ambiguous, and should be directly relevant to the objectives of the survey. It is essential to pilot test questionnaires to identify questionnaire flow and question wording problems, and allow sufficient time for improvements to be made to the questionnaire.

19. 19 Poor questionnaire design – cont’d The questionnaire should then be re-tested to ensure changes made do not introduce other problems.

20. 20 b.Interviewer bias An interviewer may influence the way a respondent answers survey questions. To prevent this, interviewers must be trained to remain neutral throughout the interviewing process and must pay close attention to the way they ask each question.

21. 21 c.Respondent errors These arise through the respondent providing inaccurate or wrong information. They occur because of memory biases or respondents giving inaccurate or false information when they believe that they are protecting their personal interests or integrity. They can also arise from the way the respondent interprets the questionnaire and the wording of the answer that the respondent gives. Careful questionnaire design and effective questionnaire testing can overcome these problems to some extent.

22. d.Problems with the survey process Errors can also occur because of problems with the actual survey process such as using proxy responses, that is, taking answers from someone other than the respondent or lacking control over the survey procedure. 22

23. Non-Response Non-response results when data is not collected from respondents. The proportion of these non-respondents in the sample is called the non-response rate. Non-response can be either total or partial. Total non-response or unit non-response can arise if a respondent cannot be contacted (because the sampling frame is incomplete or out-of-dated) or the respondent is not at home or is unable to respond because of language difficulties or illness or out rightly refuses to answer any questions or the dwelling unit is vacant. Other respondents may indicate that they simply don't have the time to complete the interview or survey form. 23

24. 24 Non-response - cont’d When conducting surveys it is important to document information on why a respondent has not responded. Partial non-response or item non-response can occur when a respondent replies to some but not all questions of the survey. This can arise due to memory problems, inadequate information or an inability to answer a particular question/section of the questionnaire. A respondent may refuse to answer if; a.they find questions particularly sensitive, or if b.they have been asked too many questions.

25. 25 Non-response - cont’d To reduce non-response, the following approaches can be used: – care should be taken in questionnaire design through the use of simple questions. – pilot testing of the questionnaire. – explaining survey purposes and uses. – assuring confidentiality of responses. – public awareness activities including discussions with key organisations and interest groups, news releases, media interview and articles.

26. 26 Processing These occur at various stages of data processing such as data cleaning, data capture and editing. Data cleaning involves taking preliminary checks before entering the data onto the processing system. Coder bias is usually a result of poor training or incomplete instructions, variability in coder performance and data entry errors.

27. 27 Processing – cont’d Inadequate checking and quality management at this stage can introduce data loss (where data is not entered into the system) and data duplication (where the same data is entered into the system more than once) thus introducing errors in data. To minimise these errors, processing staff should be given adequate training, instructions and realistic workloads.

28. 28 Time Period Bias This occurs when a survey is conducted during an unrepresentative time period. Survey timing is thus important and failure to recognise this introduces errors in data.