Research Process: Defining, Obtaining, Analyzing, and Communicating Results

1. Welcome

2. Tutorials PLEASE: • Turn off your cell phone • Be on time for the class • Attend regularly • Come prepared

3. Dates of tutorials

4. The research process

5. The research process STAGE 1: DEFINING THE PROBLEM • Deciding on the research topic • Conducting a literature review • Specify a research question • Formulating a hypothesis • Operationalizing concepts

6. The research process STAGE 2: Obtaining the information • Ethics • Research design • Sampling • Data collection STAGE 3: Analysing and interpreting the information • Describing and interpreting quantitative data • Analyses and interpretation of qualitative data

7. The research process STAGE 4: Communicating the results • Report

8. Sampling

9. Outcomes • Key terms relating to sampling • Reasons for sampling • Probabilility and nonprobability sampling • Techniques in probability sampling • Techniques in nonprobability sampling • Factors that influence the sample size • Drawing a sample in practice

10. Basic sampling concepts • Sampling • Method (process) of selecting certain members to represent the whole group • Obtaining info from a few cases and then drawing conclusions from that to the population 2. Population • The entire group of persons (or set of objects / events) of interest to the researcher • Also called target population eg single parents

11. Basic sampling concepts • Researcher should describe the population in terms of criteria: • Eligibility, inclusion criteria, distinguishing descriptors eg readers must be woman and they must be frequent readers of that specific women’s magazine • Another way of clearly defining a population is through an operational definition of the population for eg family violence • Accessible population or study population – the population the researcher has access to

12. Basic sampling concepts 2. Element / unit of analysis • Is a case or unit from a defined population about which information is obtained • People, objects, events or social groups • All elements together constitute the population 3. Parameter • Is a characteristic of the elements of a population eg the age of the students being researched • Describes a particular characteristic of the whole population

13. Basic sampling concepts 4. Sample • Is a subset of a population 5. Sampling frame • A comprehensive list of all units (elements)from which the sample is drawn 6. A representative sample • A sample that resembles the population • Which enables the researcher to accurately generalize the results

14. Basic sampling concepts 7. Sampling error • Refers to the differences between populationparameters (eg the average age of the population) and sample statistics (the average age of the sample)

15. Basic sampling concepts • Sampling error May occur because of: • Chance factors – one element may have been included rather than another • Bias in selection – faulty technique • Non-response errors – when an element of the population does not respond to a measurement instrument

16. Basic sampling concepts • Sampling bias • Is when a sample is not representative of the population • Is caused by an incorrect selection process • Is an over or underrepresentation of a segment of the population which will then impact on validity of study • Is a threat to external validity when the subjects are not randomly selected from the population

17. 2 Important factors in sampling To draw a representative sample, the following is nb • Similarity of population: how similar or dissimilar is the population? • The degree of precision with which the population is specified

18. 2 Important factors in sampling To draw a representative sample, the following is nb • Similarity of population • Heterogeneous population - consists of people who are dissimilar to each other • Homogeneous population – consists of people who are similar to each other eg a group of female junior tennis players who play in the same league and for the same province • The more alike the elements of the population, the smaller the sample can be and still be representative

19. 2 Important factors in sampling To draw a representative sample, the following is nb 2. Defining of population • The degree of precision with which the population is specified • The sampling frame is the defined population from which the sample is drawn • Eg all indv currently on ARV treatment for HIV

20. Sampling approaches • Probability or random sampling • Nonprobability sampling

21. Probability / random sampling • Each person has an equal chance of being selected for the sample • Also called random sampling – the selection of elements is based on some form of random procedure • Forms of probability / random sampling techniques: • Simple random sampling • Systematic sampling • Stratified random sampling • Cluster sampling

22. Simple random sampling • Each element has an equal chance of being in the study • To prevent sample bias • Steps in simple random sampling • First the population is defined • Then the sample frame is drawn up • Each element of the sample frame then has an equal change of being included in the sample 4. Random selection techniques • Lottery or fishbowl techniques • A random table number – use a table of random numbers to select the subjects

23. Systematic sampling • Also called interval sampling • It is when a sampling frame is available • Involves drawing every fthelement from a population • Elements are selected at equal intervals eg every 6th, 10th or 19th element

24. Systematic sampling • Procedure: • Obtain a list of the total population – N: is the symbol for the size of the population. • Determine the sample size (n) • Determine the sampling interval (k) by dividing the size of the population by the size of the sample. • Choose a random starting point between 1 and k. • p158

25. Stratified random sampling • The population is divided into different groups or subgroups called strata, so that each element of the population belongs to one and only one stratum • Random sampling is then done within each stratum, using either simple random sampling or systematic sampling.

26. Stratified random sampling • Two ways of determining the number of elements sampled from each stratum • Proportional stratified samples • The number of elements selected from each stratum is proportional to the size of the stratum in the population 3. Disproportional stratified sampling • The number of elements selected from each stratum is not proportional to the size of the stratum in the population

27. Cluster sampling • Requires that the population is divided into groups or clusters • Grouped in heterogeneous clusters • Used when a sampling frame is not available • Takes place in successive stages – multistage sampling • Researcher samples a population that is much more general than the final one (cities) • Then progresses to the next most inclusive sampling units (residential blocks) • Then reaches the final stage (participants in study)

28. Cluster sampling • Tend to contain more samplingerrors than simple or stratified random sampling • Is considered more economical and practical than other types of probability sampling

29. Nonprobability sampling • Where we don’t know whether we have included each element of the population in a sample • Cannot generalize our results to the general population • Used when probability sampling is extremely expensive or difficult or representativeness is not essential, when no sampling frame is available • Where the aim is to generate theory and to gain a wider understanding of social processes

30. Nonprobability sampling • Convenience or accidental sampling • Quota sampling • Purposive or jugdmental sampling • Snowball sampling

31. Nonprobability sampling • Convenience or accidental sampling • When the researcher selects those elements that he or she can access easily until the sample reaches the desired size • Also called accidental or availability sampling • Eg people in library available at a certain time • Quota sampling • Similar to stratified sampling, except that the final selection of elements is not random • Sampling procedure relies on convenience or accidental choice

32. Nonprobability sampling 3. Purposive or judgmental sampling • Also known as theoretical sampling • The researcher selects a sample that can be judged to be representative of the total population • Commonly used in qualitative research 4. Snowball sampling • Research respondents obtaining other potential respondents

33. Sample size • Refers to the number of elements in a sample • The larger the sample, the more valid and accurate the study • The more heterogeneous a population is, the larger the sample must be to properly represent the characteristics of the population • See factors that influence sample size on p165