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 Stage 1: Definition of the problem
Stage 2: Obtaining the information
Stage 3: Analysing and interpreting the data
Stage 4: Communicating the results

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
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 population parameters (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 fth element 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 sampling errors 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