Sampling data collection and analysis:

Sampling:

• In order to answer the research questions, it is doubtful that researcher should be able to collect data from all cases

• There is a need to select a sample

• The entire set of cases from which researcher sample is drawn in called the population; answering questions as to whether it is quantitative or qualitative studies

Stage 1: Clearly Define Target Population

• The first stage in the sampling process is to clearly define target population

• Population might be all students in CMST2TM6; it may be time consuming, and may take more resources

Stage2: Select Sampling Frame

• A sampling frame is a list of the actual cases from which sample will be drawn

• Example of a sampling frame: List with names of ALL students in CMST 2TM6; random sampling techniques

Stage 3: Choose Sampling Technique

• Taking a subset from chosen sampling frame or entire population is called sampling

• Sampling techniques can be divided into two types:

Probability or random sampling

• Non- probability or non- random sampling

• Probability or Random Sampling

• Probability sampling means that every item in the population has an equal chance of being included in sample; Mostly used in quantitative studies

• Systematic sampling: Every nth case after a random start is selected

• Stratified random sampling: Stratified sampling is where the population is divided into strata (or subgroups) and a random sample is taken from each subgroup; E.g. Subgroups might be based on gender or occupation

• Cluster sampling: Cluster sampling is where the whole population is divided into clusters or groups

• Subsequently, a random sample is taken from these clusters, all of which are used in the final sample; A cluster can be a geographical region

Non probability Sampling

• Non probability sampling is often associated with qualitative research

• Samples are smaller and are intended to examine a real life phenomenon

• Samples are not to make statistical inferences in relation to the wider population

• A sample of participants or cases does not need to be representative, or random

• A clear rationale is needed for the inclusion of some cases or individuals rather than others

• Quota sampling: Quota sampling is a non random sampling technique in which participants are chosen on the basis of predetermined characteristics

• The total sample will have the same distribution of characteristics as the wider population

• Snowball sampling: This approach is most applicable in small populations that are difficult to access due to their closed nature

• Convenience sampling: Convenience sampling is selecting participants because they are often readily and easily available

• E.g. using friends or family as part of sample is easier than targeting unknown individuals

• Purposive or judgmental sampling: It is where the researcher includes cases or participants in the sample because they believe that they have specific knowledge about the topic

Stage 4: Determine Sample Size

• In order to generalize from a random sample and avoid sampling errors or biases, a random sample needs to be of adequate size

• Several statistical formulas are available for determining sample size

Stage 5: Collect Data

• Once target population, sampling frame, sampling technique and sample size have been established, the next step is to collect data

Stage 6: Assess Response Rate:

• Response rate is the number of cases agreeing to take part in the study

• Most researchers never achieve a 100 percent response rate

• Reasons for this might include refusal to respond, ineligibility to respond, inability to respond, or the respondent has been located but researchers are unable to make contact

Data Collection:

• Although the table above illustrates qualitative and quantitative research as distinct and opposite, in practice they are often combined or draw on elements from each other

• For example, quantitative surveys can include open ended questions

• Qualitative and quantitative methods can also support each other e.g. findings from a qualitative study can be used to guide the questions in a survey

Methods for collecting and analyzing qualitative data:

• Individual interview: designed to elicit the interviewee’s knowledge or perspective on a topic

• Useful for exploring an individual’s beliefs, values, understandings, feelings, experiences and perspectives of an issue

• Focus group discussions: A focus group discussion is an organized discussion between 6 to 8 people

• Focus group discussions provide participants with a space to discuss a particular topic, in a context where people are allowed to agree or disagree with each other

• Photovoice: Photovoice involves giving a group of participant’s cameras, enabling them to capture, discuss and share stories they find significant

Quantitative methods

• Surveys and questionnaires: use carefully constructed questions, often ranking or scoring options or using closed-ended questions

• A closed-ended question limits respondents to a specified number of answers

Qualitative data analysis

• Qualitative data analysis is a process that seeks to reduce and make sense of vast amounts of information, often from different sources

• The idea is for impressions that shed light on a research question to emerge

• It is a process where you take descriptive information and offer an explanation or interpretation

• The information can consist of interview transcripts, documents, blogs, surveys, pictures, videos etc

• Qualitative data analysis ought to pay attention to the ‘spoken word’, context, consistency and contradictions of views, frequency and intensity of comments, their specificity as well as emerging themes and trends

• Codes: A code is a word or a short phrase that descriptively captures the essence of elements of your material (e.g. a quotation) and is the first step in your data reduction and interpretation

• Themes: Once you have coded all of your material you need to start abstracting themes from the codes

Quantitative analysis:

• Statistics help us turn quantitative data into useful information to help with decision making

• Statistics can be used to summarize data, describing patterns, relationships and connections

• Statistics can be descriptive or inferential

• Descriptive statistics help us to summarize data whereas inferential statistics are used to identify statistically significant differences between groups of data