Question

Q1- Compare qualitative research and quantitative research and provide examples /situations for each.

The student has to provide an example for quantitative and for qualitative research.

Q2. Compare and contrast cross-sectional studies and longitudinal studies and provide examples /situations for each.

The student has to provide an example for cross-sectional studies and longitudinal studies.

Q3. Explain the different techniques of probability sampling and non-probability sampling and provide an example for each type.

The student has to provide an example for each type.

Answer 1

• Qualitative research is used to understand the underlying reasons, opinions, and motivations. It provides insights into the problem or helps to develop ideas or hypotheses for quantitative research.
  Quantitative research gathers data in a numerical form which can be put into categories, or in rank order, or measured in units of measurement. This type of data can be used to construct graphs and tables of raw data.
• Qualitative research data are collected through perticipants observations and interview.Also data are reported language of format.
  Quantitative research data collected through experiments, surveys,observations and reported through statistical analysis.
• Qualitative research is analyzed by summarizing, categorizing and interpreting.
  Quantitative research is analyzed through mathametically, graphically and statistical inference
• Example: Qualitative Research: Two group of students' height measured from 2 different classes. We want to check student from which class is taller
  Quantitative Research: Group of students' heights are noted. We want to check how many of them is taller than the median height

• In cross-sectional research, a researcher compares multiple segments of the population at the same time.
  Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.
• Longitudinal research data collected on same sample where as cross-sectional research data collected on different sample
• Longitudinal research allows researcher to detect changes in the target population over time period
  cross-sectional research Do not provide cause and effect relationship.
• Example: Longitudinal research: A researcher measuring stamina of a group of people over 5 years
  Cross-sectional research:  A researcher measuring stamina of a group of people accross continent

Probability Sampling:

• Simple Random Sampling: Here we randomly select units from population for required number of sample size.
  Example: 25 employees chosen from a company out of 250 employees
• Stratified Random Sampling: In stratified random sampling, the subjects are initially grouped into different classifications such as gender, level of education, or socioeconomic status. It’s important to note that these classifications should not have any overlapping subjects. From here, researchers randomly select the final list of subjects from the different defined categories to ensure a well rounded sample.
  Example: a population divided into the following strata:
• Category 1: Low socioeconomic status — 39 percent
• Category 2: Middle class — 38 percent
• Category 3: Upper income — 23 percent
  To get the stratified random sample, you would randomly sample the categories so that your eventual sample size has 39 percent of participants taken from category 1, 38 percent from category 2 and 23 percent from category 3.

Systematic Random Sampling:
Systematic random sampling is the random sampling method that requires selecting samples based on a system of intervals in a numbered population. For example, reseacher can give a survey to every fourth customer that comes in to the movie theater. The fact that researcher is giving the survey to every fourth customer is what makes the sampling systematic because there is an interval system. Likewise, this is a random sample because Lucas cannot control what type of customer comes through the movie theater.
By carrying out the processes above, the subjects for our study would be patients 4, 10, 16, 22, 28, etc.

Cluster (Area) Random Sampling

Cluster random sampling is conducted when the size of a population is too large to perform simple random sampling.

Think of instances such as investigating the dietary trends amongst the entire population of Africa — the population is just too large to manage effectively.

In cluster random sampling, the initial research identifies boundaries. Sticking with the example above, our boundaries would be the various countries in Africa.
From here, the researcher randomly selects a number of identified boundaries. It’s important to note that each of the areas, in our case African countries, should have equal chances of being selected.
Finally, the researcher conducting the study can then include all of the individuals within the selected areas, or he or she can use simple random selection to select subjects from the identified countries.

Multi-Stage SamplingMulti-stage sampling involves a combination of two or more of the probability sampling methods outlined above.
With more advanced research, using just one form of probability sampling does not ensure the randomization necessary to ensure confidence in results.
By combining various probability sampling techniques at various stages of research initiatives, researchers are able to maintain confidence that they are mitigating biases as much as possible.

Non-probabilistic sampling:

• Quota sampling

  With proportional quota sampling, the aim is to end up with a sample where the strata (groups) being studied (e.g., males vs. females students) are proportional to the population being studied. If we were to examine the differences in male and female students, for example, the number of students from each group that we would include in the sample would be based on the proportion of male and female students amongst the 10,000 university students.

• Convenience sampling

  A convenience sample is simply one where the units that are selected for inclusion in the sample are the easiest to access. In our example of the 10,000 university students, if we were only interested in achieving a sample size of say 100 students, we may simply stand at one of the main entrances to campus, where it would be easy to invite the many students that pass by to take part in the research.

• Purposive sampling

  Purposive sampling, also known as judgmental, selective, or subjective sampling, is a form of non-probability sampling in which researchers rely on their own judgment when choosing members of the population to participate in their study.

• Self-selection sampling

  Self-selection sampling is a non-probability technique, that is based on the judgement of the researcher. This is a useful tool for researchers, who want people or organisations (units), to participate (or volunteer) as part of a study on their own accord.

• Snowball sampling

  Snowball sampling is particularly appropriate when the population you are interested in is hidden and/or hard-to-reach. These include populations such as drug addicts, homeless people, individuals with AIDS/HIV, prostitutes, and so forth.