Sampling and Participants Dr. K. A. Korb University of Jos

Sampling and Participants

Dr. K. A. KorbUniversity of Jos

Outline Population Validity Sampling Methods

Simple Random Sampling Stratified Sampling Cluster Sampling Convenience Sampling

Sample Size


Population Validity Extent that an experiment’s results can

generalize beyond participants in a particular study to a larger group of people

Research Example: A descriptive study examining the extent of parents’ involvement in schools


Parents in these two samples (government school vs. Hillcrest) will likely have different involvement in their child’s education because of many different reasons – finances, parental education, trust of school officials, etc.

The purpose of sampling validity is to make a strong argument for why the results of your study will generalize beyond those who participated in your study.

PopulationPopulationAll Nigerian parents with children in schoolAll Nigerian parents with children in school

SampleSampleAll Nigerian parents with students All Nigerian parents with students in Government Secondary Schoolin Government Secondary School

SampleSampleAll Nigerian parents with students All Nigerian parents with students in Hillcrestin Hillcrest


Simple Random Sampling Definition: All individuals in the defined

population have an equal chance of being part of the sample.

Advantage: Conclusions from the data can be generalized to the larger population

Disadvantage: Difficult to implement, so very few studies actually use simple random sampling


Simple Random Sampling Example

Population:Population:All Nigerian parents with children in school

Size: Millions of adults

Sample:Sample:Those randomly chosen from the population.

Size: 100-200

Participants are chosen randomly either through a random number table or putting all names in a hat.

For this study, simple random sampling would be practically impossible.


Random Sampling vs. Random Assignment Random Sampling: Randomly selecting those

people who will participate in your study. Allows you to generalize your findings beyond the

sample of your study. However, few educational studies can practically use

random selection. Random Assignment: Once you participants

have been selected, they are randomly placed into the treatment and control groups. Only applies to experimental design MUST be used for an experiment to be a true

experiment.


Random Sampling

PopulationPopulation

SampleSample

Randomly chose people from the population to be part of the research sample.


Random Assignment

SampleSample1. Names of all participants are placed in a hat

Treatment Treatment GroupGroup

Control Control GroupGroup

2. As names are drawn out of the hat, they are placed in alternating order into the treatment and control groups.


Stratified Sampling Definition: Select a sample so certain subgroups

can be adequately represented Subgroups, or stratums, are identified by demographic

variables of interest to the study, such as gender, ethnicity, age, occupation, etc.

Random sampling will be used within each stratum. Use If:

The group is heterogeneous on an important variable (e.g., ethnicity, gender).

or The purpose of the study is to compare groups of

different characteristics (e.g., a causal-comparative study)


Stratified Random Sampling Example In the research example, one research question

may compare the participation of mothers to the participation of fathers in their child’s education.

To test the difference between mothers and fathers, an equal number of mothers and fathers must be selected for the sample.

Therefore, the population should first be divided into male and female, and then random selection applied within each group.

Procedure:1. Determine the stratums to be sampled.2. Determine the number of participants necessary for

each stratum.3. Randomly sample participants from within each

sample.


Stratified Random Sampling Example

Population Stratum:Population Stratum: Fathers of children in school

SampleSample

Randomly chose the same number from both stratums.Population Stratum:Population Stratum:

Mothers of children in school

The resulting sample will have an equal number of participants from both stratums.


Cluster Sampling Definition: Randomly sample a naturally

occurring group of people For example: A group could be a classroom of students

Advantage: Easier to conduct the study. Disadvantage: Regular statistics CANNOT be

conducted with cluster sampling. Instead of conducting statistics on participants’ data,

you have to conduct the statistics on the groups’ data. Therefore, finding significant results is considerably

more difficult.


Cluster Sampling Example

Population Groups:Population Groups: Schools in Nigeria

SampleSample

Randomly chose the groups from the population.

The resulting sample will be analyzed based on the GROUP data.


Convenience Sampling Definition: Selects a sample that suits the

purpose of the study and is convenient. Advantage: Practically, most of the other

sampling methods are impossible to accomplish. A strategic convenience sample makes psychological and educational research possible.

Disadvantage: The researcher has to build a case in the conclusion of their paper about the group of people the study’s findings will generalize to.

Note: Virtually all educational research uses convenience sampling, perhaps with some elements of random sampling or stratified random sampling.


Convenience Sampling When using convenience sampling, you

will improve the quality of your work if you: Specifically describe the characteristics of your

sample. Give a rationale for why the sample was

appropriate for your study Specify the population to which your results

will likely generalize.


Convenience Sampling Example

Population:Population:All Nigerian parents with children in school

SampleSample

Participants are chosen from a group that is convenient to the experimenter and relevant for the purposes of the study.

This convenience sample includes participants from four schools that are geographically close to the experimenter and schools where the experimenter personally knows the headmaster. However, the experimenter made a point to select two public and two private schools, as well as a school with mostly wealthy children, a school with mostly poor children, and two schools in between because these characteristics may influence the results of the study.


Sample Size General Rule: Larger samples are better

Larger samples more accurately reflect the characteristics of the general population.

Larger samples also increase your chances of getting significant results for your study because one of the values that determine statistical significance is the size of the sample.

Exception: Case studies and qualitative studies tend to use smaller sample sizes


Minimum Sample Size Descriptive Survey Research Designs

At least 100 participants in each group Causal-Comparative Research Designs

At least 15 participants in each group to be compared

Correlational Research Designs At least 30 participants

Experimental Research Designs At least 15 participants in the control group

and at least 15 participants in each treatment group.


Documents

Sampling and Participants Dr. K. A. Korb University of Jos