What is the appropriate sampling procedure used when every member of the accessible population has an equal chance of being selected to participate in the study?

 Chapter 12 • Quantitative Data Collection

• Clearly identifying the target population and the accessible population is important in the sampling process.
• Random sampling techniques share two important characteristics:
  
  • The chance that each member of the population will be selected can be specified.
  • All members of the population have an equal chance of being selected.
• Simple random sampling is the best way to obtain a representative sample, although no method guarantees perfect representation of the population.
• Stratified random sampling is a process whereby certain subgroups are selected for inclusion in the sample.
  
  • Proportional stratified sampling involves a process where the identified subgroups in the sample are represented in the same proportion in which they exist in the population.
  • Equal stratified sampling is a process where the representation of subgroups is equal.
• Cluster random sampling involves the random sampling of existing groups, or clusters.
• Multistage random sampling is a combination of cluster random sampling and individual random sampling.
• Systematic sampling involves selecting the Kth individual from a population list.
  
  • Depending on the organization of the list, systematic sampling may be a random sampling technique or a nonrandom technique.
• Nonrandom sampling techniques do not permit specification of the probability of inclusion in the population, nor do they guarantee that every member of the population has an equal chance of being selected.
• Convenience sampling involves a selection of whoever happens to be available.
• In snowball sampling, current participants identify other individuals to act as participants in the study.
• Quota sampling involves the selection of the sample based on precise numbers of individuals with specific characteristics.
• Purposive or judgment sampling identifies individuals who are believed to be representative of a given population.
• Generally speaking, the larger the population size, the smaller the percentage of the population required to get a representative sample.
• When samples are selected randomly, there is still a chance that differences will exist between the population and the sample; this is known as sampling error.
• Sampling bias is a systematic error and is usually the fault of the researcher.
• A measurement scale is a system used to organize data so they can be reviewed, analyzed, and interpreted appropriately.
  
  • Nominal scales are associated with measuring a categorical variable.
  • Ordinal scales possess the same characteristics as nominal scales but also rank order variables based on the degree to which they possess a given characteristic.
  • Interval scales possess all the characteristics of nominal and ordinal scales, but the values represent equal intervals.
  • Ratio scales are the highest level of measurement and possess all the characteristics of the other scales but also have a true zero point.
• Quantitative data collection techniques vary greatly.
• Surveys are a group of quantitative data collection techniques that involve the administration of a set of questions or statements to a sample of respondents.
• Questionnaires are specific types of surveys that are administered in written form.
• Four types of survey questions are demographic, knowledge, attitudinal, and behavioral questions.
• Closed-ended, or forced-choice, questions resemble multiple-choice or other types of items where respondents select from a number of given options.
• Open-ended questions allow for more individualized responses.
• Checklists are closed-ended items that provide only a dichotomous response option
• Likert items begin with a statement and then ask individuals to respond on an agree–disagree continuum.
• Likert-type items also exist on a continuum but measure something other than agreement.
• Developing quality survey instruments is not an easy task; researchers should follow the design guidelines.
• Formative and summative classroom assessments can be used as sources for quantitative data.
• Standardized test scores can also be used as sources of data.
• Validity and reliability are essential qualities in quantitative research.
• Validity has to do with whether we actually measured what we intended to measure, and whether the inferences follow logically from our interpretations.
• Evidence of validity must be collected during research studies to determine the validity of research inferences.
  
  • Evidence of validity comes from the following sources: test content, response processes, internal structure, relations to other variables, and consequences of testing.
  • Determination of the validity of inferences from collected data is based on judgment or the calculation of validity coefficients.
• Reliability has to do with the consistency of the data that are collected.
  
  • Reliability of quantitative data is determined through several statistical procedures, including test–retest reliability (stability), equivalent-forms reliability (equivalence), and internal consistency methods.
  • Methods for determining internal consistency reliability include split-half, Kuder-Richardson, and Cronbach’s alpha reliabilities.
• Reliability is a necessary but not sufficient condition for validity.

By Saul McLeod, Ph.D.

Definitions

• Sampling is the process of selecting a representative group from the population under study.
• The target population is the total group of individuals from which the sample might be drawn.
• A sample is the group of people who take part in the investigation. The people who take part are referred to as “participants”.
• Generalisability refers to the extent to which we can apply the findings of our research to the target population we are interested in. This can only occur if the sample of participants is representative of the population.
• Biased sample iswWhen certain groups are over or under represented within the sample selected. For instance if only males are selected, or if the advert for volunteers is put into the Guardian, only people who read the Guardian are selected. This limits how much the findings of the study can be generalised to the whole population.

The Purpose of Sampling

In psychological research we are interested in learning about large groups of people who all have something in common. We call the group that we are interested in studying our 'target population'.

In some types of research the target population might be as broad as all humans, but in other types of research the target population might be a smaller group such as teenagers, pre-school children or people who misuse drugs.

It is more or less impossible to study every single person in a target population so psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in.

This is important because we want to generalize from the sample to target population. The more representative the sample, the more confident the researcher can be that the results can be generalized to the target population.

One of the problems that can occur when selecting a sample from a target population is sampling bias. Sampling bias refers to situations where the sample does not reflect the characteristics of the target population.

Many psychology studies have a biased sample because they have used an opportunity sample that comprises university students as their participants (e.g. Asch).

OK, so you’ve thought up this brilliant psychological study and designed it perfectly. But who are you going to try it out on and how will you select your participants?

There are various sampling methods. The one chosen will depend on a number of factors (such as time, money etc.).

Random Sampling

Random sampling is a type of probability sampling where everyone in the entire target population has an equal chance of being selected.

This is similar to the national lottery. If the “population” is everyone who has bought a lottery ticket, then each person has an equal chance of winning the lottery (assuming they all have one ticket each).

Random samples require a way of naming or numbering the target population and then using some type of raffle method to choose those to make up the sample. Random samples are the best method of selecting your sample from the population of interest.

• The advantages are that your sample should represent the target population and eliminate sampling bias.
• The disadvantage is that it is very difficult to achieve (i.e. time, effort and money).

Stratified Sampling

During stratified sampling, the researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative.

A list is made of each variable (e.g. IQ, gender etc.) which might have an effect on the research. For example, if we are interested in the money spent on books by undergraduates, then the main subject studied may be an important variable.

For example, students studying English Literature may spend more money on books than engineering students so if we use a very large percentage of English students or engineering students then our results will not be accurate.

We have to work out the relative percentage of each group at a university e.g. Engineering 10%, Social Sciences 15%, English 20%, Sciences 25%, Languages 10%, Law 5%, Medicine 15% The sample must then contain all these groups in the same proportion as in the target population (university students).

• The disadvantage of stratified samplingis that gathering such a sample would be extremely time consuming and difficult to do. This method is rarely used in Psychology.
• However, the advantage is that the sample should be highly representative of the target population and therefore we can generalize from the results obtained.

Opportunity Sampling

Uses people from target population available at the time and willing to take part. It is based on convenience.

An opportunity sample is obtained by asking members of the population of interest if they would take part in your research. An example would be selecting a sample of students from those coming out of the library.

• This is a quick way and easy of choosing participants (advantage)
• It may not provide a representative sample, and could be biased (disadvantage).

Systematic Sampling

Chooses subjects in a systematic (i.e. orderly / logical) way from the target population, like every nth participant on a list of names.

To take a systematic sample, you list all the members of the population, and then decided upon a sample you would like. By dividing the number of people in the population by the number of people you want in your sample, you get a number we will call n.

If you take every nth name, you will get a systematic sample of the correct size. If, for example, you wanted to sample 150 children from a school of 1,500, you would take every 10th name.

• The advantage of this method is that is should provide a representative sample.
• The disadvantage is that it is very difficult to achieve (i.e. time, effort and money).

How many participants should be used?

This depends on several factors; the size of the target population is important. If the target population is very large (e.g. all 4-6 yr olds in Britain) then you need a fairly large sample in order to be representative.

If the target population is much smaller, then the sample can be smaller but still be representative. There must be enough participants to make the sample representative of the target population.

How to reference this article:

McLeod, S. A. (2019, August 03). Sampling methods. Simply Psychology. www.simplypsychology.org/sampling.html

How to reference this article:

McLeod, S. A. (2019, August 03). Sampling methods. Simply Psychology. www.simplypsychology.org/sampling.html

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