How to use stratified sampling

In a stratified sample, researchers divide a population into homogeneous subpopulations called strata (the plural of stratum) based on specific characteristics (e.g., race, gender, location, etc.). Every member of the population should be in exactly one stratum.

Each stratum is then sampled using another probability sampling method, such as cluster or simple random sampling, allowing researchers to estimate statistical measures for each sub-population.

Researchers rely on stratified sampling when a population’s characteristics are diverse and they want to ensure that every characteristic is properly represented in the sample.

The procedure of stratified sampling.

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An introduction to cluster sampling

In cluster sampling, researchers divide a population into smaller groups known as clusters.  They then randomly select among these clusters to form a sample.

Cluster sampling is a method of probability sampling that is often used to study large populations, particularly those that are widely geographically dispersed. Researchers usually use pre-existing units such as schools or cities as their clusters.

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An introduction to simple random sampling

A simple random sample is a randomly selected subset of a population. In this sampling method, each member of the population has an exactly equal chance of being selected.

This method is the most straightforward of all the probability sampling methods, since it only involves a single random selection and requires little advance knowledge about the population. Because it uses randomization, any research performed on this sample should have high internal and external validity.

The American Community Survey (ACS) uses simple random sampling. Officials from the United States Census Bureau follow a random selection of individual inhabitants of the United States for a year, asking detailed questions about their lives in order to draw conclusions about the whole population of the US.

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An introduction to quasi-experimental designs

Like a true experiment, a quasi-experimental design aims to establish a cause-and-effect relationship between an independent and dependent variable.

However, unlike a true experiment, a quasi-experiment does not rely on random assignment. Instead, subjects are assigned to groups based on non-random criteria.

Quasi-experimental design is a useful tool in situations where true experiments cannot be used for ethical or practical reasons.

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What is a double-blind study?

In experimental research, subjects are randomly assigned to either a treatment or control group. A double-blind study withholds each subject’s group assignment from both the participant and the researcher performing the experiment.

If participants know which group they are assigned to, there is a risk that they might change their behavior in a way that would influence the results. If researchers know which group a participant is assigned to, they might act in a way that reveals the assignment or directly influences the results.

Double blinding guards against these risks, ensuring that any difference between the groups can be attributed to the treatment.

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Control groups in scientific research

In a scientific study, a control group is used to establish a cause-and-effect relationship by isolating the effect of an independent variable.

Researchers change the independent variable in the treatment group and keep it constant in the control group. Then they compare the results of these groups.

Using a control group means that any change in the dependent variable can be attributed to the independent variable.

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Independent and dependent variables

In research, variables are any characteristics that can take on different values, such as height, age, species, or exam score.

In scientific research, we often want to study the effect of one variable on another one. For example, you might want to test whether students who spend more time studying get better exam scores.

The variables in a study of a cause-and-effect relationship are called the independent and dependent variables.

  • The independent variable is the cause. Its value is independent of other variables in your study.
  • The dependent variable is the effect. Its value depends on changes in the independent variable.
Examples of independent and dependent variables
Research Question Independent variable(s) Dependent variable(s)
Do tomatoes grow fastest under fluorescent, incandescent, or natural light?
  • The type of light the tomato plant is grown under
  • The rate of growth of the tomato plant
What is the effect of diet and regular soda on blood sugar levels?
  • The type of soda you drink (diet or regular)
  • Your blood sugar levels
How does phone use before bedtime affect sleep?
  • The amount of phone use before bed
  • Number of hours of sleep
  • Quality of sleep
How well do different plant species tolerate salt water?
  • The amount of salt added to the plants’ water
  • Plant growth
  • Plant wilting
  • Plant survival rate

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What is a cross-sectional study?

A cross-sectional study is a type of research design in which you collect data from many different individuals at a single point in time. In cross-sectional research, you observe variables without influencing them.

Researchers in economics, psychology, medicine, epidemiology, and the other social sciences all make use of cross-sectional studies in their work. For example, epidemiologists who are interested in the current prevalence of a disease in a certain subset of the population might use a cross-sectional design to gather and analyze the relevant data.

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