Systematic sampling is a probability sampling method, which typically ensures a lower risk of bias than nonprobability sampling methods.
However, systematic sampling can be vulnerable to sampling bias, especially if the starting point isn’t truly random. The choice of sampling interval can also introduce bias:
If the interval is too small, the sample can lack representativeness of the population.
If the interval is too large, the sample might not capture all the variation that exists in the population.
Systematic sampling is a random sampling method. Another name for random sampling is probability sampling.
In systematic sampling, the researcher chooses a random starting point in a list of the population (e.g., by using a random number generator) before selecting subjects for the sample at a regular sampling interval (n). The random starting point and regular interval ensure the random nature of this sampling method.
You can use a formula to calculate the sampling interval in systematic sampling, which is a probability sampling method where the researcher systematically selects subjects for their sample at a regular interval.
You can calculate the sampling interval (n) by dividing the total population by the desired sample size.
Formula:n = population size/sample size
Example: I = 2,000/200 = 10
In some cases, people might use a different letter to indicate the sampling interval (e.g., k). This is irrelevant to the use of the formula.
Purposive sampling is often chosen over systematic sampling in situations where the researcher wants to select subjects that have specific traits that are needed in their sample.
Systematic sampling is a probability sampling method where the researcher systematically selects every nth member of the population with a random starting point. The researcher is unable to influence the characteristics of the people that end up in the sample.
Purposive sampling is a non-probability sampling method where the researcher selects specific subjects with traits that can provide the best information to achieve the research aims.
It is inappropriate to use systematic random sampling when your population has a periodic or cyclic order. This could result in only including individuals with a specific characteristic (e.g., age) in your sample.
Systematic sampling example: Unrepresentative sampleYour list of employees alternates between men, women, and nonbinary people. You select every third individual, which means you’re only selecting nonbinary people. This wouldn’t be a representative sample because the sample doesn’t contain any people who identify as men or women, whereas they make up most of the population.
History: Changes in the environment or events that occur outside of the study can affect the outcome.
Maturation: Changes in the participants over time (e.g., age, skill level) can affect the outcome.
Testing: The act of testing or measurement itself can affect the outcome (testing effect, practice effect, or carryover effect).
Instrumentation: Changes in the measuring instrument or tool used to collect data can affect the outcome.
Statistical regression to the mean: The tendency of extreme scores to regress towards the mean, which can lead to a loss of statistical significance.
Selection: The selection of participants for the study can affect the outcome (selection bias), especially in the case of non-probability sampling.
Experimental mortality or attrition bias: The loss of participants or dropouts during the study can affect the outcome.
Multiple-treatment interference: The interaction between different treatments or conditions can affect the outcome.
Social desirability bias: The participants’ awareness of being in a study and their desire to be well-liked by researchers can affect the outcome.
Social interaction: The participants’ awareness of being treated differently than people in other groups can affect the outcome.
Residual confounding: The presence of unmeasured or uncontrolled extraneous or confounding variables that affect the outcome and are not accounted for in the analysis.
Order effect: The order of the independent variable levels affects the dependent variable.
There are several ways to counter these threats to internal validity, for example, through randomization, the addition of control groups, and blinding.
Construct validity refers to the extent to which a study measures the underlying concept or construct that it is supposed to measure.
Internal validity refers to the extent to which observed changes in the dependent variable are caused by the manipulation of the independent variable rather than other factors, such as extraneous variables or research biases.
Construct validity vs. internal validity exampleYou’re studying the effect of exercise on happiness levels.
Construct validity would ask whether your measures of exercise and happiness levels accurately reflect the underlying concepts of physical activity and emotional state.
Internal validity would ask whether your study’s results are due to the exercise itself, or if some other factor (e.g., changes in diet or stress levels) might be causing changes in happiness levels.
Content validity and face validity are both types of measurement validity. Both aim to ensure that the instrument is measuring what it’s supposed to measure.
However, content validity focuses on how well the instrument covers the entire construct, whereas face validity focuses on the overall superficial appearance of the instrument.
The best way for a researcher to judge the face validity of items on a measure is by asking both other experts and test participants to evaluate the instrument.
The combination of experts with background knowledge and research experience, along with test participants who form the target audience of the instrument, provides a good idea of the instrument’s face validity.
Face validity refers to the extent to which a research instrument appears to measure what it’s supposed to measure. For example, a questionnaire created to measure customer loyalty has high face validity if the questions are strongly and clearly related to customer loyalty.
Construct validity refers to the extent to which a tool or instrument actually measures a construct, rather than just its surface-level appearance.
