In a repeated cross-sectional study, the same population is studied at multiple time points. At each time point, data are collected from a different sample of the population.
A repeated cross-sectional study is a type of longitudinal study because data are collected repeatedly over a period of time.
However, as the name suggests, it also resembles a cross-sectional study. Data are obtained from each group of participants at a single time point, and this process is repeated several times.
Repeated cross-sectional studies are helpful for studying changes in a population over time.
Continue reading: What is a repeated cross-sectional study?
In a cross-sectional study, researchers recruit a group of participants (often using random sampling), then measure exposure variables (e.g., risk factors—such as smoking) and outcomes (e.g., lung cancer). Cross-sectional studies are helpful for determining the prevalence of an outcome in a population.
Cohort studies instead recruit participants based on their exposure status. Cohort studies are longitudinal. They follow participants over time to observe the effect of this exposure (e.g., how many people who were exposed to asbestos go on to develop lung cancer). Cohort studies are helpful for establishing cause-and-effect relationships.
Continue reading: What is the difference between a cross-sectional study and a cohort study?
Case-control and cross-sectional studies differ in how participants are recruited and the types of questions they can answer.
In a case-control study, participants are recruited based on outcome status. Data are collected from two groups. The “case” group has an outcome of interest (e.g., a diabetes diagnosis), and the “control” group does not. These groups can be compared to understand what differences may have contributed to the outcome.
In a cross-sectional study, a sample of participants is recruited from a population without considering outcome status (often using random sampling). Data on outcomes and risk factors are then collected simultaneously from the sample. Cross-sectional studies are helpful for assessing the prevalence of an outcome.
Continue reading: What is the difference between a case-control study and a cross-sectional study?
In a cross-sectional study, researchers collect data from individuals at a single point in time.
In a longitudinal study, researchers collect data from individuals repeatedly or continuously over an extended period of time (often years).
Cross-sectional studies are faster and less expensive to conduct than longitudinal studies. However, because they collect data at a single point in time, cross-sectional studies are not the best option for establishing cause-and-effect relationships.
A common practice is to conduct a cross-sectional study to generate hypotheses. You can then use this information to design a longitudinal study.
Continue reading: What is the difference between a cross sectional study and a longitudinal study?
A Likert-type scale resembles a Likert scale—the respondent is presented with a series of statements, and they select their response from a set of ranked options.
However, for a true Likert scale, the respondent rates their level of agreement with these statements using a 5- or 7-point scale. Likert-type scales may instead ask people to rate constructs like frequency, satisfaction, or likelihood.
An example of a true Likert scale item is as follows:
Please rate your agreement with the following statement: I am most productive in the morning.
- Strongly disagree
- Disagree
- Neither agree nor disagree
- Agree
- Strongly Agree
A Likert-type scale may include items such as the following:
How frequently do you wake up before 8 a.m.?
- Never
- Rarely
- Sometimes
- Often
- Always
Continue reading: What is a Likert type scale?
Though traditional Likert scales include a 5-point response scale, some research has indicated that 7-point scales provide more reliable results.
As a rule of thumb, 5-point scales are better for unipolar constructs, which range from zero to positive, such as frequency. You may want to use 7-point scales for bipolar (or dichotomous) constructs that range from negative to positive, such as quality—some evidence suggests that doing so can increase reliability.
Continue reading: Should I use a 5- or 7-point Likert scale?
The term Likert scale is commonly mispronounced. The correct pronunciation is “Lick-urt,” not “Lie-kert.” The scale is named after its creator, Rensis Likert, and this is how you correctly say his last name.
Continue reading: How do you pronounce Likert scale?
Open-ended and close-ended questions can both be included in questionnaires, but they serve different purposes.
Open-ended questions have no set response: the respondent can respond in their own words. This freedom may encourage more detailed or unpredicted responses. However, responses to open-ended questions are often more time-consuming and difficult to analyze.
Close-ended questions have set response options. They can be answered quickly but limit the detail the respondent is able to provide. Types of close-ended questions include multiple-choice questions, rating scales, and Likert scales.
Continue reading: What’s the difference between open-ended and close-ended questions?
A rating scale is a type of multiple-choice question often included in a questionnaire. Rating scales include ordered response options that range from low to high (unipolar) or negative to positive (dichotomous). Rating scales are often used to assess frequency, satisfaction, or other subjective experiences.
A Likert scale is a special type of rating scale that is used to assess a person’s level of agreement with a series of statements about a topic.
Continue reading: What is a rating scale?
Yes—the Likert scale, which is often included in questionnaires, is an example of an ordinal measurement.
Ordinal data have a clear order (items can be logically sorted), but the distance between items is not uniform nor quantifiable.
A Likert scale asks someone to rate how much they agree with a series of statements. Response options typically include “Strongly disagree,” “Disagree,” “Neutral,” “Agree,” and “Strongly agree.”
Because these options have a clear rank (we can easily and objectively order them) but unclear spacing (i.e., the distance between “Disagree” and “Neutral” isn’t necessarily the same as the distance between “Neutral” and “Agree”), a Likert scale is considered an ordinal measurement.
Note: For analysis, Likert scale data are sometimes converted to numbers and treated as integer data. This process allows the research to use certain analysis techniques. It’s always important to carefully consider and provide justification for any analyses you decide to conduct.
Continue reading: Is a Likert scale ordinal?
