longitudinal study

(noun)

A correlational research study that involves repeated observations of the same variables over long periods of time.

Related Terms

Examples of longitudinal study in the following topics:

  • Repeated Measures Design

    • For instance, repeated measures are collected in a longitudinal study in which change over time is assessed.
    • Other studies compare the same measure under two or more different conditions.
    • Study changes in participants' behavior over time: Repeated measures designs allow researchers to monitor how the participants change over the passage of time, both in the case of long-term situations like longitudinal studies and in the much shorter-term case of order effects.

  • ANOVA Design

    • Factorial ANOVA is used when the experimenter wants to study the interaction effects among the treatments.
    • Repeated measures ANOVA is used when the same subjects are used for each treatment (e.g., in a longitudinal study).
    • ANOVA generalizes to the study of the effects of multiple factors.
    • The use of ANOVA to study the effects of multiple factors has a complication.

  • Case Studies

  • Overview of data collection principles exercises

    • Also comment on whether or not the results of the study can be generalized to the population and if the findings of the study can be used to establish causal relationships.
    • Also comment on whether or not the results of the study can be generalized to the population and if the findings of the study can be used to establish causal relationships.
    • (d) Can we conclude that studying longer hours leads to higher GPAs?
    • 1.9 (a) Explanatory: number of study hours per week.
    • The variability in GPA also appears to be larger for students who study less than those who study more.

  • Observational studies

    • Generally, data in observational studies are collected only by monitoring what occurs, while experiments require the primary explanatory variable in a study be assigned for each subject by the researchers.
    • Thus, observational studies are generally only sufficient to show associations.
    • Observational studies come in two forms: prospective and retrospective studies.
    • Aprospective study identifies individuals and collects information as events unfold.
    • One example of such a study is The Nurses Health Study, started in 1976 and expanded in 1989.

  • Introducing observational studies and experiments

    • There are two primary types of data collection: observational studies and experiments.
    • Researchers perform an observational study when they collect data in a way thatdoes not directly interfere with how the data arise.
    • For instance, researchers may collect information via surveys, review medical or company records, or follow a cohort of many similar individuals to study why certain diseases might develop.
    • In general, observational studies can provide evidence of a naturally occurring association between variables, but they cannot by themselves show a causal connection.
    • See the case study inSection 1.1 for another example of an experiment, though that study did not employ a placebo.

  • What are Observational Studies?

    • An observational study is one in which no variables can be manipulated or controlled by the investigator.
    • There are two major types of causal statistical studies: experimental studies and observational studies.
    • In other words, observational studies have no independent variables -- nothing is manipulated by the experimenter.
    • Observational studies are a type of experiments in which the variables are outside the control of the investigator.
    • Identify situations in which observational studies are necessary and the challenges that arise in their interpretation.

  • Experiments exercises

    • Briefly outline a design for this study.
    • (c) Does this study make use of blocking?
    • The subjects volunteered to be a part of the study.
    • (c) Has blocking been used in this study?
    • We could say the study was partly double-blind.

  • Critical Evaluation

    • We need to critically evaluate the statistical studies we read about and analyze before accepting the results of the study.
    • Self-Funded or Self-Interest Studies: A study performed by a person or organization in order to support their claim.
    • Is the study impartial?
    • Read the study carefully to evaluate the work.
    • Do not automatically assume that the study is good but do not automatically assume the study is bad either.

  • Exercises