A classic example of an interrupted time-series design is a study analyzing a factory's reduction of work shifts from 10 hours to 8 hours. Researchers took a time series of weekly worker productivity measurements over an extended period. The change in shift length served as the experimental 'interruption'. Because productivity increased rapidly following the shift reduction and remained consistently elevated for many months afterward, the researchers could confidently conclude that the shortened shifts were the direct cause of the increased productivity.

Example of an Interrupted Time-Series Design: Factory Shift Reduction

An interrupted time-series design can be illustrated by tracking the number of student absences per week in a course. In this scenario, the 'interruption' is the instructor beginning to publicly take daily attendance. If the multiple measurements show a consistently high number of absences before this treatment, followed by an immediate and sustained drop afterward, the treatment is deemed effective. However, if the long-term trend of absences remains virtually unchanged on average after the intervention, it suggests the treatment failed to produce a meaningful effect, even if minor weekly fluctuations exist.

Example of an Interrupted Time-Series Design: Student Absences

An interrupted time-series design with nonequivalent groups is a quasi-experimental approach that enhances the basic interrupted time-series design by incorporating a control group. This methodology involves taking multiple measurements of a dependent variable at regular intervals over an extended period both before and after an intervention is implemented across two or more groups that were not randomly assigned. By comparing the baseline and post-intervention trends of the treatment group to those of the nonequivalent control group, researchers can obtain stronger evidence regarding the effectiveness of the intervention.

Interrupted Time-Series Design with Nonequivalent Groups

An AB design is a basic single-subject research structure that involves only two phases: an initial baseline phase (A) followed by a single treatment phase (B). This methodology functions similarly to an interrupted time-series design applied to a single participant. A significant limitation of the AB design is its susceptibility to confounding variables; if the dependent variable changes following the intervention, it remains unclear whether the treatment or a coincidental extraneous variable caused the change, resulting in lower internal validity compared to reversal designs.

AB Design

A primary advantage of the interrupted time-series design over a simpler one-group pretest-posttest design is its ability to account for normal, ongoing fluctuations in the dependent variable. By collecting multiple measurements both before and after an intervention, researchers can establish a baseline trend. This prevents them from falsely attributing a change to the treatment when it might simply be normal week-to-week variation, a crucial distinction that a single pretest and posttest measurement cannot reliably detect.

Advantage of Interrupted Time-Series Over Pretest-Posttest Designs

Which of the following procedures is the defining characteristic of an interrupted time-series design?

An interrupted time-series design is a quasi-experimental approach and a variant of the pretest-posttest design. In this methodology, a researcher takes a time series—multiple measurements of a dependent variable at regular intervals—both before and after a treatment is introduced. The treatment effectively 'interrupts' the ongoing measurements. Unlike the simpler pretest-posttest design, which relies on a single before-and-after measurement, the inclusion of multiple measurements allows researchers to better distinguish the actual effect of the intervention from normal variation.

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A one-group pretest-posttest design is a quasi-experimental research method where a single group of participants is measured on a dependent variable both before and after a treatment is administered. Unlike a within-subjects experiment, this design does not counterbalance the order of conditions because the treatment must inherently occur between the initial baseline measurement (pretest) and the subsequent measurement (posttest). While an improvement in posttest scores compared to pretest scores might intuitively suggest that the treatment was effective, such conclusions are difficult to make with certainty because the absence of a control group leaves the study highly vulnerable to alternative explanations and threats to internal validity.

One-Group Pretest-Posttest Design

A time series is a set of measurements of a quantitative variable taken at sequential intervals over a continuous period of time. This approach allows researchers to track trends and ongoing changes within a specific domain, such as recording a factory's weekly worker productivity over an entire year.

Time Series

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Interrupted Time-Series Design

To effectively rule out threats to internal validity—such as history, maturation, testing, instrumentation, regression to the mean, and spontaneous remission—researchers can add a control group to a pretest-posttest design. A control group does not receive the experimental treatment but is subjected to the same external events, passage of time, and repeated measurements as the treatment group. By comparing the posttest changes between the two groups, researchers can isolate the actual effect of the treatment from these alternative explanations, although doing so means the study is no longer a one-group design.

Control Group in Pretest-Posttest Designs

In research design, history serves as an alternative explanation for changes in a dependent variable, representing a significant threat to internal validity. This threat occurs when extraneous external events happen between the pretest and posttest measurements, unintentionally causing a shift in the participants' scores. For instance, if an anti-drug education program is being evaluated, an unexpected and highly publicized celebrity drug overdose occurring during the study period could independently influence participants' attitudes, confounding the results and making it impossible to determine if the education program was the true cause of the change.

History as a Threat to Internal Validity

Maturation is a threat to internal validity that occurs when participants undergo natural developmental or physiological changes—such as growing older, learning, or becoming fatigued—between the pretest and posttest measurements of a study. These natural, ongoing changes, rather than the experimental treatment itself, may be the actual cause of any observed differences in the dependent variable over time. For example, over the course of a year-long intervention, children naturally become better reasoners, which could be misconstrued as an effect of the intervention.

Maturation as a Threat to Internal Validity

The testing threat to internal validity occurs when the mere act of taking a pretest inadvertently influences a participant's responses on the subsequent posttest. Completing the initial measurement might inspire further thought, conversation, or practice regarding the topic being studied. This increased awareness or familiarity can independently cause a change in the posttest scores, regardless of whether the actual experimental treatment was effective.

Testing as a Threat to Internal Validity

Instrumentation threatens a study's internal validity when the fundamental characteristics of the measuring instrument or process change between the pretest and posttest. This issue frequently arises in observational studies where human observers are used to measure behavior, as they may gradually gain skill, become fatigued, or unconsciously alter their evaluation standards over time. Additionally, instrumentation effects can manifest when the participants themselves change their approach to the measurement tool, such as taking a survey very seriously during the novel pretest but becoming bored and less careful with their responses during the posttest.

Instrumentation as a Threat to Internal Validity

Regression to the mean acts as a severe threat to internal validity when participants are selected for a research study specifically because of their extreme initial scores. Statistically, individuals who achieve exceptionally high or low scores on a pretest are highly likely to score closer to the population average on a subsequent posttest simply due to natural, random variability. This makes it incredibly difficult to determine whether a shift in scores was genuinely caused by the experimental treatment or was merely the result of statistical regression.

Regression to the Mean as a Threat to Internal Validity

Spontaneous remission is the natural tendency for many medical conditions and psychological problems to gradually improve over time without any formal treatment or intervention. This phenomenon acts as a significant confounding variable in studies that lack a control group; researchers evaluating a treatment using a simple pretest-posttest design may falsely attribute a patient's recovery to their specific intervention, when in reality, the condition would have naturally improved on its own.

Spontaneous Remission

A practical example of a one-group pretest-posttest design involves evaluating an anti-drug education program for elementary school students. Researchers first measure the students' baseline attitudes toward illegal drugs (the pretest). Next, they implement the anti-drug curriculum over a specified period. Finally, they measure the students' attitudes again (the posttest). By comparing the pretest and posttest scores, researchers can see if attitudes changed, though the lack of a control group makes it difficult to definitively attribute any change solely to the educational program.

Example of a One-Group Pretest-Posttest Design

Why is it difficult to conclude with certainty that a treatment was effective when using a one-group pretest-posttest design?

Time Series Analysis and Its Applications: With R Examples (Third edition)

A time series is a sequence of numerical data points in successive order. In investing, a time series tracks the movement of the chosen data points, such as a security’s price, over a specified period of time with data points recorded at regular intervals. There is no minimum or maximum amount of time that must be included, allowing the data to be gathered in a way that provides the information being sought by the investor or analyst examining the activity.

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