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Interrupted time series is a _____________ level longitudinal design
Interrupted time series is a _____________ level longitudinal design
macro-level
What is interrupted time series analysis?
What is interrupted time series analysis?
A quasi-experimental design that can evaluate an intervention effect using longitudinal data.
When should researchers use the interrupted time-series design?
When should researchers use the interrupted time-series design?
To understand how a discrete event affects a specific outcome.
What are the four steps in interrupted time-series design?
What are the four steps in interrupted time-series design?
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What are the limitations of the interrupted time-series design?
What are the limitations of the interrupted time-series design?
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What are the benefits of the interrupted time-series design?
What are the benefits of the interrupted time-series design?
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How does this design compensate for the infeasibility of randomized designs?
How does this design compensate for the infeasibility of randomized designs?
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Describe the basic design of interrupted time series.
Describe the basic design of interrupted time series.
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What type of outcomes is best for interrupted time-series designs?
What type of outcomes is best for interrupted time-series designs?
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What is level/intercept change?
What is level/intercept change?
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What is trend/slope change?
What is trend/slope change?
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What is level and slope change?
What is level and slope change?
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What does slope change after a lag mean?
What does slope change after a lag mean?
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What is temporary level change?
What is temporary level change?
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What are the five key threats to validity?
What are the five key threats to validity?
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What are some ways to improve ITS designs?
What are some ways to improve ITS designs?
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Study Notes
Interrupted Time Series Design Overview
- Interrupted time series design is classified as a macro-level longitudinal design.
- It is a quasi-experimental approach that evaluates the effect of interventions using longitudinal data, implementing an intervention at a specific time to assess its impact.
Purpose and Application
- This design is suited for understanding the effect of discrete events on specific outcomes, such as laws or policies.
- Example: Analyzing the impact of new gun laws on crime rates several years later.
Steps in Interrupted Time Series Design
- Regres outcome data based on the discrete event while controlling for confounding variables, creating pre-treatment and post-treatment models.
- Examine changes in the time-series lines/equations post-event: identify sharp discontinuities, different intercepts, and slope alterations.
- Investigate the pattern of the dependent variable (DV) changes following the interruption: gradual vs. abrupt, immediate vs. delayed, temporary vs. permanent.
- Conduct a Granger causality test to determine which variable changed first.
Limitations
- Ordinary statistics are inappropriate due to the lack of independence in observations; multi-level modeling (MLM) is required.
- Incomplete data on potential confounders can lead to spurious results; using control areas aids comparison.
- Internal validity is threatened by history effects, particularly as data spans long periods.
- Insufficient temporal resolution may obscure when changes occurred or may not capture long-term trends.
- A narrow time frame could miss significant trends, as interventions may influence outcomes immediately or over extended periods.
Benefits
- Establishes temporal order, a critical criterion for causation.
- Collects extensive DV data (typically over 20 time points) to analyze trends before and after intervention.
- Allows for a comprehensive understanding of changes in the DV across time.
Addressing Non-feasibility of Randomized Designs
- Incorporate non-equivalent control groups for comparative analysis.
- Utilize non-equivalent dependent variables to assess intervention impact across similar but different metrics.
- Implement treatment removal at known time points to evaluate effects.
Basic Design Structure
- Involves multiple observations of a single case, with pre-intervention cases serving as a counterfactual for post-intervention analysis.
- DV measurements should be taken consistently over defined intervals (e.g., every two years).
- Requires substantial pre- and post-intervention data collection (often 20 measurements each) to effectively demonstrate changes.
Optimal Outcomes for Design
- Best suited for short-term outcomes or well-defined lag effects, facilitating clearer attribution of changes to the intervention.
Types of Changes in Outcomes
- Level/intercept change: DV level shifts without slope alteration, a common expectation.
- Trend/slope change: Lack of immediate level change post-intervention.
- Level and slope change: Immediate shifts in both level and slope following the intervention.
- Slope change after a lag: Delayed adjustment in the DV.
- Temporary level change: Short-lived effects of the intervention.
Key Threats to Validity
- History: Concurrent events might distort results; shorter, purposively selected time periods can mitigate this.
- Instrumentation: Changes may originate independently from the intervention or result from improved data collection methods.
- Gradual implementation can obscure true effects.
- Short time series may not capture the true trend; additional time points might alter findings.
- Archival data limitations can hinder understanding of precise intervention timeliness and implementation variedness.
Enhancements for ITS Designs
- Select control series with similar pre-intervention levels and trends for more accurate comparisons.
- Assess multiple time patterns for comprehensive analysis.
- Test robustness by altering time points or intervention timing to validate findings.
- Narrowly define dependent variables for improved construct validity and analyze non-equivalent outcomes.
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Description
Explore the concepts and applications of Interrupted Time Series Design in this quiz. Learn how this quasi-experimental approach evaluates interventions using longitudinal data and analyze its impact on specific outcomes. Understand the steps involved in implementing this design effectively.
