Interrupted Time Series Design Overview

Questions and Answers

Interrupted time series is a _____________ level longitudinal design

macro-level

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?

To understand how a discrete event affects a specific outcome.

What are the four steps in interrupted time-series design?

(1) Regress the outcome on the discrete event while controlling for confounding variables. (2) Analyze differences in post-treatment and pre-treatment time-series lines. (3) Determine the pattern of the dependent variable after the interruption. (4) Conduct a Granger causality test. Signup and view all the answers

What are the limitations of the interrupted time-series design?

(1) Cannot use ordinary statistics due to autocorrelation, (2) Potential lack of confounder data, (3) Susceptibility to history effects, (4) Missing changes in trend if data points are too far apart, (5) May not capture long-term trends. Signup and view all the answers

What are the benefits of the interrupted time-series design?

(1) Good for establishing temporal order, (2) Collects information on DV at many points, (3) Allows analysis of change before and after the event. Signup and view all the answers

How does this design compensate for the infeasibility of randomized designs?

By including non-equivalent control groups, non-equivalent dependent variables, and removing treatment at a known time. Signup and view all the answers

Describe the basic design of interrupted time series.

Multiple observations for a single case, with pre-intervention cases serving as the counterfactual. Signup and view all the answers

What type of outcomes is best for interrupted time-series designs?

Best with short-term outcomes or a well-defined lag. Signup and view all the answers

What is level/intercept change?

A change in DV level after intervention, maintaining the same slope. Signup and view all the answers

What is trend/slope change?

No immediate change and the level is the same after intervention. Signup and view all the answers

What is level and slope change?

Immediate change in both level and slope after intervention. Signup and view all the answers

What does slope change after a lag mean?

Not an immediate effect on Y. Signup and view all the answers

What is temporary level change?

Intervention had a short-term effect. Signup and view all the answers

What are the five key threats to validity?

1 - History, 2 - Changes in instrumentation, 3 - Gradual implementation of intervention, 4 - Short time series, 5 - Use of archival data. Signup and view all the answers

What are some ways to improve ITS designs?

1 - Carefully select control series, 2 - Test for multiple time patterns, 3 - Test for robustness with different time points, 4 - Test for effects based on different intervention times, 5 - Define DV narrowly. Signup and view all the answers

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.