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Research DesignClassification

NTR 629 - Week 2

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How Study Designs Differ

Number of observations made Directionality of exposure Data collection methods Timing of data collection Unit of observation Availability of subjects

Experimental Approacheso Manipulation (exposure of

interest controlled by the investigator)

o Hypothesis testing o Examines cause-effecto Quantitative and analytic

Experimental Design Most rigorous design.

Randomization of study subjects/units

Quasi-experimental Design Less rigorous, because no

randomization of study subjects/units

Observational Approaches No manipulation No randomization of study

subjects/units Less rigorous than

experimental designs Analytic or descriptive

Analytic studies E.g., many ecologic

studies, case-control studies, cohort studies

Descriptive studies: E.g. cross-sectional

surveys

Study Design Approaches

Analytical Experimental Quasi Experimental Pre-Experimental Cohort studies Case Control (or Single

Subject) Historiography Analytical Survey Content Analysis Causal-Comparative

Descriptive Case Study Case Series Developmental Correlational Descriptive Survey Field/Ethnographic

Classification Based on Purpose

Analytical Test hypothesis Allows detection of causal

associations Numerical data –

quantitative.

Descriptive No true hypothesis Establishes a relationship Describes state of nature

at point in time No control of variables Recording of

observations Primarily to totally

qualitative

Design Characteristics

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Analytical Designs (Part 1):Experimental Designs

True Experimental Design

Pretest post-test control group design TWO (or more) groups:

Random/control group O1 O2 Random/experimental group O1 X O2

Caution with within-session variation between treatments A and B… control conditions. Pretest important if need to check equivalence of groups.

Key for Study Design Symbols: O1 = observation 1 (measurement of dependent variable) X = manipulated variable; independent variable O2 = observation 2 (measurement of same dependent variable as O1)

True Experimental Design

Pretest post-test control group design THREE groups:

Random/control group O1 O2 Random/experimental group A O1 XA O2 Random/experimental group B O1 XB O2

Caution with within-session variation between treatments A and B… control conditions. Pretest important if need to check equivalence of groups.

True Experimental Design

Post-test only control group design TWO (or more) groups:

Random/control group O2 Random/experimental group X O2

No pretest? Assume equivalence with randomization. No interaction effect with pretesting.

True Experimental Design

Solomon four group design FOUR groups:

Random/experimental group O1 X O2 Random/control group 1 O1 O2 Random/control group 2 X O2 Random/control group 3 O2

Important if taking pretest influences post-test.

Quasi Experimental Design

Nonequivalent control group design TWO groups:

Experimental group O1 X O2 Control group O1 O2

Uses intact groups (e.g., class); no randomization

Quasi Experimental Design

Static group design TWO groups:

Experimental group X O2 Control group O2

Uses intact groups (e.g., class); no randomization

Quasi Experimental Design Counterbalanced design

FOUR (or more) groups (A, B, C, D) and FOUR (or more) treatment variations (1, 2, 3, 4), with exposure at different times during study:

ReplicationTreatment Variations

XA XB XC XD1 A B C D2 B D A C3 C A D B4 D C B A

Uses intact groups (e.g., class); no randomization

Quasi Experimental Design

Single subject design ONE subject:

Experimental subject base-O1 X withdraw-X O2

“Behavioral”, natural setting, little generalizability

Quasi Experimental Design

One group time series design ONE group:

Experimental groupO1 O2 O3 O4 X O5 O6 O7 O8

Determine if effect of X, and if X is short-term effect.

Quasi Experimental Design

Control group time series design TWO groups:

Experimental group O1 O2 O3 O4 X O5 O6 O7 O8

Control group O1 O2 O3 O4 O5 O6 O7 O8

Helps control selection-maturation effects.

Quasi Experimental Design

Control group time series design FIVE (or more) groups:

Experimental group A O1 X O2 Experimental group B O1 X O2 Experimental group C O1 X O2 Experimental group D O1 X O2 Experimental group E O1 X O2

Helps control maturation, pretest, regression, history

Factorial Design

2x2 factorial design To examine interaction effects of two or more

independent variables (X) and test several H0 simultaneously.

Teaching method (X1) Length of period (X2)50 minutes 30 minutes

Discussion O1 O2

Lecture O3 O4

Factorial Design There are many variations of factorial

designs. The variables can have multiple levels. E.g.,: 2x3 design

two X (X = manipulation): one with two levels, one with three levels

3x3 design Three X, each with three levels

2x2x2 design Three independent variables, each varied two

ways

Advantages Experimental Design

Comparing outcomes in treated group compared to an equivalent control group

Participants in both groups are enrolled (random assignment into group), treated, and followed over the same time period

Single or double-blinded. Used to test efficacy of

preventive (prophylactic) or therapeutic (curative) measures

Multicenter trials--results from several researchers pooled.

Limitations Artificial setting Limited scope of potential

impact Adherence to protocol is

difficult to enforce Possible ethical dilemmas

Controlled Clinical Trials

Schematic Diagram of a Clinical Trial

SAMPLE

Randomization to groups

Intervention group Control group

Measure outcome Measure outcome

Lost to follow-up

Nonparticipants

Clinical Trial Crossover Designs

Any change of treatment for a patient in a clinical trial involving a switch of study treatments.

Planned crossovers Protocol is developed in

advance, and the patient may serve as his or her own control.

Unplanned crossovers Exist for various reasons,

such as patient’s request to change treatment.

Members of both groups receive both treatment regimens

Group 1 receives treatment A then treatment B

Group 2 receives treatment B then treatment A

Treatment A

Treatment B

Advantages Represents the only way to

estimate directly the impact of change in behavior or modifiable exposure on the incidence of disease.

Community intervention trials determine the potential benefit of new policies and programs.

Community refers to a defined unit, e.g., a county, state, or school district.

Limitations Inferior to clinical trials with

respect to ability to control entrance into study, delivery of the intervention, and monitoring of outcomes.

Fewer study units are capable of being randomized, which affects comparability.

Affected by population dynamics, secular trends, and nonintervention influences

Community Trials

Community Trials - Steps Community trials start by:

Determining eligible communities and their willingness to participate

Collect baseline measures of the problem to be addressed in the communities, e.g., disease rates, knowledge, attitudes, and practices

Communities are randomized (intervention and control)

Followed over time Outcomes of interest are measured