Intervention Studies and Validity in Psychology Research
Overview
Psychology research systematically applies the scientific method to understand behavior and mental processes. This involves a cyclical process:
Observe a phenomenon: Identify an interesting pattern or problem.
Develop a general explanation (a theory): Formulate a broad conceptual framework to account for the observations.
Generate testable predictions: Deduce specific, falsifiable statements from the theory.
Test them in empirical studies: Design and conduct experiments or other research to collect data relevant to the predictions.
Refine or revise the theory: Based on the empirical findings, the theory is supported, modified, or rejected.
Key goals for this session: To deeply understand various psychological study types, the critical concepts of internal and external validity, and the specific factors that enhance or threaten these validities in research design.
Central ideas:
Theory: A comprehensive, overarching explanation for a set of observations or phenomena that applies broadly to many individuals, contexts, or outcomes. Theories provide a framework for understanding and generating new research questions.
Prediction: A specific, testable statement logically derived from a broader theory. Predictions guide the design of empirical studies.
Hypothesis: A very specific, falsifiable, and testable prediction that is formulated for a particular study, often stating an expected relationship between variables. It is the direct question a study aims to answer.
Strong treatment research: Aims to rigorously demonstrate that observed changes or improvements are directly caused by the specific treatment or intervention being administered, rather than by other confounding factors.
Example framing (depression): If a prevailing psychological theory posits that pessimistic thinking patterns significantly contribute to the onset and maintenance of depressive symptoms, then a therapeutic intervention designed to specifically teach individuals more optimistic cognitive restructuring techniques would be developed. The hypothesis would be that participants receiving this intervention will exhibit a measurable reduction in depressive symptoms compared to a control group, thereby testing the theory linking pessimistic thinking to depression.
Terminology to know: theory, hypothesis, experiment, prediction, treatment, internal validity, external validity.
Types of intervention studies (five main types)
Open trial
Description: This is a one-group treatment study where participants receive an intervention, and their symptoms or outcomes are measured both before (pre-treatment) and after (post-treatment) the intervention. There is no comparison group receiving an alternative treatment or no treatment.
Conclusion limitations: At best, an open trial can demonstrate that symptoms or conditions changed after treatment. However, it cannot definitively attribute any observed change directly to the treatment itself due to the absolute lack of a control group. We cannot rule out other factors.
Threats to validity: The primary threats include:
Placebo effects: Participants' expectations of improvement can lead to perceived or actual changes, irrespective of the treatment's specific active ingredients.
Spontaneous remission: Many conditions, especially psychological ones, may naturally improve over time without any intervention. An open trial cannot differentiate this natural course from treatment effects.
Dropout (attrition): Participants who drop out of the study may differ systematically from those who complete it, biasing the observed outcomes (e.g., only those who are improving remain).
Time/External events: Other life events or the mere passage of time could influence outcomes, making it impossible to isolate the treatment's effect.
Case study
Description: Involves an intensive, rich, and detailed description of a single participant, a small number of participants, or a specific unusual situation. It often includes historical data, observations, and relevant psychological test results.
Utility: Often used in the early stages of exploring a novel intervention, understanding rare conditions, or documenting an unusual clinical presentation. It can generate hypotheses for future research.
Validity: Internal validity is inherently low because there's no comparison (baseline or control) to establish causality, and external validity is severely limited due to the unique nature of the case(s), making generalizability to a broader population difficult.
Strengths: Highly informative descriptively, providing deep insights into complex phenomena that might be overlooked in larger studies.
Experiments (randomized controlled trials, RCTs)
Description: Considered the gold standard in intervention research for establishing causality. Participants are randomly assigned to either one or more treatment groups or to various control groups.
Causal Inference: This design is the best tool for rigorously determining whether the treatment caused the observed changes in outcomes. Randomization is key to this inference.
Internal Validity: Internal validity is high when randomization procedures are correctly implemented and appropriate control conditions are used. Random assignment helps ensure that groups are, on average, comparable at baseline on both known and unknown confounding variables.
Control conditions: Can include various types:
No-treatment control: Participants receive no intervention.
Active treatment control: Participants receive an established, alternative treatment.
Placebo control: Participants receive an inert intervention believed to have no specific therapeutic effect but mimics the treatment (e.g., sugar pill, supportive listening).
Standard care control: Participants receive the usual treatment provided in a clinical setting.
Quasi-experiments
Description: Involves two or more groups, similar to an experiment, but participants are not randomly assigned to these groups. Assignment occurs based on pre-existing conditions, natural events, or administrative criteria.
Examples: One common example is comparing outcomes between groups formed by their time of enrollment (e.g., those enrolled early vs. late), or pre-existing groups like students in different schools, patients in different clinics, or employees on different work shifts where an intervention is applied to one group but not another.
Internal Validity: Weaker than an RCT because the lack of random assignment means the groups may differ significantly at baseline on important characteristics (selection bias), which could confound the results. Any observed differences in outcomes might be due to these pre-existing group differences rather than the intervention itself. Sophisticated statistical methods may be employed to try to account for these baseline differences, but they can never fully replicate the power of randomization.
#### Single-participant experiments (single-subject designs)
Description: These designs intensely focus on one participant or a very small number of participants, often involving repeated measurements of behavior over time. The participant serves as their own control.
Design Example: An A-B-A (or reversal) design involves a baseline phase (A), followed by an intervention phase (B), and then a return to the baseline phase (A). If the behavior changes during B and reverts during the second A phase, it provides strong evidence for the intervention's effect on that individual.
High Internal Validity (for the individual): By systematically introducing and withdrawing the intervention, these designs can demonstrate a causal link between the intervention and the behavior change for that specific individual. Variability across settings or phases helps rule out alternative explanations.
Limited Generalizability: While strong for the individual, the findings are generally not generalizable to a wider population without replication across many individuals.
Internal validity: what it means and what threatens it
Definition: Internal validity refers to the degree of confidence that a study can unequivocally state that the observed changes in the dependent variable (outcome) are solely due to the independent variable (treatment or intervention), effectively ruling out all plausible alternative explanations or confounding factors for those observed changes.
Threats to internal validity (major ones):
Time (Maturation): Changes in participants due to natural growth, development, or spontaneous improvement/deterioration over the duration of the study, independent of the intervention. For example, in studies of depression, a significant percentage of individuals (e.g., “60-70% from a first depressive episode”) recover naturally over time, masking the true effect of a treatment if not controlled.
Selection (Group Differences at Start): Occurs when groups being compared (e.g., treatment vs. control) are not equivalent at the beginning of the study. If groups differ significantly in severity of symptoms, demographics, motivation, or other relevant factors at baseline, any observed outcomes may simply reflect these initial differences rather than the treatment effect. This is a common issue in quasi-experiments where randomization is absent.
Changes in External Events (History): Unforeseen external occurrences or events that happen during the course of the study that can influence outcomes. These events (e.g., major news events like an economic recession, a natural disaster, implementation of new social policies, or even seasonal changes like weather) might affect one group more than another, or all groups in a way that confounds the treatment effect. For instance, a new public health campaign launched during a study could influence health behaviors, making it hard to attribute changes solely to the study's intervention.
Attrition/Dropout (Mortality): The loss of participants from the study. This threat becomes particularly damaging if dropouts occur differentially across groups or are systematically related to the outcome. For example, if more severely affected individuals drop out of the control group, the control group might appear to improve more than it truly did, biasing the comparison with the treatment group.
Placebo effects and Expectancy: Participants' and/or researchers' beliefs and expectations about the treatment's effectiveness can significantly influence outcomes, independent of the active ingredients of the intervention.
Participant Expectancy (Placebo Effect): Participants expecting to get better might report improvements simply due to that belief, even if they receive an inert intervention.
Researcher Expectancy (Rosenthal Effect or Observer Bias): Researchers treating participants or assessing outcomes might unconsciously (or consciously) act in ways that favor the expected outcome, influencing participant behavior or biased data collection/interpretation.
Baseline Variability: High heterogeneity (wide range of severity, characteristics) in participants at the start of the study can obscure or exaggerate average improvements. A treatment might work well for a specific subgroup but appear ineffective on average if the sample is too diverse, making it difficult to draw clear conclusions about its efficacy for individuals.
How to improve internal validity:
Random Assignment: This is the most powerful tool. By randomly assigning participants to treatment and control groups, researchers aim to distribute both known and unknown confounding variables equally across groups, thereby making the groups statistically equivalent at baseline and minimizing selection bias.
Appropriate Control Condition: Design a control condition that isolates the active ingredient of the treatment. For example, using a credible placebo or an active comparison treatment allows researchers to disentangle the specific effects of the new intervention from general factors like attention, therapeutic alliance, or natural remission.
Monitor and Report Dropout Rates: Carefully track participant attrition. Analyze whether dropout rates differ significantly between groups and assess if dropouts have different baseline characteristics or outcomes compared to completers. Statistical methods (e.g., intention-to-treat analysis) can help address potential bias from attrition.
Balance Timing and Exposure to External Events: Strive to ensure that all study groups experience similar external events and are treated during comparable time periods. This minimizes the chance that extraneous historical events confound the results.
Blinding (Masking): Where feasible, keep participants and/or researchers unaware of who is receiving the active treatment versus the control condition. This significantly reduces expectancy effects and evaluator bias:
Single-blind: Participants do not know their group assignment.
Double-blind: Neither participants nor the researchers directly interacting with them or assessing outcomes know the group assignment.
External and Internal validity (generalizability)
Definition: External validity refers to the extent to which the findings and conclusions of a study can be confidently applied, generalized, or extended to real-world settings, different populations beyond the study sample, and diverse contexts (e.g., other geographical locations, other time periods, varying clinical environments).
Definition: Internal validity: Refers to the degree to which an experiment reliably demonstrates a causal relationship between the independent and dependent variables, essentially ruling out alternative explanations for the observed effects.