Comprehensive Guide to Experimental Design and Causality

Definition of Experimental Research: Experimental research designs are particularly effective for isolating causal relationships due to their high degree of internal validity.
- Internal Validity: This validity originates from the researcher’s capacity to control exposure to an experimental treatment, enabling confident inferences about causality.
- Mechanism: By manipulating the independent variable and observing its effect on the dependent variable, experiments determine whether changes in the independent variable directly cause changes in the dependent variable while minimizing confounding factors.

Gold Standard: The classical randomized experiment is recognized as the gold standard in experimental research due to its rigorous structure characterized by five fundamental features:
- Treatment and Control Groups:
- Presence of at least one treatment group (receiving experimental manipulation) and one control group (not receiving the manipulation), serving as a baseline comparison.
- Random Assignment:
- Participants are randomly allocated to either the treatment or control group, aiding in the prevention of self-selection bias, thereby ensuring that groups are comparable from the outset.
- Controlled Administration of Treatment:
- Researchers dictate the timing and conditions under which the treatment is delivered.
- Pre-Test and Post-Test Measurements:
- Measurement of the dependent variable before and after the treatment, allowing attribution of differences observed to the treatment effect, provided other variables are controlled.
- Control Over the Experimental Environment:
- Management of time, location, and physical conditions to mitigate extraneous influences on experimental outcomes.
- Enhancement of Internal Validity: Collectively, these features bolster the internal validity of the experiment, facilitating strong causal inferences.

Despite aiming for high internal validity, multiple factors can jeopardize this:
- History: External events occurring between pre-test and post-test can influence outcomes independently from the treatment.
- Maturation: Natural, inevitable changes in subjects over time that could skew results, such as learning or fatigue effects, particularly in lengthy studies.
- Test-Subject Interaction: Participants' responses may be influenced by the act of measurement itself before the treatment.
- Selection Bias: Differences between volunteers and non-volunteers or between groups that arise not through random assignment can distort results.
- Experimental Mortality: Non-random dropouts can introduce bias if specific types of participants are more likely to leave the study.
- Demand Characteristics: If participants conjecture the study's purpose, they might alter their behavior, impacting results.
Importance of Mitigation: Awareness and strategies to mitigate these threats are vital for maintaining the internal validity of experimental findings.

Resembles the classical randomized experiment but omits the pre-test.
- Assumption: The sample is sufficiently large and randomly selected, allowing treatment and control groups to be thought equivalent at baseline.
- Post-Test Measurement: The dependent variable is evaluated following the treatment, attributing differences to the treatment's effect.

Involves multiple measurements both before and after the treatment (several pre-tests and post-tests).
- Purpose: This design aims to assess immediate and long-term effects of the treatment employing multiple assessments of the dependent variable.
- Trend Identification: It helps identify trends over time and manage variability in individual responses.

This design implements more than two groups, each receiving distinct treatments or varying doses.
- Richer Data Comparisons: It enables simultaneous comparisons of different treatment effects, offering extensive data about the efficacy or differential impacts of interventions.

Conducted in real-world contexts rather than controlled environments.
- Characteristics:
- Researchers lack control over group assignments but can manipulate independent variables.
- Enhances external validity, assessing causal effects in natural settings while potentially sacrificing some internal controls.
- Example: Evaluating a new group management strategy in different organizations to observe its impact on employee productivity without random assignment while maintaining ecological validity.

Definition: A research question is a clear, focused, and specific inquiry identifying the primary issue a study aims to clarify.
Role: It guides research design, data collection, and analysis, generating meaningful insights.

Definition: A systematic examination and interpretation of existing literature relevant to a topic, providing the foundation for future research.
Purpose: To inform further research work and identify gaps in current knowledge.

A quality assurance mechanism where experts evaluate a scholarly work before publication or funding to assess its quality, validity, and contribution.
- Types of Peer Review:
- Blind Review (Single Blind): Reviewers know the author's identity; the author does not know the reviewers.
- Double Blind Review: Neither authors nor reviewers know each other's identities, promoting impartiality.
- Triple Blind Review: Reviewers, authors, and editors are all unaware of each other's identities to minimize bias.

Definition: Organized electronic collections of scholarly sources searchable for reliable information aiding academic and professional research.
Examples: JSTOR, PubMed, Scopus, Web of Science, Google Scholar.

Concept: A variable is a concept that varies, permitting measurement and comparison across different observations.
Types of Variables:
- Quantitative Variables: Measure numerical quantities (e.g., income, age).
- Qualitative Variables: Describe categories or attributes (e.g., gender, type).

Definition: A variable thought to influence, affect, or cause variation in another variable.
- Role: It is manipulated or observed for its effect on the dependent variable.
- Example: In studies relating income to health, income acts as the independent variable.

Definition: A variable that depends upon changes in the independent variable.
- Purpose: It reflects the outcome resulting from variations in the independent variable.
- Example: In the income-health relationship, health status is the dependent variable.

Variable Relationships: Understanding the relationship between variables is crucial for scientific inquiry, where the independent variable is presumed to influence the dependent variable.
The independence of variables facilitates a focused approach to establishing causal relationships in research designs, contributing to valid conclusions in studies.