Experimental and Non-Experimental Study Designs

• Researchers manipulate an independent variable to observe its effect on a dependent variable during experimental study designs
• Experimental study designs aim to establish cause-and-effect relationships
• Key features of experimental study designs are intervention/treatment, a control group, random assignment, and manipulation of the independent variable
• True experimental designs include random assignment to groups, such as a pretest-posttest control group
• Quasi-experimental designs lack random assignment but still manipulate the independent variable
• Non-experimental study designs involve researchers observing and measuring variables without intervention
• Non-experimental study designs are used to describe relationships, trends, or characteristics without establishing causality
• Key features of non-experimental study designs are no intervention or manipulation, often used for descriptive or correlational research
• Descriptive designs measure variables as they exist, like patient satisfaction surveys
• Correlational designs examine relationships between variables, such as stress levels and heart disease

Experimental vs. Non-Experimental Studies Comparison

• Experimental studies include intervention, non-experimental studies do not
• Random assignment exists in experimental studies, and does not in non-experimental studies
• Causality can be determined in experimental studies, while only associations can be determined in non-experimental studies
• Control groups are usually present in experimental studies, but are typically absent in non-experimental studies

Steps to Conducting Experimental Research

• The first step is to identify the research problem, and defining the problem and research purpose
• The next step is selecting a research design; choosing between true experimental, quasi-experimental, or factorial designs
• A testable cause-and-effect hypothesis must be formulated
• Ensure the sample represents the population using random selection
• Participants are randomly placed in control or treatment groups via random assignment
• Define the manipulated factor (independent variable, example: a new drug)
• Define the outcome being measured (dependent variable, example: pain levels)
• Ensure control by using control groups to compare results and eliminate extraneous variables
• Administer the treatment to the experimental group for intervention
• Collect data using reliable and valid instruments such as surveys, lab tests, observational checklists
• Conduct statistical tests (t-tests, ANOVA, regression analysis) to determine the effect of the intervention
• Assess whether the intervention had a significant impact when interpreting results
• Report findings and discuss implications for practice to draw conclusions
• Ensure ethical compliance by obtaining informed consent and securing Institutional Review Board (IRB) approval

Uniqueness of Quantitative Strategies

• Ensure a representative sample by random sampling during the recruitment and sampling stage
• Define who can participate using inclusion/exclusion criteria
• Use power analysis to calculate the necessary number of participants to determine sample size
• Structured methods need to be ensured, to ensure standardization and consistency when collecting data
• Data can be collected via surveys and questionnaires, experiments (drug trials), observational checklists, and physiological measures (blood pressure monitoring)

Data Analysis

• Statistical Conclusion Validity determines the accuracy of statistical findings
• Common analyses include descriptive statistics (mean, median, standard deviation) and inferential statistics (regression analysis, hypothesis testing)

Ethical Considerations

• Confidentiality and Privacy is a key consideration, and requires protecting participant data under HIPAA regulations
• Minimizing bias requires the use of randomization and blinding techniques

Strengthening Study Validity and Reliability

• Internal Validity ensures that changes in the dependent variable stem from the independent variable, not extraneous factors
• History effects, maturation, and testing effects are all threats to internal validity
• Random assignment and control groups are solutions to internal validity
• External Validity determines whether findings can be generalized beyond the study population
• Sample homogeneity and small sample size are threats to external validity
• Increase sample size and use random selection to ensure external validity Construct Validity: Ensures that a study accurately measures the concept it intends to study
• Poorly defined variables threaten construct validity
• Solutions: Use validated measurement tools
• Statistical Conclusion Validity: Ensures correct statistical tests were applied
• Low statistical power due to small sample sizes can be a threat
• Solutions: Use power analysis to determine sample size
• Standardized procedures, pilot testing, and blinding techniques improves reliability
• Single-Blind: Participants don't know if they are in treatment or control group
• Double-Blind: Neither participants nor researchers know group assignments
• Confirm findings using multiple observations, using repeated measures, and reduce measurement errors by training data collectors

Practical Strategies to Strengthen Study Designs

• Randomization reduces selection bias, ensures balanced groups
• Use of a control group provides a baseline for comparison
• A large sample size increases external validity
• Clear variable definitions prevent construct validity threats
• Blinding reduces experimenter bias
• Experimental involves intervention, control, and randomization, while non-experimental focuses on observation without manipulation
• Identify the problem, select the design, randomize participants, define variables, apply intervention, collect data, analyze results, ensure ethics
• Control variables, increase sample size, use blinding, standardize methods, and validate instruments when needing to strengthen validity & reliability
• Sampling, structured data collection, statistical analysis, and bias reduction must occur in order for quantitative strategies to be unique

Explore experimental, quasi-experimental, and non-experimental study designs. Understand how researchers manipulate variables in experimental designs to establish cause-and-effect relationships. Learn about the features of each design type, including random assignment and the role of control groups.