CH10 Simple Experiments Cards
Chapter 10: Simple Experiments
Page 1: Title
Chapter 10: Simple Experiments
Page 2: Today's Plan
2 Variables Refresher: An overview of the key concepts of independent and dependent variables in experimental research.
Revisiting Causality with a Focus on Internal Validity: Exploring the relationship between causation and various forms of validity in experiments.
Design Types and Considering Pretest/Posttest, Posttest Only: Discussion on the different experimental designs, including their strengths and weaknesses, and how pretesting can influence the results.
Interrogating Causal Claims: Methods to critically analyze causal claims made in psychological research.
Page 3: Learning Objectives
Establish causation through experiments: Understanding how to apply three primary criteria: covariance (the extent to which variables change together), temporal precedence (the order of cause and effect), and internal validity (ensuring no alternative explanations for results).
Identify variables: Clearly delineate between independent variables (IVs), dependent variables (DVs), and control variables in experimental designs.
Classify experiment designs: Recognize the differences between independent-groups designs (different subjects for different conditions) and within-groups designs (same subjects across all conditions).
Evaluate threats to internal validity: Factors such as design confounds (uncontrolled variables that might influence results), selection effects (biases in sample selection), and order effects (the impact of sequence of conditions) will be analyzed.
Interrogate experimental design using four validities: Focus on construct, external, statistical, and internal validity to assess the quality of the research.
Page 4: Variables Review
Page 5: Experimental Variables
Experiment: Involves the manipulation of one or more variables and the measurement of their effects using random assignment to conditions.
Manipulated Variable: The research assigns different levels to this variable (e.g., comparing different notetaking methods, like computer versus longhand).
Measured Variable: This variable records the outcomes of the manipulation (e.g., number of anagrams solved by participants).
Independent Variable (IV): The variable that is purposely manipulated in the experiment.
Dependent Variable (DV): The variable that is measured to assess the effects of the IV.
Page 6: Control Variables
Control Variable: A variable that is held constant throughout the experiment to eliminate its potential impact on the DV.
Clarification:
Variable: Changes or is measured during the study.
Constant: A variable that does not change throughout the duration of the study.
Page 7: Simple Experiment Example 1: Taking Notes
Study by Pam Mueller & Daniel Oppenheimer (2014): This influential study compared the effectiveness of taking notes on laptops versus notebooks in classroom settings.
Method: Participants watched TED Talks, took notes using their assigned method, engaged in a filler activity, and were subsequently quizzed on the material.
Page 8: Example Q&A
Independent variables count?: Focus on identifying and distinguishing between the independent and dependent variable in the context of this study.
Causal claims analysis: Critically analyze the causal claims stemming from the experiment's findings.
Page 9: Simple Experiment Example 2: Motivating Babies
Study with 100 babies aged 13-18 months: Investigated the influence of motivation on behavior through two conditions: "effort" (where babies had to press a button to access toys) versus "no-effort" (toys readily available).
Methodology: Behavior was recorded based on the time spent with toys and the number of attempts made to press the button.
Page 10: Example Q&A
Identifying independent variables: Questions aimed at discerning how independent variables are established in this observational context.
Causal claims evaluation: Further scrutiny of the causal relationships presented in the findings.
Page 11: Revisiting Causality
A detailed examination of internal validity and its significance in establishing true causation in experimental designs.
Page 12: Why Experiments Support Causal Claims
Establishing three crucial criteria: Covariance (the correlation between the IV and DV), temporal precedence (the timing of the IV in relation to changes in the DV), and internal validity (ensuring there are no third-variable confounds).
Careful evaluation of potential confounds is essential.
Page 13: Experiments Establish Covariance
The necessity of comparison groups, which include both control groups (no treatment) and treatment groups (receiving the IV).
Page 14: Experiments Establish Temporal Precedence
Importance of demonstrating that changes in the DV occur after manipulations of the IV, solidifying the causal direction.
Page 15: Internal Validity
The critical need for ruling out third-variable explanations (e.g., alternative explanations for the outcomes in the notetaking study that don't involve the IV directly).
Page 16: Design Confounds
Definition and impact of systematic variability that can lead to alternative explanations for the results obtained in the study.
Page 17: Selection Effects
An exploration of how participants systematically differ across levels of the IV, potentially skewing results.
Page 18: Avoiding Selection Effects
Utilize matched groups to ensure participants are comparable based on certain characteristics, particularly in small sample sizes.
Page 19: Notetaking Study Internal Validity
A comprehensive breakdown of the study's methodology to highlight strengths and weaknesses related to internal validity.
Page 20: Internal Validity Considerations
Consideration of participant demographics and preferences that may influence the results and their generalizability.
Page 21: Design Types
Distinction between between-subjects designs (independent groups) and within-subjects designs (repeated measures).
Page 22: Design Types Explained
Independent-groups Design: Different participants are assigned to various levels of the IV.
Within-groups Design: Same participants are subjected to all levels of the IV, enhancing reliability.
Page 23: Posttest-Only Design
An overview of designs that only test participants after they have been exposed to the IV.
Page 24: Pretest/Posttest Design
Discusses the framework involving random assignment to groups tested both before and after the intervention.
Page 25: Which Design Is Better?
Emphasizes the context-dependent nature of deciding which experimental design to adopt based on the research question at hand.
Page 26: Within-Groups Designs
Types include repeated-measures (same subjects under all conditions) and concurrent-measures (comparing different conditions in one trial).
Page 27: Advantages of Within-Groups Designs
Notable for controlling individual differences as each participant acts as their own control.
Page 28: Between vs. Within Comparison
A detailed overview of the advantages and disadvantages inherent in both types of designs to inform methodological choice.
Page 29: Evaluating Internal Validity in Within-Groups Designs
Analysis of order effects and methods to mitigate their influence on the results.
Page 30: Counterbalancing for Order Effects
Detailed discussion of full versus partial counterbalancing techniques to manage presentation order variations.
Page 31: Latin Square Design
Examination of how to utilize partial counterbalancing within factorial designs to control for order effects.
Page 32: Disadvantages of Within-Groups Designs
Considerations of potential order effects and demand characteristics that may arise as participants become aware of the experimental manipulation.
Page 33: Pretest/Posttest Design Clarification
Differences in exposure to the IV juxtaposed between different design frameworks to highlight implications on results.
Page 34: Interrogating Causal Claims
Validity considerations are outlined, highlighting the critical nature of assessing construct, external, statistical, and internal validity in research findings.
Page 35: Construct Validity Explained
Involves the quality of variable measurement and the effectiveness of manipulation within the experiment.
Page 36: External Validity Analysis
Generalizability to other populations and settings is discussed, highlighting factors that influence applicability.
Page 37: Statistical Validity Considerations
Discussion on the concepts of statistical significance and effect sizes, emphasizing the importance of rigorous statistical analysis in experimental research.
Page 38: Effect Size Measurement
Understanding and calculating effect sizes through Pearson's r and Cohen's d, providing insight into the practical significance of research findings.
Page 39: Internal Validity Questions
Critical considerations for ensuring internal validity throughout the experimental process are outlined, emphasizing the rigor required in designing experiments.
Page 40: Practice Questions
Pages 41-49: Clicker Questions
Interactive questions designed to reinforce learning and assess understanding of experimental concepts.
Page 50: Discussion Questions on Simple Experiments
Clarification of misconceptions related to control groups, the necessity of pretests, and the difference between random samples versus random assignment.