08

Page 1: Understanding the Cambridge Effect

• According to research from Cambridge University:

  • The order of letters in a word does not hinder reading ability as long as the first and last letters are correctly placed.

  • This phenomenon suggests that the human mind processes words as whole units rather than individual letters.

  • It showcases how cognitive processing of words can influence reading speed and comprehension.

Page 2: Factors Influencing the Cambridge Effect

• Potential factors that might contribute to the Cambridge Effect include:

  • Age of the reader.

  • Language proficiency.

  • Context in which the reading occurs.

  • Complexity of vocabulary used.

  • Learning disabilities (LDs) present in the reader.

  • Length of sentences.

Page 3: Research Details on Jumble Types

• Research from Linneatrain University includes:

  • Independent Variables (IVs):

    • IV1: Type of jumble (random letters vs. "Cambridge" jumble) - Randomly selected letters versus coding to keep double letters together.

    • IV2: Grammatical complexity (typical vs. advanced).

  • Measurements taken based on WP complexity scale:

    • Typical = average newspaper reading level.

    • Advanced = average journal reading level.

  • IV3: Vocabulary complexity measured across grade levels (Grade 5, Grade 8, Grade 11).

Page 4: Experimental Design II - Factorial Designs

• Chapter Objectives:

  • Explain factorial designs with standard notation (e.g., 2x2, 3x5).

  • Accurately place data into a factorial matrix and calculate row/column means.

  • Define and identify main effects in factorial designs.

  • Understand and determine interaction effects within experimental data.

Page 5: Interpreting Interactions in Factorial Designs

• Know how to:

  • Interpret interactions and recognize their impact on main effects.

  • Describe Jenkins and Dallenbach’s (1924) study on sleep and memory in terms of interactions.

  • Position factorial designs in context with single-factor designs discussed in Chapter 7.

Page 6: Mixed Factorial Designs

• Objectives:

  • Identify mixed factorial designs and explain counterbalancing applicability.

  • Explain PxE factorial designs and their evaluation of main effects and interactions.

  • Differentiate mixed PxE designs from simple PxE factorial designs.

Page 7: Participant Requirements for Factorial Designs

• Objectives:

  • Calculate the number of participants necessary for various factorial designs.

  • Highlight ethical responsibilities as an experimenter.

Page 8: Essentials of Factorial Designs

• Definition of Factorial Design:

  • Involves more than one independent variable (IV).

  • Each IV is referred to as a “factor.”

• Notation System:

  • Digits indicate the number of independent variables; numerical values indicate the levels of each IV.

  • Example: A 2x3 factorial is comprised of 6 total conditions (2 IVs, one with 2 levels and one with 3).

Page 9: Identifying Factorial Designs

• Factorial Matrix:

  • Example: 2x2 matrix includes two levels each for types of training and presentation rates.

Page 10: Main Effects and Interactions

• Main Effects:

  • Measure the overall effect of individual Independent Variables, such as types of training.

  • Example: Comparison of data from different training methods to find overall effectiveness.

Page 11: Further Exploration of Main Effects

• Presentation Rate:

  • Evaluate the effect of presentation rates on outcomes by comparing means from different conditions.

Page 12: Calculating Means for Hypothetical Data

• Calculation examples for row and column means using sample data:

  • Row mean for imagery training = 20, rote training = 15.

  • Column mean for 2-sec presentation rate = 14.5, and 4-sec rate = 20.5.

Page 13: Implications of Main Effects

• Example:

  • Imagery training results in better recall compared to rote training (M = 20 vs. M = 15).

  • A longer presentation rate also correlates with better recall (M = 20.5 vs. M = 14.5).

Page 14: Example of Main Effects

• Research Example 19:

  • Analyzed influence of gender of raters and time periods on ratings:

    • Men rating women receive higher scores (6.2) than women rating men (5.2).

    • Rating trends by time show variability (highest at 12:00).

Page 15: Graphical Representation of Research Example 19

• Visual representation of men's ratings versus women's ratings across different time periods shows clear trends.

Page 16: Understanding Interactions

• Interactions occur when the effect of one IV depends on the level of another IV.

• Example: Impacts of course emphasis on performance can vary across different subjects (no main effects).

Page 17: Exploring Interactions Further

• Demonstrates how lab vs. lecture emphasizes impacts performance based on students' majors.

Page 18: Major vs. Minor Performance Ratings

• Performance differences based on course emphasis:

  • Science majors perform better in labs, while humanities majors excel in lecture formats.

Page 19: Conditional Interactions in Studies

• Research Example 20:

  • Investigating study and test conditions reveals matching conditions optimize memory recall.

Page 20: Interactions Surpassing Main Effects

• Not all main effects are significant; interactions might be the key finding in studies.

Page 21: Interactions with Main Effects

• Scenarios where one IV has a significant effect (e.g., imagery instructions) but does not yield interaction effects.

Page 22: Interaction Patterns and Main Effects

• Example: No interaction observed when only one IV yields a significant main effect.

Page 23: Combinations of Main Effects

• Scenario where both main effects are significant but interactions do not play a role.

Page 24: Importance of Main Effects

• Combination of main effects and interactions can indicate the relevance of each effect in different settings.

Page 25: Relevant Combinations of Effects

• Some main effects gain significance in the presence of evident interactions.

Page 26: Graphs in Interaction Analysis

• Line graphs can showcase interactions; nonparallel lines indicate that interactions exist between IVs.

Page 27: Study of Retroactive Interference

• Famous 1924 Study:

  • Explored effects of sleep deprivation on recall under a 2x4 repeated measures factorial design.

Page 28: Decision Tree for Factorial Designs

• Flowchart for understanding factorial IVs, focusing on whether they are between-group or within-subject factors.

Page 29: Mixed Factorial Designs Overview

• Importance of understanding how mixed factorials combine subject variables and manipulated variables.

Page 30: Research Example in Mixed Factorial Design

• Example 21:

  • 2x3 mixed factorial design focusing on the influence of instructions on political candidate evaluations.

Page 31: Further Research Example in Mixed Designs

• Example 22:

  • Mixed factorial design exploring health measures after varying instructions and testing conditions influences.

Page 32: Understanding PxE Designs

• P x E Designs defined with person factors and environmental factors illustrating their interactions.

Page 33: To Consider for E Factor Effects

• Different influences of environmental factors affecting performance metrics across personality types.

Page 34: Interactions in Educational Research

• P x E interactions can provide insight into educational strategies and effectiveness.

Page 35: Research Example in Educational Context

• Example 23: Studies how gender influence on responses in group conditions affects test outcomes.

Page 36: Age-Related Research Example

• Example 24: Findings show age-related differences in driving performance, emphasizing significant effects need evaluation.

Page 37: Necessary Participants for Factorial Designs

• Calculation of required participants varies per design type using a numbered approach for clarity in recruiting.

Page 38: Analyzing Factorial Designs

• Overview of factorial ANOVAs and simple effects analyses, with historical context connecting to Fischer’s factorial design origins.

Page 39: Summary of Factorial Designs

• Factorial designs are critical for evaluating effects of multiple IVs on DVs; understanding main effects and interactions is essential.

• Factorial ANOVAs provide the statistical framework to analyze results comprehensively, underscoring the ethical responsibilities of researchers.