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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.