Podcast
Questions and Answers
What does a correlation coefficient (r) of 0 indicate?
What does a correlation coefficient (r) of 0 indicate?
- A moderate positive correlation
- A perfect negative correlation
- A perfect positive correlation
- No linear relationship between the variables (correct)
Low variability in data enhances the ability to detect correlations.
Low variability in data enhances the ability to detect correlations.
False (B)
If a correlation coefficient (r) is 0.60, what would be the value of the coefficient of determination (r²)?
If a correlation coefficient (r) is 0.60, what would be the value of the coefficient of determination (r²)?
0.36
The coefficient of determination, r², represents the proportion of ______ in one variable that is explained by the other variable.
The coefficient of determination, r², represents the proportion of ______ in one variable that is explained by the other variable.
Match the following terms related to correlation with their descriptions:
Match the following terms related to correlation with their descriptions:
Which of the following scales of measurement involves ordered categories with equal intervals?
Which of the following scales of measurement involves ordered categories with equal intervals?
Differential research involves comparing groups that are created by the researcher.
Differential research involves comparing groups that are created by the researcher.
What is a crucial element of an experimental design that ensures the change in the dependent variable is due to the independent variable?
What is a crucial element of an experimental design that ensures the change in the dependent variable is due to the independent variable?
In a time-series research, observations are compared from one time versus those made at ___________.
In a time-series research, observations are compared from one time versus those made at ___________.
Match the following scales of measurement with their descriptions:
Match the following scales of measurement with their descriptions:
Which of the following is an example of a ratio scale?
Which of the following is an example of a ratio scale?
Correlational designs can definitively establish causation between variables.
Correlational designs can definitively establish causation between variables.
In experimental designs, what is the main purpose of randomisation to conditions?
In experimental designs, what is the main purpose of randomisation to conditions?
A factor loading of +.35 is generally considered acceptable for inclusion in a factor.
A factor loading of +.35 is generally considered acceptable for inclusion in a factor.
What is the minimum number of items recommended for a factor to ensure adequate reliability?
What is the minimum number of items recommended for a factor to ensure adequate reliability?
Describe two key considerations when deciding whether to eliminate an item from a factor analysis.
Describe two key considerations when deciding whether to eliminate an item from a factor analysis.
The principle that suggests adding more items to a factor beyond a certain point provides diminishing returns is known as the ______ of diminishing returns.
The principle that suggests adding more items to a factor beyond a certain point provides diminishing returns is known as the ______ of diminishing returns.
Which of the following factor loadings is considered 'very good' according to Comrey & Lee (1992)?
Which of the following factor loadings is considered 'very good' according to Comrey & Lee (1992)?
Match the following factor loading ranges with their corresponding descriptions:
Match the following factor loading ranges with their corresponding descriptions:
Factor analysis is a purely objective process that relies solely on mathematical calculations.
Factor analysis is a purely objective process that relies solely on mathematical calculations.
Why is it important to consider the interpretability of a factor structure?
Why is it important to consider the interpretability of a factor structure?
What does ANOVA stand for?
What does ANOVA stand for?
A t-test can only compare two means at a time.
A t-test can only compare two means at a time.
What is the purpose of using a paired samples t-test?
What is the purpose of using a paired samples t-test?
In ANOVA, the __________ variable is referred to as a factor.
In ANOVA, the __________ variable is referred to as a factor.
Match the following terms with their correct definitions:
Match the following terms with their correct definitions:
What is the primary focus of a within-subjects experimental design?
What is the primary focus of a within-subjects experimental design?
The F-value in ANOVA represents the ratio of within-group variance to between-group variance.
The F-value in ANOVA represents the ratio of within-group variance to between-group variance.
What is the difference between a single-factor design and a factorial design?
What is the difference between a single-factor design and a factorial design?
What is the recommended aim for the percentage of variance explained when conducting a factor analysis?
What is the recommended aim for the percentage of variance explained when conducting a factor analysis?
The first factor extracted in factor analysis always explains the least amount of variance.
The first factor extracted in factor analysis always explains the least amount of variance.
What should be done when factors no longer represent useful clusters or variables?
What should be done when factors no longer represent useful clusters or variables?
A ______ plot is used to depict the amount of variance explained by each factor.
A ______ plot is used to depict the amount of variance explained by each factor.
What describes orthogonal rotation in factor analysis?
What describes orthogonal rotation in factor analysis?
Name one type of rotation used in factor analysis.
Name one type of rotation used in factor analysis.
Which statement is true regarding factor loadings?
Which statement is true regarding factor loadings?
Match the type of rotation with its description:
Match the type of rotation with its description:
Which assumption of ANOVA pertains to the requirement that the variances of different groups are equal?
Which assumption of ANOVA pertains to the requirement that the variances of different groups are equal?
Exploratory Factor Analysis (EFA) is used to confirm existing hypotheses about factor structures.
Exploratory Factor Analysis (EFA) is used to confirm existing hypotheses about factor structures.
What is the goal of conducting a factor analysis?
What is the goal of conducting a factor analysis?
The ______ test is a non-parametric alternative to ANOVA used when data does not meet the assumptions of normality.
The ______ test is a non-parametric alternative to ANOVA used when data does not meet the assumptions of normality.
Match the following regression analysis terms with their correct definitions:
Match the following regression analysis terms with their correct definitions:
Which of the following factors is not an assumption of a regression model?
Which of the following factors is not an assumption of a regression model?
Correlation implies causation between two variables.
Correlation implies causation between two variables.
What is a scree plot used for in factor analysis?
What is a scree plot used for in factor analysis?
Flashcards
Quasi-Experimental Research
Quasi-Experimental Research
Research that resembles true experiments but lacks random assignment.
Differential Research
Differential Research
Study comparing pre-existing groups like gender or handedness.
Time-Series Research
Time-Series Research
Comparison of observations at different times, like before and after effects.
Nominal Scales
Nominal Scales
Signup and view all the flashcards
Ordinal Scales
Ordinal Scales
Signup and view all the flashcards
Interval Scales
Interval Scales
Signup and view all the flashcards
Ratio Scales
Ratio Scales
Signup and view all the flashcards
Parametric vs Non-parametric Tests
Parametric vs Non-parametric Tests
Signup and view all the flashcards
Random Variation
Random Variation
Signup and view all the flashcards
% of Variance Explained
% of Variance Explained
Signup and view all the flashcards
Correlation Coefficient (r)
Correlation Coefficient (r)
Signup and view all the flashcards
Coefficient of Determination (r²)
Coefficient of Determination (r²)
Signup and view all the flashcards
Linear vs Nonlinear Relationships
Linear vs Nonlinear Relationships
Signup and view all the flashcards
Meaningful Interpretation of Factors
Meaningful Interpretation of Factors
Signup and view all the flashcards
Variance Explained
Variance Explained
Signup and view all the flashcards
Scree Plot
Scree Plot
Signup and view all the flashcards
Factor Loadings (FLs)
Factor Loadings (FLs)
Signup and view all the flashcards
First Factor Extraction
First Factor Extraction
Signup and view all the flashcards
Orthogonal Rotation
Orthogonal Rotation
Signup and view all the flashcards
Oblique Rotation
Oblique Rotation
Signup and view all the flashcards
Interpretation of Unrotated Factors
Interpretation of Unrotated Factors
Signup and view all the flashcards
T-test
T-test
Signup and view all the flashcards
Independent Samples T-test
Independent Samples T-test
Signup and view all the flashcards
Paired Samples T-test
Paired Samples T-test
Signup and view all the flashcards
ANOVA
ANOVA
Signup and view all the flashcards
Between-Subjects Design
Between-Subjects Design
Signup and view all the flashcards
Within-Subjects Design
Within-Subjects Design
Signup and view all the flashcards
Factor in ANOVA
Factor in ANOVA
Signup and view all the flashcards
F-value in ANOVA
F-value in ANOVA
Signup and view all the flashcards
Assumptions of ANOVA
Assumptions of ANOVA
Signup and view all the flashcards
Non-parametric alternative to ANOVA
Non-parametric alternative to ANOVA
Signup and view all the flashcards
Goal of Factor Analysis
Goal of Factor Analysis
Signup and view all the flashcards
Exploratory vs Confirmatory Factor Analysis
Exploratory vs Confirmatory Factor Analysis
Signup and view all the flashcards
Eigenvalue in Factor Analysis
Eigenvalue in Factor Analysis
Signup and view all the flashcards
Mediator vs Moderator Variables
Mediator vs Moderator Variables
Signup and view all the flashcards
Assumptions of Regression Model
Assumptions of Regression Model
Signup and view all the flashcards
Factor Loadings
Factor Loadings
Signup and view all the flashcards
Cut-off for Loadings
Cut-off for Loadings
Signup and view all the flashcards
Cross Loadings
Cross Loadings
Signup and view all the flashcards
Interpretability
Interpretability
Signup and view all the flashcards
Reliability in Factors
Reliability in Factors
Signup and view all the flashcards
Simple Factor Structure
Simple Factor Structure
Signup and view all the flashcards
Law of Diminishing Returns
Law of Diminishing Returns
Signup and view all the flashcards
Minimum Items per Factor
Minimum Items per Factor
Signup and view all the flashcards
Study Notes
Introduction to Statistics
- Statistics is a set of methods and rules for organizing, summarizing, and interpreting information.
- Statistics can help condense large amounts of information into simpler figures or statements.
- Researchers are often interested in specific groups of individuals or events (e.g., children, Americans, left-handed people).
Populations and Samples
- A population includes every individual or event of interest.
- A parameter is a characteristic of a population, such as the average age.
- A sample is a subset of the population, intended to represent the population.
- A statistic is a characteristic of a sample, such as the average age of people in a sample group.
Descriptive Statistics
- Descriptive statistics simplify and summarize data using graphs, charts, and averages.
Inferential Statistics
- Inferential statistics allow researchers to study samples and make generalizations about the population.
- Techniques include Wilcoxon, chi-square, and correlations.
- Researchers need to consider sampling error when making generalizations about populations from samples.
Research Methods
- Correlational Research: Observes relationships between variables as they naturally exist (e.g., personality and executive performance). This method cannot determine cause-and-effect.
- Experimental Research: Establishes cause-and-effect relationships by manipulating one variable (the independent variable) to observe its effect on another (the dependent variable). Researchers try to control extraneous factors.
Experimental Research in Detail
- Independent Variable: The variable that is manipulated
- Dependent Variable: The variable measured to assess the effect of the independent variable
- Control Group: A group that does not receive the experimental treatment (often a placebo)
- Confounding Variables: Uncontrolled variables that could affect results.
- Quasi-Experimental Research: Similar to true experiments but lacks full experimental control. Useful when complete experiments are impossible or unethical.
- Pre- and post-testing: Observing the dependent variable before and after the experimental manipulation to assess any change.
Scales of Measurement
- Nominal: Data categorized into distinct groups (e.g., eye colour)
- Ordinal: Categorized with an ordered sequence (e.g., ranks, ordinal ratings)
- Interval: Ordered categories with equal intervals but no true zero point (e.g., temperature)
- Ratio: Ordered categories with equal intervals and a true zero point (e.g., weight)
Recap of Questions
- Identify parametric and non-parametric test types.
- Understand differences between parametric and non-parametric tests.
- Describe the function of correlations and when to use different correlation types.
- Discuss when using t-tests, including the different types, and necessary assumptions.
- Summarize important assumptions for using t-tests.
Correlation and Regression
- Correlation: Measures the strength and direction of the relationship between two continuous variables (e.g., height and weight).
- Regression: Predicts the value of one variable based on another. Linear regression predicts a single outcome based on a single predictor variable, while multiple regression uses multiple predictor variables.
Types of ANOVA
- Between-Subjects ANOVA: Compares means from multiple independent groups
- Within-Subjects ANOVA: Compares means from the same group under multiple conditions
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.