Correlation and Measurement Quiz
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Questions and Answers

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.

    False (B)

    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.

    <p>variance</p> Signup and view all the answers

    Match the following terms related to correlation with their descriptions:

    <p>Form = Linear or nonlinear relationship Degree = Strength and direction of the relationship r = Correlation Coefficient r² = Coefficient of determination</p> Signup and view all the answers

    Which of the following scales of measurement involves ordered categories with equal intervals?

    <p>Interval Scale (B)</p> Signup and view all the answers

    Differential research involves comparing groups that are created by the researcher.

    <p>False (B)</p> Signup and view all the answers

    What is a crucial element of an experimental design that ensures the change in the dependent variable is due to the independent variable?

    <p>control</p> Signup and view all the answers

    In a time-series research, observations are compared from one time versus those made at ___________.

    <p>another</p> Signup and view all the answers

    Match the following scales of measurement with their descriptions:

    <p>Nominal Scale = Data fall into different categories Ordinal Scale = Categories are organized in an ordered sequence Interval Scale = Ordered categories with equal intervals Ratio Scale = Interval scale with an absolute zero point</p> Signup and view all the answers

    Which of the following is an example of a ratio scale?

    <p>Length of an object (A)</p> Signup and view all the answers

    Correlational designs can definitively establish causation between variables.

    <p>False (B)</p> Signup and view all the answers

    In experimental designs, what is the main purpose of randomisation to conditions?

    <p>to reduce bias</p> Signup and view all the answers

    A factor loading of +.35 is generally considered acceptable for inclusion in a factor.

    <p>False (B)</p> Signup and view all the answers

    What is the minimum number of items recommended for a factor to ensure adequate reliability?

    <p>3 (D)</p> Signup and view all the answers

    Describe two key considerations when deciding whether to eliminate an item from a factor analysis.

    <p>Two key considerations are the size of the main factor loading and the size of cross-loadings. A large main loading and low cross-loadings are generally desirable.</p> Signup and view all the answers

    The principle that suggests adding more items to a factor beyond a certain point provides diminishing returns is known as the ______ of diminishing returns.

    <p>law</p> Signup and view all the answers

    Which of the following factor loadings is considered 'very good' according to Comrey & Lee (1992)?

    <blockquote> <p>.63 (D)</p> </blockquote> Signup and view all the answers

    Match the following factor loading ranges with their corresponding descriptions:

    <blockquote> <p>.70 = Excellent .63 = Very good .55 = Good .45 = Fair .32 = Poor</p> </blockquote> Signup and view all the answers

    Factor analysis is a purely objective process that relies solely on mathematical calculations.

    <p>False (B)</p> Signup and view all the answers

    Why is it important to consider the interpretability of a factor structure?

    <p>Interpretability is crucial because researchers need to understand and meaningfully explain the extracted factors. A well-interpreted factor structure allows for valid conclusions and meaningful insights.</p> Signup and view all the answers

    What does ANOVA stand for?

    <p>Analysis of Variance (C)</p> Signup and view all the answers

    A t-test can only compare two means at a time.

    <p>True (A)</p> Signup and view all the answers

    What is the purpose of using a paired samples t-test?

    <p>To compare means from the same group under different conditions.</p> Signup and view all the answers

    In ANOVA, the __________ variable is referred to as a factor.

    <p>independent</p> Signup and view all the answers

    Match the following terms with their correct definitions:

    <p>Null Hypothesis = Assumes no difference between means Experimental Hypothesis = Assumes a difference between means Independent Samples = Comparison between different groups Paired Samples = Comparison within the same group</p> Signup and view all the answers

    What is the primary focus of a within-subjects experimental design?

    <p>Measuring differences within the same group due to different conditions (A)</p> Signup and view all the answers

    The F-value in ANOVA represents the ratio of within-group variance to between-group variance.

    <p>False (B)</p> Signup and view all the answers

    What is the difference between a single-factor design and a factorial design?

    <p>A single-factor design has one independent variable, while a factorial design has two or more independent variables.</p> Signup and view all the answers

    What is the recommended aim for the percentage of variance explained when conducting a factor analysis?

    <p>50-75% (B)</p> Signup and view all the answers

    The first factor extracted in factor analysis always explains the least amount of variance.

    <p>False (B)</p> Signup and view all the answers

    What should be done when factors no longer represent useful clusters or variables?

    <p>Stop extracting factors.</p> Signup and view all the answers

    A ______ plot is used to depict the amount of variance explained by each factor.

    <p>Scree</p> Signup and view all the answers

    What describes orthogonal rotation in factor analysis?

    <p>Factors are uncorrelated. (A)</p> Signup and view all the answers

    Name one type of rotation used in factor analysis.

    <p>Orthogonal rotation or Oblique rotation.</p> Signup and view all the answers

    Which statement is true regarding factor loadings?

    <p>Some variables may not load highly on any factors. (D)</p> Signup and view all the answers

    Match the type of rotation with its description:

    <p>Orthogonal rotation = Factors are uncorrelated Oblique rotation = Factors have some degree of correlation</p> Signup and view all the answers

    Which assumption of ANOVA pertains to the requirement that the variances of different groups are equal?

    <p>Homogeneity of variance (A)</p> Signup and view all the answers

    Exploratory Factor Analysis (EFA) is used to confirm existing hypotheses about factor structures.

    <p>False (B)</p> Signup and view all the answers

    What is the goal of conducting a factor analysis?

    <p>To identify underlying factors that explain patterns in data.</p> Signup and view all the answers

    The ______ test is a non-parametric alternative to ANOVA used when data does not meet the assumptions of normality.

    <p>Kruskal-Wallis</p> Signup and view all the answers

    Match the following regression analysis terms with their correct definitions:

    <p>R² = Proportion of variance explained by predictors Mediator variable = Explains the relationship between two variables Moderator variable = Affects the strength of a relationship Linearity = Assumption of a straight-line relationship</p> Signup and view all the answers

    Which of the following factors is not an assumption of a regression model?

    <p>Redundancy (B)</p> Signup and view all the answers

    Correlation implies causation between two variables.

    <p>False (B)</p> Signup and view all the answers

    What is a scree plot used for in factor analysis?

    <p>To determine the number of factors to retain.</p> Signup and view all the answers

    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

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    Related Documents

    Statistics 2 Notes PDF

    Description

    Test your knowledge on correlation coefficients, scales of measurement, and research designs in this engaging quiz. Understand the significance of the correlation coefficient and the coefficient of determination while exploring essential experimental design components. Ideal for students studying research methods in psychology or statistics.

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