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Quizzes > Quizzes for Business > Education

Take the Quantitative Research Methods Quiz

Evaluate Statistical Techniques with Multiple-Choice Questions

Difficulty: Moderate
Questions: 20
Learning OutcomesStudy Material
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Ready to test your grasp of statistical analysis in research? This Quantitative Research Methods Quiz is ideal for students, early-career researchers, and anyone eager to deepen their data analysis skills. Tackle 15 thought-provoking multiple-choice questions and compare your results with Statistics Research Methods Quiz or explore the Research Methods Fundamentals Quiz for further practice. Feel free to customize any question in our editor to match your learning objectives, then browse our quizzes library for more challenges. Joanna Weib invites you to elevate your quantitative research expertise today.

Which measure of central tendency represents the arithmetic average of a dataset?
Mean
Median
Mode
Range
The mean is calculated by summing all values and dividing by the number of observations. It reflects the arithmetic average, unlike the median or mode.
What term describes a testable statement predicting a relationship between two variables?
Hypothesis
Variable
Sample
Population
A hypothesis is a specific, testable prediction about the relationship between variables. Variables themselves are the elements under study, not the predictive statement.
Which sampling strategy involves selecting participants based on ease of access?
Convenience sampling
Stratified sampling
Cluster sampling
Systematic sampling
Convenience sampling selects participants who are readily available, making it the easiest but least representative method. Other strategies involve more structured selection procedures.
Which concept refers to the consistency of a measurement instrument across repeated applications?
Reliability
Validity
Accuracy
Generalizability
Reliability indicates the degree to which an instrument yields consistent results over time or across items. Validity, by contrast, refers to whether it measures what it intends to measure.
What does validity assess in a research instrument?
Whether the instrument measures the intended construct
Whether the instrument yields consistent scores
Whether the sample is representative
Whether the data collection method is cost-effective
Validity concerns whether the instrument accurately captures the construct of interest. Consistency is a matter of reliability, not validity.
Which statistical test is used to compare the means of two independent groups?
Independent samples t-test
Paired samples t-test
One-way ANOVA
Chi-square test
The independent samples t-test compares the means of two separate groups. A paired t-test is for related samples, ANOVA is for more than two groups, and chi-square is for categorical data.
What is the primary purpose of a one-way ANOVA?
To compare means across three or more groups
To compare variances across groups
To test for correlation between variables
To assess reliability of measurements
One-way ANOVA tests whether there are statistically significant differences among the means of three or more groups. It focuses on mean comparisons, not variances or correlations.
Which sampling strategy ensures representation across predefined subgroups?
Stratified sampling
Simple random sampling
Cluster sampling
Snowball sampling
Stratified sampling divides the population into subgroups and samples from each proportionally. This ensures each subgroup is represented in the final sample.
A researcher obtains a Cronbach's alpha of 0.72 for a new scale. What does this indicate?
Acceptable internal consistency
Poor internal consistency
Excellent internal consistency
No internal consistency
Cronbach's alpha values above 0.7 are generally considered acceptable for internal consistency. Values above 0.9 indicate excellent reliability, while lower values suggest poor consistency.
Which type of validity examines whether items cover the full range of a construct's domain?
Content validity
Face validity
Criterion validity
Construct validity
Content validity assesses whether a measure includes all facets of the intended construct. Face validity refers to appearance, while criterion and construct validity examine different relationships.
What does the standard deviation of a dataset represent?
Average distance of values from the mean
Difference between the highest and lowest values
Sum of squared deviations
Average of absolute deviations
Standard deviation measures the average distance of data points from the mean, indicating data spread. Range is the difference between max and min, and sum of squared deviations is part of variance calculation.
If a hypothesis test yields p = 0.03 at α = 0.05, what decision should a researcher make?
Reject the null hypothesis
Fail to reject the null hypothesis
Increase the sample size
Conclude no effect exists
A p-value of 0.03 is less than the α level of 0.05, so the null hypothesis should be rejected. Failing to reject occurs when p ≥ α.
In the hypothesis "Study time affects exam scores," what are the independent and dependent variables?
Independent variable: study time; Dependent variable: exam scores
Independent variable: exam scores; Dependent variable: study time
Both are independent variables
Both are dependent variables
The independent variable is the presumed cause - study time - while the dependent variable is the outcome - exam scores. The other options mix up cause and effect.
What is an appropriate null hypothesis for testing a positive relationship between hours studied and test performance?
There is no relationship between hours studied and test performance
Hours studied increase test performance
Hours studied decrease test performance
There is a negative relationship between hours studied and performance
The null hypothesis asserts no relationship between the variables. Alternative hypotheses state the expected positive or negative relationship.
Which study design collects data from participants at a single point in time?
Cross-sectional design
Longitudinal design
Experimental design
Cohort design
Cross-sectional designs gather data once, providing a snapshot. Longitudinal studies collect data repeatedly over time.
When the assumption of normality is violated for two independent samples, which test is most appropriate?
Mann-Whitney U test
Independent samples t-test
Paired samples t-test
One-way ANOVA
The Mann-Whitney U test is a nonparametric alternative to the independent samples t-test when normality is violated. Paired t-tests and ANOVA require normality.
A Cohen's d of 0.8 indicates which level of effect size?
Large effect
Small effect
Medium effect
Negligible effect
Cohen's guidelines classify d = 0.8 as a large effect size, 0.5 as medium, and 0.2 as small. Values near zero are negligible.
An ANOVA report shows F(2,47) = 4.6, p = 0.015. What does this result indicate?
At least one group mean differs significantly
All group means are equal
Variances across groups are equal
No differences exist among groups
A significant F statistic (p < .05) indicates that not all group means are equal and that at least one pair differs. It does not specify which groups differ.
To achieve a margin of error of ±3% at 95% confidence for estimating a proportion, roughly what sample size is needed?
1067
385
250
5000
The sample size formula n ≈ (z^2 × p(1−p))/E^2 with z=1.96, p=0.5, and E=0.03 yields approximately 1067. Values like 385 correspond to a 5% margin of error.
Which form of validity assesses that a measure is not strongly related to different constructs?
Discriminant validity
Convergent validity
Face validity
Criterion validity
Discriminant validity ensures a measure does not correlate too highly with measures of different constructs. Convergent validity tests high correlations with similar constructs.
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Learning Outcomes

  1. Analyse survey data using basic statistical tests (e.g., t-test, ANOVA).
  2. Interpret numerical results to draw valid research conclusions.
  3. Identify appropriate sampling strategies for quantitative studies.
  4. Evaluate measurement reliability and validity in data collection.
  5. Apply descriptive statistics to summarize research findings.
  6. Demonstrate proficiency in formulating testable quantitative hypotheses.

Cheat Sheet

  1. Distinguish Independent and Dependent Variables - Think of independent variables as the ingredients you tweak in a recipe, and dependent variables as the tasty result you measure. Nailing this difference keeps your experiments on track and your conclusions crystal clear. CliffsNotes: Variables
  2. Explore Quantitative Research Designs - From full-on experiments to descriptive snapshots, each design has its own superpower for answering research questions. Picking the right one means your study will soar instead of flop. Quantitative Methods Guide
  3. Master Descriptive Statistics - Want to summarize a mountain of data without breaking a sweat? Mean, median, mode and standard deviation are your trusty sidekicks for turning chaos into clear insights. Quantitative Methods Guide
  4. Get Hands-On with Inferential Tests - T-tests, ANOVA and friends help you decide if your findings are true heroes or just lucky flukes. Learning when and how to use them gives your research serious credibility. Five Quant Concepts
  5. Grasp Reliability and Validity - Reliability is all about getting the same result over and over, and validity is about hitting the bullseye on what you actually want to measure. Together they make your data rock-solid. Five Quant Concepts
  6. Explore Sampling Techniques - Whether you're drawing names from a hat or dividing people into layers, your sampling method decides how well your results generalize. Pick wisely to avoid sneaky biases. CliffsNotes: Sampling
  7. Understand Sample Size Importance - Too few participants and your results wobble; too many and you might waste time and resources. Finding that sweet spot keeps your study both reliable and efficient. Sample Size Insights
  8. Craft Clear Survey Questions - Ambiguous or leading questions can derail even the best surveys. Keep your wording simple, unbiased, and laser-focused on what you want to learn. Survey Design Tips
  9. Recognize Statistical Significance - A p-value under 0.05 usually means your results probably didn't happen by chance. This magic threshold helps you decide when to shout "Eureka!" and when to hold off. Five Quant Concepts
  10. Differentiating Correlation and Causation - Just because two things move together doesn't mean one causes the other. Knowing this prevents wild claims and keeps your conclusions on solid ground. Five Quant Concepts
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