Exam 1 — Study Guide
KIN 610: Quantitative Analysis of Research in Kinesiology — Spring 2026
Week 10 — Tuesday, March 24, 2026 | RE 276 (Computer Lab) | Full class session (~2 hrs 40 min)
Use this guide to review concepts, practice SPSS procedures, and locate the relevant sections in the Statistics for Movement Science (SMS) textbook before the exam.
How to Use This Guide
This guide maps each exam topic to:
- Key concepts you need to know
- SPSS procedure you must be able to execute
- SMS textbook section — practice navigating to that page now, so you are not searching during the exam
- Formulas where manual calculation is required (e.g., z-scores, Cohen’s d)
Practice each SPSS procedure using the course dataset before exam day.
Practice Dataset
Use the dataset below to rehearse every SPSS procedure covered on the exam. It is not the exam dataset — it uses different variables — so working through it will build your skills without revealing exam content.
Download: sport-performance-practice.csv
Variable Descriptions
| Variable | Description | Unit | Type |
|---|---|---|---|
ID |
Participant identifier | — | Nominal |
Group |
Training status: 1 = Athlete, 2 = Non-athlete | — | Nominal (2 groups) |
Flexibility |
Sit-and-reach score | cm | Continuous |
Grip_Strength |
Dominant-hand grip dynamometer | kg | Continuous |
Balance_Time |
Single-leg stance duration | sec | Continuous |
Sprint_Time |
40-meter sprint time | sec | Continuous |
N = 30 (15 athletes, 15 non-athletes)
Suggested Practice Tasks
Work through these in order — they mirror the structure of the exam:
- Central tendency — Report M, Mdn, and Mode for
Flexibility, separately for eachGroup. - Variability — Report SD, Variance, and Range for
Grip_Strengthfor the full sample. - Normality — Assess normality of
Balance_Timeusing histogram, Q-Q plot, Shapiro-Wilk, and z-skewness/z-kurtosis. - z-score — Calculate the z-score for a
Sprint_Timeof 7.0 sec (use the full-sample M and SD). Then determine what percentage of participants would be slower than that. - Statistical inference — Given a fictitious result (t(28) = 2.45, p = .021, α = .05), write out the decision and interpretation.
- Pearson correlation — Compute the correlation between
FlexibilityandGrip_Strength. Report in APA format and classify strength using Cohen’s (1988) criteria. - Simple regression — Use
Flexibilityto predictBalance_Time. Write the regression equation and interpret R². - Independent t-test — Compare
Sprint_Timebetween athletes and non-athletes. Report in APA format, check Levene’s test, and calculate Cohen’s d.
After completing each task, verify that your SPSS output matches expectations: group means should differ in a logical direction, the correlation should be positive, and the t-test should show a meaningful group difference.
Unless stated otherwise, use only the method demonstrated in class to copy/paste charts, tables, and output from SPSS into your exam Word document. Do not take screenshots with your phone or use methods not covered in class. Improperly formatted or missing output will not receive full credit.
Topic 1 — Measures of Central Tendency
Exam Question 1 (10 pts)
Key Concepts
- Mean: arithmetic average; sensitive to outliers; used when data are approximately normally distributed
- Median: middle value (50th percentile); robust to outliers; preferred when distribution is skewed
- Mode: most frequent value; applicable to all scales; SPSS reports the smallest mode when multiple modes exist
- Choosing the best measure: understand when each measure is most appropriate based on distribution shape and the presence of outliers
SPSS Procedure
Analyze → Descriptive Statistics → Frequencies
Move variable to Variable(s), click Statistics → check Mean, Median, Mode → OK
To split by group:
Data → Select Cases → use a condition (e.g., a categorical variable = group code) → run analysis → clear filter → repeat for the other group.
SMS Reference
📖 Chapter 4 — Measures of Central Tendency
Practice: Navigate to this chapter now and locate the section on choosing between mean and median for skewed vs. symmetric distributions.
Topic 2 — Measures of Variability
Exam Question 2 (10 pts)
Key Concepts
- Range: max − min; simple but sensitive to extremes
- Variance (s²): average squared deviation from the mean; in squared units
- Standard Deviation (SD): square root of variance; same units as original data — the primary measure to report
- When to report SD: preferred for continuous, approximately normal data; forms the basis of most inferential statistics
SPSS Procedure
Analyze → Descriptive Statistics → Descriptives
Move variable to Variable(s), click Options → check Std. deviation, Variance, Range → OK
SMS Reference
📖 Chapter 5 — Measures of Variability
Practice: Locate the section on standard deviation and the guidance on which variability measure to report in a research context.
Topic 3 — Data Visualization and Normality
Exam Question 3 (10 pts)
Key Concepts
- Histogram: visual shape of the distribution; look for bell-curve symmetry, skew, or outliers
- Q-Q plot: data quantiles vs. theoretical normal quantiles; points near the diagonal = normal
- Shapiro-Wilk test: formal test of normality; p > .05 → no significant departure from normality
- z-Skewness and z-Kurtosis: standardized scores for shape statistics; values within ±1.96 support normality
Formulas:
\[z_{skew} = \frac{Skewness}{SE_{Skewness}} \qquad z_{kurt} = \frac{Kurtosis}{SE_{Kurtosis}}\]
SPSS Procedure
Analyze → Descriptive Statistics → Explore
Move variable to Dependent(s) → click Plots → check “Normality plots with tests” → also check “Histogram” → OK
Skewness and Kurtosis (with SE) appear in the Descriptives table of the Explore output.
SMS Reference
📖 Chapter 3 — Data Visualization
📖 Chapter 7 — The Normal Distribution
Practice: Find the section on interpreting Q-Q plots and the ±1.96 criterion for z-skewness/z-kurtosis.
Topic 4 — z-scores and the Normal Curve
Exam Question 4 (10 pts)
Key Concepts
- z-score: number of standard deviations a score falls above or below the mean
- Percentage above/below: use the z-table to find the area below z, then subtract from 1.00 if you need the area above; convert to percentage by multiplying by 100
Formula
\[z = \frac{X - M}{SD}\]
Using the z-table: Know how to find the area (proportion/percentage) corresponding to a z-score — both above and below a given value.
SMS Reference
📖 Chapter 6 — Percentiles and Standard Scores
📖 Chapter 7 — The Normal Distribution
Practice: Locate the z-table in the SMS appendix and practice the lookup procedure.
📖 Appendix — Statistical Tables
Topic 5 — Statistical Inference
Exam Question 5 (10 pts)
Key Concepts
| Concept | Definition |
|---|---|
| Type I Error (α) | Rejecting a true H₀ — false positive |
| Type II Error (β) | Failing to reject a false H₀ — false negative |
| Statistical Power | Probability of correctly rejecting a false H₀; power = 1 − β |
Trade-offs when changing α: Understand how raising or lowering the significance level affects Type I error risk, Type II error risk, and statistical power — and why these trade-offs matter.
Interpreting a reported result:
Compare p to your chosen α:
- p < α → Reject H₀ (statistically significant)
- p ≥ α → Fail to reject H₀ (not statistically significant)
Always explain your reasoning: state the α level, state the p-value, and state which decision rule applies.
SMS Reference
📖 Chapter 10 — Hypothesis Testing
Practice: Find the section on Type I and Type II errors and the trade-off between alpha levels and power.
Topic 6 — Pearson Correlation
Exam Question 6 (10 pts)
Key Concepts
- Pearson r: measures linear relationship between two continuous variables; ranges from −1.00 to +1.00
- Direction: sign indicates positive or negative relationship
- Strength: use Cohen’s (1988) criteria (required for this exam):
| r | |
|---|---|
| < .10 | Negligible |
| .10 – .29 | Small |
| .30 – .49 | Medium |
| .50 – 1.00 | Large |
APA format: r(df) = .xx, p = .xxx
(df = N − 2)
SPSS Procedure
Analyze → Correlate → Bivariate
Move both variables to Variables → ensure Pearson is checked → OK
SMS Reference
📖 Chapter 11 — Correlation and Regression
📖 Appendix — Effect Size Benchmarks (Pearson r)
Practice: Navigate to the correlation chapter and locate the Cohen’s criteria table. Confirm you know how to read a Pearson correlation matrix from SPSS output.
Topic 7 — Simple Linear Regression
Exam Question 7 (10 pts)
Key Concepts
- R²: proportion of variance in the outcome explained by the predictor; expressed as a percentage
- Unstandardized coefficient (B): for every 1-unit increase in the predictor, the outcome changes by B units
- Regression equation: \(\hat{Y} = b_0 + B(X)\) where b₀ is the intercept (Constant) and B is the slope
SPSS Procedure
Analyze → Regression → Linear
Move outcome variable to Dependent → move predictor to Independent(s) → OK
Report the Model Summary table (for R²) and the Coefficients table (for B and the intercept).
SMS Reference
📖 Chapter 11 — Correlation and Regression
Practice: Find the section on interpreting R² and the unstandardized regression coefficient. Practice writing out the regression equation from a Coefficients table.
Topic 8 — Independent Samples t-test
Exam Question 8 (10 pts)
Key Concepts
- Null hypothesis (H₀): no difference between group means in the population (μ₁ = μ₂)
- Alternative hypothesis (H₁): a difference exists (μ₁ ≠ μ₂)
- Levene’s test: tests homogeneity of variance assumption
- Levene’s p > .05 → use “Equal variances assumed” row
- Levene’s p ≤ .05 → use “Equal variances not assumed” row
- APA format for t-test: t(df) = x.xx, p = .xxx, 95% CI [lower, upper]
- Cohen’s d: standardized mean difference; use Cohen’s (1988) criteria:
| d Value | Interpretation |
|---|---|
| < 0.20 | Negligible / Trivial |
| 0.20 – 0.49 | Small |
| 0.50 – 0.79 | Medium |
| ≥ 0.80 | Large |
Cohen’s d formula:
\[d = \frac{M_1 - M_2}{SD_{pooled}}\]
SPSS Procedure
Analyze → Compare Means → Independent Samples T Test
Move outcome to Test Variable(s) → move grouping variable to Grouping Variable → click Define Groups (enter 1 and 2) → OK
SMS Reference
📖 Chapter 13 — Comparing Two Means
📖 Appendix — Effect Size Benchmarks (Cohen’s d)
📖 Appendix — SPSS: Comparing Two Means
Practice: Navigate to the SPSS tutorial appendix and rehearse reading the Levene’s test result and selecting the correct t-test row.
Quick Reference — Key Formulas
| Formula | When to Use |
|---|---|
| \(z = \dfrac{X - M}{SD}\) | Convert a raw score to a z-score |
| \(z_{skew} = \dfrac{Skewness}{SE_{Skewness}}\) | Test skewness for normality |
| \(z_{kurt} = \dfrac{Kurtosis}{SE_{Kurtosis}}\) | Test kurtosis for normality |
| \(\hat{Y} = b_0 + B(X)\) | Write the regression equation |
| \(d = \dfrac{M_1 - M_2}{SD_{pooled}}\) | Calculate effect size for t-test |
SMS Textbook — Sections to Review Before the Exam
Practice navigating to each of these pages in the SMS textbook before the exam day so you are not searching during the exam.
| Topic | SMS Chapter / Appendix |
|---|---|
| Central tendency | Ch. 4 |
| Variability | Ch. 5 |
| Data visualization | Ch. 3 |
| Normal distribution & z-table | Ch. 7 |
| Standard scores (z-scores) | Ch. 6 |
| Statistical tables (z-table) | Appendix |
| Hypothesis testing, Type I/II errors | Ch. 10 |
| Correlation and regression | Ch. 11 |
| Independent samples t-test | Ch. 13 |
| Effect size benchmarks | Appendix |
| SPSS: Comparing two means | Appendix |
| APA reporting | Appendix |