KIN 610 - Spring 2026
  • Overview
  • Syllabus
  • Assignments
    • Attendance & Participation
    • Weekly Quizzes
    • Major Takeaways
    • Lab Assignments
    • ePortfolio
    • Exams

    • Exam 1
    • Exam 1 Study Guide
  • Weekly Materials
    • Week 2
    • Measurement

    • Week 3
    • Central Tendency
    • Variability

    • Week 4
    • Normal Curve

    • Week 5
    • Probability and Sampling Error
    • Hypothesis Testing

    • Week 6
    • Correlation and Regression

    • Week 7
    • Multiple Correlation and Regression

    • Week 8
    • Comparing Two Means

    • Labs
    • Lab 1: Data Collection
  • Resources

On this page

  • 1 Prepare
    • 1.1 Chapter Overview
    • 1.2 Multimedia Resources
    • 1.3 Read the Chapter
  • 2 Practice
    • 2.1 Frequently Asked Questions
    • 2.2 Test your Knowledge
  • 3 Participate
  • 4 Perform
    • 4.1 Apply Your Learning

Chapter 1: Measurement

Student Resources

ImportantHow to study this chapter

I use the 4 “P’s” framework to help you learn the material in this chapter: Prepare, Practice, Participate, and Perform. To increase the chances to succeed in this course, I strongly encourage you to complete all four “P’s” for each chapter.

1 Prepare

1.1 Chapter Overview

Before diving into the chapter details, familiarize yourself with the key concepts and vocabulary. The materials below will help you build a foundation before reading the full chapter.

1.2 Multimedia Resources

The following table provides access to video and slide resources for this chapter. Click the links to open them in an overlay for better viewing on all devices.

Multimedia Resources
Resource Description Link
Long Video Overview A detailed video explaining the key concepts of measurement, reliability, validity, and statistical inference in movement science research. 🔗 Watch Video
Slide Overview PDF PDF slides that serve as an overview of this chapter. Read these before the textbook to introduce the main concepts and vocabulary. 🔗 View Slides
Slide Deck HTML Interactive HTML slides for class. During class, the instructor controls the presentation; after class, review at your own pace. 🔗 Open Slides
Slide Deck PDF PDF version of the slide deck for download and offline viewing. 🔗 Download PDF

1.3 Read the Chapter

Read (Weir & Vincent, 2021, p. Ch1) and (Furtado, 2026, p. Ch2) - optional but recommended - to understand measurement concepts, research, and statistical inference in movement science.

To succeed in this course, you must read the textbook chapters assigned for each topic. This is the only way to learn the material in depth.

Once done, proceed to the next section to practice what you learned.

2 Practice

Practicing what you learned in the chapter is essential to mastering the material. Below are some resources to help you practice the material in this chapter.

2.1 Frequently Asked Questions

Measurement is the process of assigning numbers or labels to objects, events, or characteristics according to specific rules. In movement science, measurement allows us to quantify physical performance, physiological responses, and behavioral outcomes in a systematic and objective way.

Measurement is fundamental to movement science research because it allows us to: - Objectively assess physical performance and fitness - Track changes over time - Compare individuals or groups - Test hypotheses about movement and exercise - Make evidence-based decisions in practice

Reliability refers to the consistency or reproducibility of measurements. A reliable test produces similar results when repeated under the same conditions. Validity refers to whether the test actually measures what it is supposed to measure. A test can be reliable without being valid, but it cannot be valid without being reliable.

The main types of validity are: - Content validity: Does the test adequately represent the construct being measured? - Criterion validity: How well does the test correlate with an established criterion? - Construct validity: Does the test measure the theoretical construct it claims to measure? - Face validity: Does the test appear to measure what it claims to measure?

Statistical inference is the process of drawing conclusions about a population based on data from a sample. It involves using probability theory to estimate population parameters, test hypotheses, and make predictions while accounting for uncertainty.

Descriptive statistics summarize and describe the characteristics of a dataset (e.g., mean, median, standard deviation). Inferential statistics use sample data to make generalizations about a larger population and test hypotheses (e.g., t-tests, ANOVA, correlation).

2.2 Test your Knowledge

Take this low-stakes quiz to test your knowledge of the material in this chapter. This quiz is for practice only and will help you identify areas where you may need additional review.

# What is measurement? - [ ] The process of collecting information through surveys - [x] The process of comparing a value to a standard - [ ] The process of evaluating the quality of performance - [ ] The process of organizing data for analysis # Which of the following is a continuous variable? - [ ] Number of basketball free throws made - [ ] Number of players on a team - [x] Time to run 100 meters - [ ] Number of heartbeats in a minute # What type of scale classifies subjects into mutually exclusive categories without qualitative differentiation? - [x] Nominal scale - [ ] Ordinal scale - [ ] Interval scale - [ ] Ratio scale # Which scale of measurement has equal units AND an absolute zero point? - [ ] Nominal scale - [ ] Ordinal scale - [ ] Interval scale - [x] Ratio scale # What does reliability refer to in measurement? - [ ] Whether the test measures what it is designed to measure - [x] The consistency or reproducibility of measurements - [ ] The absence of investigator bias - [ ] The ability to generalize results to a population # In experimental research, what is the independent variable? - [ ] The variable that is measured - [x] The variable that is manipulated or controlled by the researcher - [ ] The variable that represents random error - [ ] The variable that is dependent on other factors # What distinguishes experimental research from observational research? - [ ] Experimental research uses surveys while observational research does not - [ ] Experimental research examines the past while observational research examines the present - [x] Experimental research manipulates variables while observational research describes without manipulation - [ ] Experimental research uses smaller sample sizes # What is the null hypothesis (H₀)? - [ ] The hypothesis that prompts the research - [x] The hypothesis that predicts no relationship or difference between groups - [ ] The hypothesis that is always accepted - [ ] The hypothesis that requires no statistical testing # What does internal validity refer to? - [ ] The ability to generalize results to the population - [x] The control within the experiment to ensure results are due to the treatment - [ ] The consistency of measurements over time - [ ] The appropriateness of the test in measuring what it is designed to measure # Which of the following is an example of an intervening variable? - [ ] The independent variable being manipulated - [ ] The dependent variable being measured - [x] Fatigue affecting posttest results when not controlled - [ ] The random assignment of subjects to groups # Why is the metric system superior to the English system for scientific measurement? - [ ] It uses smaller units of measurement - [ ] It was invented more recently - [x] It uses consistent units based on multiples of 10 and constant standards - [ ] It measures time more accurately # What is the purpose of a control group in experimental research? - [ ] To increase the sample size - [ ] To provide baseline measurements only - [x] To determine whether changes are due to the treatment or other factors - [ ] To eliminate the need for statistical analysis

3 Participate

This section includes activities and discussions that will be completed during class time. Your active participation is essential for deepening your understanding of the material.

TipIn-Class Activities

During class, we will: - Discuss real-world measurement challenges in movement science - Practice evaluating measurement tools for reliability and validity - Work through case studies of research designs - Engage in collaborative problem-solving exercises

4 Perform

4.1 Apply Your Learning

Now that you’ve prepared, practiced, and participated, it’s time to demonstrate your mastery of the material through assignments and assessments.

WarningNote to Students

I strongly encourage you to complete the previous “Ps” (Prepare, Practice, Participate) before attempting any assignments or assessments associated with this chapter.

References

Furtado, O., Jr. (2026). Statistics for movement science: A hands-on guide with SPSS (1st ed.). https://drfurtado.github.io/sms/
Weir, J. P., & Vincent, W. J. (2021). Statistics in kinesiology (5th ed.). Human Kinetics.

© 2026 Dr. Ovande Furtado Jr. | CC BY-NC-SA