Week 3: Intro to Stats

KIN 610 - Spring 2023

Dr. Ovande Furtado Jr

Credits

(furtado2023?)

Measurement

Introduction to Measurement in Kinesiology

  • The importance of measurement in Kinesiology
  • Measurement as the process of determining the value of a characteristic or quantity

Why Measurement is Important in Kinesiology

  • Measurement provides objective data
  • Measurement allows for comparison

Evaluation of Interventions

  • Measurement is essential for evaluating the effectiveness of interventions
  • Measurement can help to identify patterns or trends in large amounts of data

Statistics in Kinesiology

Intro

  • Statistics plays a crucial role in Kinesiology
  • Allows researchers and practitioners to analyze and interpret data related to human movement, physical activity, and exercise

Data Analysis Techniques

  • One of the most common uses of statistics in Kinesiology is for data analyses This includes:
  • Descriptive statistics: summarize and describe the main characteristics of a dataset
  • Inferential statistics: infer about a specific population based on a sample of data
  • Descriptive statistics: mean, median, standard deviation, range
  • Inferential statistics: t-tests, ANOVA, regression analysis

Design and Analysis of Experiments and Studies

  • Statistics used to design and analyze experiments and studies in Kinesiology
  • Statistical tests: determine if there is a significant difference between groups or if an intervention has an effect
  • Examples: t-test, ANOVA

Correlation and Causal Inference

  • Correlation: measures the association between two variables
  • Causal inference: determines if an observed association is due to a causal relationship

Interpreting Results

  • Consider level of significance: probability that results occurred by chance (p < 0.05)
  • Consider practical significance: meaningful results in the real world

Visualizing Data

  • Graphical methods used to visualize data: histograms, scatter plots, box plots
  • Help identify patterns, outliers, and communicate results in an easily understandable format

Variables and Constants in Scientific Research

Variables

  • A variable is a characteristic or factor that can change or take on different values within a study or experiment.
  • Example: In a study investigating the effects of a new exercise program on muscle strength, the variable of interest would be muscle strength.

Constants

  • A constant is a characteristic or factor that remains unchanged or fixed within a study or experiment.
  • Example: In a study investigating the effects of a new exercise program on muscle strength, the age of the participants could be considered a constant.

Benefits of Controlling Constants

  • Allows researchers to isolate the effects of a particular variable of interest.
  • Confidence that any changes observed are due to the variable of interest and not other factors.

Summary

  • Variables and constants are important concepts in scientific research.
  • Variables are factors that can change, while constants are factors that remain unchanged.
  • Understanding and controlling constants is important to isolate the effects of the variables of interest.

Continuous vs. Discrete Variables

Continuous Variables

  • A continuous variable is a variable that can take on any value within a specific range.
  • Examples: height, weight, time
  • Can be measured to an infinite number of decimal places.

Discrete Variables

  • A discrete variable is a variable that can only take on specific values.
  • Examples: number of children in a household
  • Discrete variables are countable, usually measured by counting.

Difference between Continuous and Discrete Variables

  • The main difference between continuous and discrete variables is how data is collected and analyzed.
  • Example 1: Study investigating the relationship between weight and blood pressure, weight is a continuous variable, and blood pressure is also continuous. Pearson Correlation Coefficient would be used to investigate the relationship.
  • Example 2: Study investigating the relationship between the number of hours of sleep and the number of absenteeism in a company, hours of sleep are continuous and can have an infinite number of values, while absenteeism is discrete. Spearman Rho would be used to investigate the correlation.

Importance of Understanding Continuous vs. Discrete Variables

  • Affects the methods used to collect and analyze data.
  • Researchers must use the appropriate statistical techniques for their specific type of variables to draw valid conclusions from their data.

Summary

  • Continuous and discrete variables are two types of variables used in research.
  • Continuous variables can take on any value within a specific range, while discrete variables can only take on specific values.
  • Understanding the difference between these two types of variables is important because it can affect the methods used to collect and analyze data.

Levels of Numerical Data

Intro

  • Numerical data is classified into four types based on their level of measurement and the types of statistical analysis that can be used.
  • Nominal, ordinal, interval, and ratio data are different types of numerical data.

Nominal Data

  • Nominal data is a non-numeric type of data that can be placed into categories but cannot be meaningfully ordered or quantified.
  • Examples: color, gender, religion
  • Analysis methods: frequency distributions, chi-squared tests, contingency tables

Ordinal Data

  • Ordinal data is a type of data that can be placed into categories and can be meaningfully ordered, but the difference between the data points is not necessarily equal.
  • Examples: education level, socioeconomic status, satisfaction level
  • Analysis methods: medians, quartiles, percentiles

Interval Data

  • Interval data is a type of data that has equal intervals between the data points but does not have an absolute zero point.
  • Examples: temperature measured in Celsius or Fahrenheit
  • Analysis methods: central tendency and variability measures

Ratio Data

  • Ratio data is a type of data with equal intervals between the data points and an absolute zero point.
  • Examples: weight, height, time
  • Analysis methods: central tendency and variability measures, correlation, and regression analysis

Conclusion

  • Understanding the differences between nominal, ordinal, interval, and ratio data is crucial because it affects the statistical methods used to analyze and interpret the data.
  • Choosing the appropriate type of data and analysis is important to draw meaningful and accurate conclusions.

Dependent and Independent Variables

Independent Variable

  • Independent variable (IV) is the variable that is manipulated or changed in an experiment.
  • The IV is the factor the researcher is interested in studying.
  • Example: In a study investigating the effects of a new exercise program on muscle strength, the IV would be the exercise program itself.

Dependent Variable

  • Dependent variable (DV) is the factor affected by the independent variable.
  • The DV is the variable that is measured or observed in response to the manipulation of the independent variable.
  • Example: In a study investigating the effects of a new exercise program on muscle strength, the DV would be muscle strength, measured before and after the exercise program to see if there is an improvement.

The Relationship between IV and DV

  • The independent variable is manipulated or controlled by the researcher.
  • The dependent variable is measured or observed.
  • Understanding the relationship between these two variables allows researchers to establish a cause-and-effect relationship and draw valid conclusions from their research.

Another Example

  • A study investigating the effect of a specific training program on running performance.
  • Independent variable: The training program
  • Dependent variable: Running performance, measured before and after the program.

Conclusion

  • Dependent and independent variables are important concepts in scientific research.
  • The researcher manipulates independent variables and dependent variables are measured or observed.
  • Understanding the relationship between these two variables is crucial to establish a cause-and-effect relationship and draw valid conclusions from research.

Types of Research

Intro

  • Research is a systematic and scientific inquiry into a subject or phenomenon
  • It involves a process of discovering new knowledge, understanding complex issues, and making informed decisions

Three Common Types of Research

  • Historical Research
  • Observational Research
  • Experimental Research

Historical Research

  • Involves the collection and analysis of primary and secondary sources

  • Investigates events, phenomena, or people in the past

  • Relying on written documents, photographs, and other artifacts to reconstruct the past

  • Understanding the origin of events, historical trends, and the development of ideas, institutions, or societies

  • Observational Research

  • The researcher observes but does not manipulate the phenomenon of interest

  • May involve collecting both quantitative and qualitative data

  • Typically does not involve manipulation of independent variables

  • Can be conducted in natural settings, such as in the field or controlled laboratory settings

  • Example: studying athletes’ natural movement patterns or assessing the effectiveness of rehabilitation programs

Experimental Research

  • The researcher manipulates one or more independent variables and measures their effects on one or more dependent variables
  • Establishes cause-and-effect relationships between variables
  • Involves using control groups and randomly assigning participants to treatment or control groups
  • Example: investigating the effects of different training protocols on physical performance or testing the effectiveness of different therapeutic interventions

Conclusion

  • Research is a systematic and scientific inquiry into a particular subject or phenomenon
  • Historical research, observational research, and experimental research are three common types of research
  • Each type of research has its strengths, limitations, and purposes
  • The choice of which type to use will depend on the research question and the study’s goals.

Internal and external validity

Introduction

  • Research is a systematic and scientific inquiry into a particular subject or phenomenon
  • Two critical concepts in research: internal and external validity

Internal Validity

  • Definition: degree to which the results of a study can be attributed to the manipulation of the independent variable
  • Importance in Kinesiology: ensuring that the results of a study can be attributed to the specific training protocol or intervention tested
  • Example: investigating the effects of a specific strength training program on muscle mass

External Validity

  • Definition: degree to which the results of a study can be generalized to other populations, settings, and periods
  • Importance in Kinesiology: ensuring that the results of a study can be applied to real-world situations and other populations
  • Example: investigating the effects of a specific strength training program on muscle mass in young men

Internal and External Validity

  • Separate concepts, but related
  • Researchers should strive to achieve high internal and external validity
  • Trade-offs between the two may be necessary

Conclusion

  • Statistics is a critical component of kinesiology
  • Understanding and applying statistical concepts and methods is crucial for informed decision-making
  • Key concepts like measurement, variables, and research types must be understood and applied properly
  • Understanding and applying concepts like internal and external validity is essential for drawing valid conclusions from research findings.

References

https://drfurtado.github.io/kin610/