Measurement
Before we begin our discussion about statistics, we must address the topic of measurement. Measurement is the process of determining the value of a characteristic or quantity. It involves assigning numbers to objects or events in a way that is consistent and meaningful. Measurement is a crucial aspect of statistics in Kinesiology, as it allows researchers to quantify and analyze various physical and physiological phenomena related to human movement and performance[thomas2011?]. This section will explore why measurement is important in Kinesiology.
- Measurement provides objective data: Kinesiology research often deals with subjective phenomena, such as pain or fatigue, which can be challenging to quantify. Measurement allows researchers to collect objective data that can be used to understand these subjective experiences better. For example, measuring muscle activity or heart rate can provide insight into how the body responds to different physical activities or interventions.
- Measurement allows for comparison: Measurement allows researchers to compare different groups of participants or even the same individuals at different points in time. This can help to identify differences and similarities in physical characteristics, performance, or response to interventions.
- Measurement allows for the evaluation of interventions: Measurement is essential for evaluating the effectiveness of different interventions, such as exercises, treatments, or training programs. Researchers can determine whether or not a particular approach is effective by measuring critical outcomes before and after an intervention.
- Measurement can be used to identify patterns: Measurement can help to identify patterns or trends in large amounts of data. For example, measuring the physical activity levels of a large population can provide insight into that population’s overall physical fitness levels. It can be used to identify populations at risk for certain health conditions.
Measurement is the process of determining the value of a characteristic or quantity. It involves assigning numbers to objects or events in a way that is consistent and meaningful.
Therefore, measurement is a critical aspect of statistics in Kinesiology, providing objective data, allowing for comparisons, evaluating interventions, and identifying patterns. The proper design, execution, and interpretation of measurements are the foundation of statistical analysis; therefore, it is an essential tool for Kinesiologists to make informed decisions.
Statistics
Statistics play a crucial role in Kinesiology, as it allows researchers and practitioners to analyze and interpret data related to human movement, physical activity, and exercise[thomas2011?] This section will explore how statistics are used in Kinesiology, including data analysis techniques, statistical tests, and key considerations when interpreting the results.
One of the most common uses of statistics in Kinesiology is for data analyses. This includes techniques such as descriptive statistics, which are used to summarize and describe the main characteristics of a dataset, and inferential statistics, which are used to infer about a specific population based on a sample of data. Some of the most commonly used descriptive statistics in Kinesiology include measures of central tendency, such as mean and median, and measures of variability, such as standard deviation and range. Additionally, inferential statistics such as t-tests, ANOVA, and regression analysis are used in Kinesiology.
Descriptive statistics is used to summarize and describe the main characteristics of a dataset while inferential statistics is used to infer about a specific population based on a sample of data.
Another important use of statistics in Kinesiology is in the design and analysis of experiments and studies[huck2004?]. Researchers often use statistical tests to determine if there is a significant difference between groups or if a particular intervention has an effect. These tests help researchers to determine whether their results are due to chance or if they are significant. For example, a t-test might be used to compare the muscle strength of a group of individuals who have undergone a resistance training program to a group who have not. Another example is ANOVA, which is used to compare multiple groups.
Statistics allow researchers and practitioners to analyze and interpret data related to human movement, physical activity, and exercise.
Additionally, correlation and causal inference are also important aspects of Kinesiology. Correlation measures the association between two variables, while causal inference deals with determining if an observed association is due to a causal relationship[weir2021?].
When interpreting the results of statistical analyses, it is important to consider the level of significance, which is the probability that the results occurred by chance. A commonly used significance level is p < 0.05, meaning there is less than a 5% chance that the results are due to chance. However, it is also essential to consider the practical significance of the results, which refers to how meaningful the results are in the real world.
In addition to these techniques, kinesiologists use various graphical methods to visualize data, such as histograms, scatter plots, and box plots[1]. These methods can help researchers to identify patterns and outliers in the data and to communicate their findings to others in an easily understandable format.
Statistics play an essential role in Kinesiology by allowing researchers and practitioners to analyze and interpret human movement, physical activity, and exercise data. Using data analysis techniques, statistical tests, and careful interpretation of results, researchers and practitioners can gain valuable insights into improving human movement, physical activity, and exercise.
Variables and Constants
Variables and constants are two important concepts used to design and conduct experiments in scientific research[weir2021?]. In this section, we will take a closer look at what variables and constants are and how they are used in research.
A variable is a characteristic or factor that can change or take on different values within a study or experiment. For example, in a study investigating the effects of a new exercise program on muscle strength, the variable of interest would be muscle strength. The researchers would measure muscle strength before and after the intervention to see if there was an improvement.
On the other hand, a constant is a characteristic or factor that remains unchanged or fixed within a study or experiment. For example, in the same study mentioned above, the age of the participants could be considered a constant. Therefore, the researchers would control the age of the participants to ensure that any changes in muscle strength observed were due to the intervention and not due to an age difference.
A variable is a characteristic or factor that can change or take on different values within a study or experiment.
One of the main benefits of controlling constants is that it allows researchers to isolate the effects of a particular variable of interest. In addition, by keeping certain factors constant, researchers can be more confident that any changes observed are due to the variable of interest and not other factors.
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 are important because it allows researchers to isolate the effects of the variables of interest.
A constant is a characteristic or factor that remains unchanged or fixed within a study or experiment.
Continuous vs. Discrete Variables
Variables can be classified as either continuous or discrete[weir2021?]. Understanding the difference between these two types of variables is important because it can affect the methods used to collect and analyze data. This section will look closely at continuous and discrete variables and their use in research.
A continuous variable is a variable that can take on any value within a specific range. For example, height, weight, and time are all continuous variables. These variables can be measured to an infinite number of decimal places. For example, height can be measured as 1.78 meters, 1.7825 meters, etc.
A discrete variable is a variable that can only take on specific values. For example, the number of children in a household is a discrete variable because it can only take on whole number values, such as 0, 1, 2, and so on. Discrete variables are countable, thus, usually measured by counting.
A continuous variable is a variable that can take on any value within a specific range, while a discrete variable can only take on specific values.
The main difference between continuous and discrete variables is how data are collected and analyzed. For example, suppose a study investigates the relationship between weight and blood pressure. In that case, weight is a continuous variable, and blood pressure is also continuous. Researchers would use a statistical technique called Pearson Correlation Coefficient to investigate the relationship between the two variables.
On the other hand, a study investigating the relationship between the number of hours of sleep and the number of absenteeism in a company would use a different statistical approach. This is because several accounts of absenteeism are discrete; it can only take a limited set of values, whereas hours of sleep are continuous and can have an infinite number of values depending on how it was measured (i.e., hours, minutes, seconds). In this case, a technique called Spearman Rho can be used to investigate the correlation between the two variables.
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. Therefore, researchers must use the appropriate statistical techniques for their specific type of variables to draw valid conclusions from their data.
Levels of Numerical Data
Nominal, ordinal, interval and ratio data are all types of numerical data[weir2021?]. However, they differ in their level of measurement and the types of statistical analyses that can be used to analyze them.
- 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 of nominal data include color, gender, or religion. Because nominal data cannot be ordered, statistical methods such as mean and standard deviation cannot be used to analyze them. Instead, nominal data is often analyzed using frequency distributions, chi-squared tests, or 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 of ordinal data include education level, socioeconomic status, or satisfaction level. Statistical methods used to analyze ordinal data include medians, quartiles, and 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 of interval data include temperature measured in Celsius or Fahrenheit. With interval data, it is possible to determine the relative magnitude of differences between values, but it is impossible to determine the true ratio of values. Statistical methods used to analyze interval data include 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 of ratio data include weight, height, or time. With ratio data, it is possible to determine the relative magnitude of differences between values and the true ratio of values. Statistical methods used to analyze ratio data include central tendency and variability measures, correlation, and regression analysis.
Nominal, ordinal, interval, and ratio data are different types of numerical data based on their level of measurement and the methods used to analyze them. Understanding the differences between these types of data is crucial because it can affect the statistical methods used to analyze and interpret the data. Moreover, selecting the appropriate type of data and analysis is important to draw meaningful and accurate conclusions.
Dependent and Independent Variables
Independent (IV) and dependent variables (DV) are two important concepts used to design and conduct experiments[weir2021?]. The independent variable is the variable that is manipulated or changed in an experiment. In contrast, the dependent variable is the variable that is measured or observed in response to the manipulation of the independent variable.
Independent variable: The independent variable is the factor the researcher is interested in studying. In Kinesiology, an independent variable could be something like a new exercise program, a specific training protocol, or a therapeutic intervention. For example, in a study investigating the effects of a new exercise program on muscle strength, the independent variable would be the exercise program itself.
Dependent variable: The dependent variable is the factor affected by the independent variable. In Kinesiology, a dependent variable could be muscle strength, cardiovascular fitness, or pain levels. Continuing with the example of the study investigating the effects of a new exercise program on muscle strength, the dependent variable would be muscle strength, measured before and after the exercise program to see if there is an improvement.
It is important to note that the independent variable is manipulated or controlled by the researcher while the dependent variable is measured or observed; this allows the researcher to establish a cause-and-effect relationship between the variables and to draw a conclusion on the effects of the independent variable on the dependent variable[huck2004?].
Another example would be a study investigating the effect of a specific training program on running performance. The Independent variable, in this case, would be the training program, and the dependent variable would be the running performance measured before and after the program.
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 important because it allows researchers to establish a cause-and-effect relationship and draw valid conclusions from their research.
Types of Research
Research is a systematic and scientific inquiry into a particular subject or phenomenon[weir2021?]. It is a process of discovering new knowledge, understanding complex issues, and making informed decisions. Research can take many forms, but it typically follows a similar process, including defining a research question, designing a study, collecting data, analyzing it, and interpreting the results.
Three common types of research are historical research, observational research, and experimental research.
Historical research: Historical research is a form of research that involves the collection and analysis of primary and secondary sources to investigate a particular event, phenomenon, or person in the past. Historical research often relies on written documents, photographs, and other artifacts to reconstruct the past. It is typically used to understand the origin of events, historical trends, and the development of ideas, institutions, or societies.
Observational research: Observational research is a type of research in which the researcher observes, but does not manipulate, the phenomenon of interest. It may involve collecting both quantitative and qualitative data, and it typically does not involve any manipulation of the independent variable. Observational research can be conducted in natural settings, such as in the field or controlled laboratory settings. In Kinesiology, observational research can be used to study athletes’ natural movement patterns or assess the effectiveness of rehabilitation programs.
Experimental research: Experimental research is a type of research in which the researcher manipulates one or more independent variables and measures their effects on one or more dependent variables. Experimental research is often used to establish cause-and-effect relationships between variables. It typically involves using control groups and randomly assigning participants to treatment or control groups. In Kinesiology, experimental research can investigate the effects of different training protocols on physical performance or test the effectiveness of different therapeutic interventions.
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 with its strengths, limitations, and purposes. For example, historical research is used to understand the past, observational research is used to study the present, and experimental research is used to establish cause-and-effect relationships between variables. Each type of research is helpful, but the choice of which type to use will depend on the research question and the study’s goals.
Internal and External Validity
Internal and external validity are two critical concepts that refer to the degree to which the results of a study can be generalized and applied to real-world situations[weir2021?].
Internal validity: Internal validity refers to the degree to which the results of a study can be attributed to the manipulation of the independent variable and not to other extraneous variables. In other words, it assesses whether the study was conducted so that the cause-and-effect relationship between the independent and dependent variables is established. In Kinesiology, internal validity is important to ensure that the results of a study can be attributed to the specific training protocol or intervention that was tested. For example, a study investigating the effects of a specific strength training program on muscle mass must control for other variables that may affect muscle mass, such as diet, genetics, or baseline muscle mass.
External validity: External validity refers to the degree to which the results of a study can be generalized to other populations, settings, and periods. In other words, it assesses whether the results of a study are generalizable beyond the specific sample and setting in which the study was conducted. In Kinesiology, external validity is essential to ensure that the results of a study can be applied to real-world situations and other populations. For example, a study investigating the effects of a specific strength training program on muscle mass in young men (sample) must be generalized the larger population.
It is important to note that internal and external validity are related but separate concepts, and a study may have high internal validity but low external validity or vice versa. Therefore, researchers should strive to achieve high internal and external validity in their studies. However, trade-offs between the two may be necessary depending on the research question and the study’s goals.
In conclusion, Statistics is a critical component of Kinesiology, providing researchers with the tools to collect, analyze, and interpret data related to human movement and performance. By understanding the role of statistics in Kinesiology, researchers can design and conduct studies that will yield valid and meaningful results. Key concepts such as measurement, variables, and research types (historical, observational, and experimental) must be understood and applied properly to ensure that the data collected is representative, unbiased, and appropriate for the research questions. Additionally, concepts like internal and external validity are essential to establish causality relationships, determine the generalizability of the findings, and to draw valid and meaningful conclusions from the study. Overall, understanding and applying statistical concepts and methods is crucial for kinesiologists to make informed decisions based on their research findings.
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@misc{furtado2023,
author = {Furtado, Ovande},
title = {Understanding the {Role} of {Statistics} in {Kinesiology}},
pages = {undefined},
date = {2023-01-11},
url = {https://drfurtado.github.io/randomstats/posts/01102023-role-statistics-Kinesiology/},
langid = {en}
}