Null Hypothesis
Null Hypothesis (McMillan, 2012, p. 49): A null hypothesis states that no significant statistical relationship or difference exists between the groups that are being compared in astudy.
This term relates to all of the studies I read for my research study analyses. They all compared the academic achievement of high school athletes and non-athletes in some way or another. While the null hypotheses were not explicitly mentioned in the studies, they all would have been something along the lines of, “There is no significant difference between the academic achievement of athletes and non-athletes in secondary schools.”
Simple Random Sampling (McMillan, 2012, p. 98): Simple random sampling is when each member of a population has an equal chance …show more content…
of being picked for a sample.
If I wanted to do a small study to observe what the overall feeling towards extracurricular sports was in a high school, I would use simple random sampling to select a group of students to interview. I would use a computer program to randomly select the participants because it is the most convenient way of choosing the students, and it would select them without any bias (by bias I mean that if one picked the participants not at random, they may purposefully select more athletes or non-athletes so they get the results they want).
Stratified Sampling (McMillan, 2012, p. 99): Stratified sampling is when the population is first separated into subgroups before the researchers select participants from each subgroup either randomly or systematically.
A study done by Kamau, Rintaugu, Muniu, and Amusa utilizes stratified random sampling. The overall population of the study was all secondary school students in Murang’a County, Kenya. When selecting the participants, the researchers first divided all the secondary schools in the county into four strata: girls’ boarding schools, boys’ boarding schools, mixed boarding schools, and mixed day schools. Then the researchers randomly selected 8 students to analyze from each school. (Kamau, 2015)
Disproportional Stratified Sampling (McMillan, 2012, p. 101): This is a form of stratified sampling where the researcher takes the same number of participants from each stratum for the sample, regardless of whether or not the number taken from each group is indicative of the proportion of that group in the actual population.
The study done by Kamau, Rintaugu, Muniu, and Amusa uses this form of stratified sampling. The same number of participants were selected from each type of school, but mixed boarding schools had by far the highest number of students in the county. (Kamau, 2015)
Purposeful Sampling (McMillan, 2012, p. 105): Purposeful sampling is a form of sampling done in qualitative research studies where the researchers select participants they believe with be “particularly informative” about the research topic being studied.
This term relates to a study I would be interested in doing which would look at which type of coaching style was most effective in breeding team success on the field. In this type of study I would use purposeful sampling to select coaches and players to interview that have been part of successful teams to see what their thoughts were about their experiences and why they believed their coaching styles were effective.
Descriptive Statistics (McMillan, 2012, p. 121): Descriptive statistics are statistics that convert a set of numerical data into indicators that summarize the overall characteristics of a sample. These include mean, percentages, and standard deviation, among other forms of statistics.
In a study done by Fox, Barr-Anderson, Neumark-Sztainer, and Wall, the researchers incorporated descriptive statistics by taking individual GPAs of high school athletes and non-athletes in Minnesota and converting that data into average scores for each group. This helped summarize the data and give the reader an easy-to-understand look at how the GPAs of the different groups compared to each other. (Fox, 2010)
Mean (McMillan, 2012, p. 125): The mean is the mathematical average of all the scores in a distribution of numerical data.
Every study I read for my research study analyses used means to display the average scores of athletes and non-athletes on different assessments, whether it was SAT scores, ACT scores, state assessments, or GPA.
Positive Correlation (McMillan, 2012, p. 128): A positive correlation is a relationship in which the increase in one variable is associated with the increase in another variable.
This term relates to the Fox, Barr-Anderson, Neumark-Sztainer, and Wall study which found a positive correlation between GPA and the number of sports played by high school students. In the analysis of the data, the researchers found that, among the high school students in the sample, a higher number of extracurricular sports played by individual students was associated with higher GPAs for those students. (Fox, 2010)
Negative Correlation (McMillan, 2012, p. 129): A negative correlation is a relationship in which the increase in one variable is accompanied by the decrease in another variable.
While I have not read a study for this class that involved a negative correlation, I do know a possible study where one might exist. With the information that has come out about CTE and how it causes depression, I would be interested in researching how the number of head injuries and athlete suffers related to their overall feeling of happiness after football. If the study held true with what has been discovered about CTE, it is likely that as the number of head injuries increased, the athletes’ overall feeling of happiness would decrease.
Structured Questions (McMillan, 2012, p.
167): Structured questions are a type of interview question that provides the participants of a study with multiple responses from which they can choose an answer. Structured questions are used for data collection in quantitative research studies.
I would use structured questions if I were to conduct a study that was meant to look into how important extracurricular sports were to high school students. I could ask questions like, “How important is it for your school to provide a variety of sports in which you can compete? It is very important, important, not very important, or not important at all?”
Longitudinal Survey (McMillan, 2012, p. 199): A longitudinal survey is used to study the same group of participants over a specific period of time. The participants are surveyed multiple times throughout the length of the study to observe how their responses may or may not have changed.
I would utilize a longitudinal survey if I were to conduct a study that investigated if there were a relationship between school connectedness and years spent in secondary school. I would create a survey and administer it to the participants each year of their high school careers to see how their feeling of school connectedness changed, or did not change, over
time.
Subject Attrition (McMillan, 2012, p. 218): Subject attrition occurs when participants drop out of a study, and their loss from the participant pool affects the results of the study.
This term could also be associated with the longitudinal study of how school-connectedness is affected by the number of years spent in secondary school. The students who do not feel connected to a school could leave that school, or perhaps even dropout, before they become upperclassmen. This could skew the results of the study as their loss could make the self-reported school connectedness of upperclassmen seem higher than it really is.
Subject Effects (McMillan, 2012, p. 220): Subject effects occur when participants change their behavior or responses when they are put into experimental situations. Participants may know what the purpose of the study is and may try to alter their responses or behaviors in order to respond more positively to what the researchers are looking for.
While subject effects were not mentioned in the Fox, Barr-Anderson, Neumark-Sztainer, and Wall study, they could have definitely affected the results of the study. In this study, the participants were asked to self-report which two letter grades they received most often, so that the researchers could estimate their GPA. (Fox, 2010) However, if the participants knew that the researchers were going to look into their academic achievement, they might have self-reported higher grades to make themselves look more intelligent.
References:
Fox, C., Barr-Anderson, D., Neumark-Sztainer, D., & Wall, M. (2010). Physical activity and sports team participation: associations with academic outcomes in middle school and high school students. Journal of School Health, 80(1), 31-37. Retrieved November 27, 2015, from SPORTDiscus.
Kamau, A.W., Rintaugu, E.G., Muniu, R.K. & Amusa, L.O. (2015). The effect of participation in competitive sports on school connectedness of secondary school students. Recreation African Journal for Physical, Health Education and Dance, 21(3), 877-890. Retrieved November 26, 2015, from SPORTDiscus.
McMillan, J. H. (2012). Educational research: Fundamentals for the consumer (6th ed.). Boston, MA: Pearson.
This term relates to all of the studies I read for my research study analyses. They all compared the academic achievement of high school athletes and non-athletes in some way or another. While the null hypotheses were not explicitly mentioned in the studies, they all would have been something along the lines of, “There is no significant difference between the academic achievement of athletes and non-athletes in secondary schools.”
Simple Random Sampling (McMillan, 2012, p. 98): Simple random sampling is when each member of a population has an equal chance …show more content…
of being picked for a sample.
If I wanted to do a small study to observe what the overall feeling towards extracurricular sports was in a high school, I would use simple random sampling to select a group of students to interview. I would use a computer program to randomly select the participants because it is the most convenient way of choosing the students, and it would select them without any bias (by bias I mean that if one picked the participants not at random, they may purposefully select more athletes or non-athletes so they get the results they want).
Stratified Sampling (McMillan, 2012, p. 99): Stratified sampling is when the population is first separated into subgroups before the researchers select participants from each subgroup either randomly or systematically.
A study done by Kamau, Rintaugu, Muniu, and Amusa utilizes stratified random sampling. The overall population of the study was all secondary school students in Murang’a County, Kenya. When selecting the participants, the researchers first divided all the secondary schools in the county into four strata: girls’ boarding schools, boys’ boarding schools, mixed boarding schools, and mixed day schools. Then the researchers randomly selected 8 students to analyze from each school. (Kamau, 2015)
Disproportional Stratified Sampling (McMillan, 2012, p. 101): This is a form of stratified sampling where the researcher takes the same number of participants from each stratum for the sample, regardless of whether or not the number taken from each group is indicative of the proportion of that group in the actual population.
The study done by Kamau, Rintaugu, Muniu, and Amusa uses this form of stratified sampling. The same number of participants were selected from each type of school, but mixed boarding schools had by far the highest number of students in the county. (Kamau, 2015)
Purposeful Sampling (McMillan, 2012, p. 105): Purposeful sampling is a form of sampling done in qualitative research studies where the researchers select participants they believe with be “particularly informative” about the research topic being studied.
This term relates to a study I would be interested in doing which would look at which type of coaching style was most effective in breeding team success on the field. In this type of study I would use purposeful sampling to select coaches and players to interview that have been part of successful teams to see what their thoughts were about their experiences and why they believed their coaching styles were effective.
Descriptive Statistics (McMillan, 2012, p. 121): Descriptive statistics are statistics that convert a set of numerical data into indicators that summarize the overall characteristics of a sample. These include mean, percentages, and standard deviation, among other forms of statistics.
In a study done by Fox, Barr-Anderson, Neumark-Sztainer, and Wall, the researchers incorporated descriptive statistics by taking individual GPAs of high school athletes and non-athletes in Minnesota and converting that data into average scores for each group. This helped summarize the data and give the reader an easy-to-understand look at how the GPAs of the different groups compared to each other. (Fox, 2010)
Mean (McMillan, 2012, p. 125): The mean is the mathematical average of all the scores in a distribution of numerical data.
Every study I read for my research study analyses used means to display the average scores of athletes and non-athletes on different assessments, whether it was SAT scores, ACT scores, state assessments, or GPA.
Positive Correlation (McMillan, 2012, p. 128): A positive correlation is a relationship in which the increase in one variable is associated with the increase in another variable.
This term relates to the Fox, Barr-Anderson, Neumark-Sztainer, and Wall study which found a positive correlation between GPA and the number of sports played by high school students. In the analysis of the data, the researchers found that, among the high school students in the sample, a higher number of extracurricular sports played by individual students was associated with higher GPAs for those students. (Fox, 2010)
Negative Correlation (McMillan, 2012, p. 129): A negative correlation is a relationship in which the increase in one variable is accompanied by the decrease in another variable.
While I have not read a study for this class that involved a negative correlation, I do know a possible study where one might exist. With the information that has come out about CTE and how it causes depression, I would be interested in researching how the number of head injuries and athlete suffers related to their overall feeling of happiness after football. If the study held true with what has been discovered about CTE, it is likely that as the number of head injuries increased, the athletes’ overall feeling of happiness would decrease.
Structured Questions (McMillan, 2012, p.
167): Structured questions are a type of interview question that provides the participants of a study with multiple responses from which they can choose an answer. Structured questions are used for data collection in quantitative research studies.
I would use structured questions if I were to conduct a study that was meant to look into how important extracurricular sports were to high school students. I could ask questions like, “How important is it for your school to provide a variety of sports in which you can compete? It is very important, important, not very important, or not important at all?”
Longitudinal Survey (McMillan, 2012, p. 199): A longitudinal survey is used to study the same group of participants over a specific period of time. The participants are surveyed multiple times throughout the length of the study to observe how their responses may or may not have changed.
I would utilize a longitudinal survey if I were to conduct a study that investigated if there were a relationship between school connectedness and years spent in secondary school. I would create a survey and administer it to the participants each year of their high school careers to see how their feeling of school connectedness changed, or did not change, over
time.
Subject Attrition (McMillan, 2012, p. 218): Subject attrition occurs when participants drop out of a study, and their loss from the participant pool affects the results of the study.
This term could also be associated with the longitudinal study of how school-connectedness is affected by the number of years spent in secondary school. The students who do not feel connected to a school could leave that school, or perhaps even dropout, before they become upperclassmen. This could skew the results of the study as their loss could make the self-reported school connectedness of upperclassmen seem higher than it really is.
Subject Effects (McMillan, 2012, p. 220): Subject effects occur when participants change their behavior or responses when they are put into experimental situations. Participants may know what the purpose of the study is and may try to alter their responses or behaviors in order to respond more positively to what the researchers are looking for.
While subject effects were not mentioned in the Fox, Barr-Anderson, Neumark-Sztainer, and Wall study, they could have definitely affected the results of the study. In this study, the participants were asked to self-report which two letter grades they received most often, so that the researchers could estimate their GPA. (Fox, 2010) However, if the participants knew that the researchers were going to look into their academic achievement, they might have self-reported higher grades to make themselves look more intelligent.
References:
Fox, C., Barr-Anderson, D., Neumark-Sztainer, D., & Wall, M. (2010). Physical activity and sports team participation: associations with academic outcomes in middle school and high school students. Journal of School Health, 80(1), 31-37. Retrieved November 27, 2015, from SPORTDiscus.
Kamau, A.W., Rintaugu, E.G., Muniu, R.K. & Amusa, L.O. (2015). The effect of participation in competitive sports on school connectedness of secondary school students. Recreation African Journal for Physical, Health Education and Dance, 21(3), 877-890. Retrieved November 26, 2015, from SPORTDiscus.
McMillan, J. H. (2012). Educational research: Fundamentals for the consumer (6th ed.). Boston, MA: Pearson.