The document analyzes the relationship between exercise, major type, study hours, and GPA for 151 UCLA students. Chi-squared tests found GPA was independent of exercise level but dependent on major type. Further tests found major type was independent of exercise level but dependent on study hours. This suggests differences in GPA across majors are driven by varying study habits rather than exercise habits.
Effect of Perceived Goal Difficulty, Perceived Exercise Exertion and Sub-Goal...Meenakshi Singh
This study examined the effects of sub-goals on motor task performance, and the relationship between perceived goal difficulty and perceived exercise exertion. 40 college students performed sit-ups over 6 weeks with sub-goals set at 20%, 40%, and 60% improvements from their baseline over 2 week periods. 24 subjects completed the study. Results showed significant improvements in sit-up performance over time, with 61% of participants achieving their 60% improvement goal within 4 weeks. Perceived goal difficulty and perceived exercise exertion both decreased significantly over time. A positive correlation was found between perceived goal difficulty and perceived exercise exertion. The results support that setting sub-goals can improve motor task performance and that perceived goal difficulty reflects the effort required to achieve
Chahine Understanding Common Study ResultsSaad Chahine
This document discusses key concepts for understanding common study results, including:
1) Studies include statistical analysis which may have flaws;
2) The objectives are to interpret various statistical analyses like confidence intervals, t-tests, and regression and differentiate statistical from clinical significance;
3) When analyzing studies, one should examine the claim, data, warrant, backing, rebuttal, qualifier, and descriptive statistics.
This document discusses key criteria for good measurement in research: validity and reliability. It defines validity as measuring what is intended and discusses three types: face validity, construct validity, and criterion-related validity. Reliability is defined as consistency of measurement and the document discusses test-retest reliability, equivalent forms reliability, and internal consistency reliability. Sensitivity is defined as a measure's ability to detect meaningful differences in responses.
This document provides an overview of different types of educational research categorized by purpose and method. The main types discussed are:
1. Basic research which aims to develop theories without focusing on practical applications.
2. Applied research which seeks to solve practical problems in fields like education, medicine, and psychology.
3. Action research which is conducted by teachers to diagnose and address issues in their classrooms.
The document also examines research methods including descriptive research, experimental research, case studies, surveys, correlation research, causal comparative studies, and historical research. It provides examples and discusses the characteristics, procedures, advantages, and limitations of each type of educational research method.
Reliability what is it, and how is it measuredanalisedecurvas
This article discusses the concept of reliability in clinical measurements. Reliability refers to the consistency or repeatability of measurements. It is important for clinicians to understand reliability to interpret their own findings and published studies. The article defines two types of reliability - relative reliability, which is the consistency of an individual's ranking compared to others, and absolute reliability, which is the variation of repeated measures of an individual. Several common methods for quantifying reliability are described, including correlation coefficients, standard error of measurement, and limits of agreement. Understanding different reliability estimates helps clinicians evaluate the value of specific measurements.
Modeling – Based Instructional Strategy for Enhancing Problem Solving Ability...QUESTJOURNAL
ABSTRACT: The modeling-based instructional framework accommodates the physics modeling mechanism in which the learner apply the fundamental principles in physics and develop an idealized physics model of the real world situation by means of assumptions and approximations. The present study was intended to find out the effectiveness of Modeling-based instructional strategy for enhancing physics problem solving ability of students at secondary school level. The investigator adopted a quasi-experimental method with two group pretest post-test design for the study. The sample selected for the study consisted of 242 IX standard students from three different schools of Palakkad district. The tools used for collecting the data were the Problem Solving Ability Test in Physics, lesson designs based on Modeling-based instructional strategy and activity oriented method. The findings of the study concluded that the Modeling-based instructional strategy enhanced the problem solving ability of students of secondary school level. And also the strategy scaffolded the formation of mental models of problem representations with in the cognitive structure of the learner.
- Reliability is a measure of reproducibility of a test when repeated, quantifying random error. Validity is how well a test measures what it intends to, requiring comparison to a criterion.
- Reliability is typically quantified by the typical error or intraclass correlation. Validity uses correlation and error of estimate from regression of the test on a criterion.
- Both reliability and validity should be high for a test to accurately track small individual changes over time and distinguish individuals. Ideal values are >0.96 for reliability and validity correlations and typical/estimate errors <20% of between-subject standard deviation.
The study examined the relationship between combat exposure, insomnia, and adjustment to college among student veterans. It found that insomnia fully mediates the relationship between combat exposure and personal/emotional adjustment. Specifically:
1) Combat exposure negatively predicted personal/emotional adjustment.
2) Combat exposure positively predicted insomnia.
3) Insomnia strongly negatively predicted personal/emotional adjustment.
4) When accounting for insomnia, combat exposure no longer predicted adjustment, indicating insomnia fully mediated the relationship.
Effect of Perceived Goal Difficulty, Perceived Exercise Exertion and Sub-Goal...Meenakshi Singh
This study examined the effects of sub-goals on motor task performance, and the relationship between perceived goal difficulty and perceived exercise exertion. 40 college students performed sit-ups over 6 weeks with sub-goals set at 20%, 40%, and 60% improvements from their baseline over 2 week periods. 24 subjects completed the study. Results showed significant improvements in sit-up performance over time, with 61% of participants achieving their 60% improvement goal within 4 weeks. Perceived goal difficulty and perceived exercise exertion both decreased significantly over time. A positive correlation was found between perceived goal difficulty and perceived exercise exertion. The results support that setting sub-goals can improve motor task performance and that perceived goal difficulty reflects the effort required to achieve
Chahine Understanding Common Study ResultsSaad Chahine
This document discusses key concepts for understanding common study results, including:
1) Studies include statistical analysis which may have flaws;
2) The objectives are to interpret various statistical analyses like confidence intervals, t-tests, and regression and differentiate statistical from clinical significance;
3) When analyzing studies, one should examine the claim, data, warrant, backing, rebuttal, qualifier, and descriptive statistics.
This document discusses key criteria for good measurement in research: validity and reliability. It defines validity as measuring what is intended and discusses three types: face validity, construct validity, and criterion-related validity. Reliability is defined as consistency of measurement and the document discusses test-retest reliability, equivalent forms reliability, and internal consistency reliability. Sensitivity is defined as a measure's ability to detect meaningful differences in responses.
This document provides an overview of different types of educational research categorized by purpose and method. The main types discussed are:
1. Basic research which aims to develop theories without focusing on practical applications.
2. Applied research which seeks to solve practical problems in fields like education, medicine, and psychology.
3. Action research which is conducted by teachers to diagnose and address issues in their classrooms.
The document also examines research methods including descriptive research, experimental research, case studies, surveys, correlation research, causal comparative studies, and historical research. It provides examples and discusses the characteristics, procedures, advantages, and limitations of each type of educational research method.
Reliability what is it, and how is it measuredanalisedecurvas
This article discusses the concept of reliability in clinical measurements. Reliability refers to the consistency or repeatability of measurements. It is important for clinicians to understand reliability to interpret their own findings and published studies. The article defines two types of reliability - relative reliability, which is the consistency of an individual's ranking compared to others, and absolute reliability, which is the variation of repeated measures of an individual. Several common methods for quantifying reliability are described, including correlation coefficients, standard error of measurement, and limits of agreement. Understanding different reliability estimates helps clinicians evaluate the value of specific measurements.
Modeling – Based Instructional Strategy for Enhancing Problem Solving Ability...QUESTJOURNAL
ABSTRACT: The modeling-based instructional framework accommodates the physics modeling mechanism in which the learner apply the fundamental principles in physics and develop an idealized physics model of the real world situation by means of assumptions and approximations. The present study was intended to find out the effectiveness of Modeling-based instructional strategy for enhancing physics problem solving ability of students at secondary school level. The investigator adopted a quasi-experimental method with two group pretest post-test design for the study. The sample selected for the study consisted of 242 IX standard students from three different schools of Palakkad district. The tools used for collecting the data were the Problem Solving Ability Test in Physics, lesson designs based on Modeling-based instructional strategy and activity oriented method. The findings of the study concluded that the Modeling-based instructional strategy enhanced the problem solving ability of students of secondary school level. And also the strategy scaffolded the formation of mental models of problem representations with in the cognitive structure of the learner.
- Reliability is a measure of reproducibility of a test when repeated, quantifying random error. Validity is how well a test measures what it intends to, requiring comparison to a criterion.
- Reliability is typically quantified by the typical error or intraclass correlation. Validity uses correlation and error of estimate from regression of the test on a criterion.
- Both reliability and validity should be high for a test to accurately track small individual changes over time and distinguish individuals. Ideal values are >0.96 for reliability and validity correlations and typical/estimate errors <20% of between-subject standard deviation.
The study examined the relationship between combat exposure, insomnia, and adjustment to college among student veterans. It found that insomnia fully mediates the relationship between combat exposure and personal/emotional adjustment. Specifically:
1) Combat exposure negatively predicted personal/emotional adjustment.
2) Combat exposure positively predicted insomnia.
3) Insomnia strongly negatively predicted personal/emotional adjustment.
4) When accounting for insomnia, combat exposure no longer predicted adjustment, indicating insomnia fully mediated the relationship.
Sørg alltid for å få nok søvn før du starter på en lang kjøretur.Vær sikker på at bilen er i førsteklasses stand, at dekkene har nok luft og at bensintanken er full. Også i tåke blir sikten dårligere, og da bør du unngå forbikjøringer.
URL:http://www.nokiantyres.no/innovativt-arbeide/sikkerhet/billige-dekk/
This document provides a brief history of modern research ethics and institutional review boards (IRBs). It describes how early human experimentation often involved abuse and coercion. The Nuremberg Code established informed consent standards after Nazi human experiments. In the US, regulations were established in 1974 requiring IRB oversight of research involving human subjects to protect their rights and welfare. IRBs ensure research proposals meet ethical standards regarding risks, benefits, and voluntary informed consent.
This document is a biography and profile of Nick Boruch. It provides biographical details about Boruch such as being born in Florida, raised in DC, and currently being a student pursuing entrepreneurship. It outlines personal qualities of Boruch such as being passionate, resourceful, inspiring, dedicated. It discusses his work experience owning a landscaping company. It shares Boruch's inspirations and goals such as owning a record label to support musicians. Contact information is provided at the end.
Crea ted and presented by mirna mandelamirnamandela
This document discusses how ICT (information and communication technology) tools can be utilized in education. It identifies common ICT tools like computers, audio devices, the internet, television, telephones, and mobile gadgets. It also explains how ICT tools can be implemented in schools through the use of learning videos, internet applications, CD Roms, and educational software programs to make learning easier for students. The conclusion states that ICT tools are very useful for teaching in the classroom and enable teachers and students to stay up to date.
INFLUENCE OF TYRE PRESSURE ON COEFFICIENT OF ROLLING RESISTANCE AND TOTAL POW...Adam Frank
In this paper a four-wheeled electric bicycle was in focus. We determined the coefficient of rolling resistance (Cr) and total power output (TPO) at five different tyre pressure levels and on three different road surfaces. Cr was estimated using riding velocity and power output and total power output was combined power output of the motor and the driver. The author assumed that the effect of tyre inflation level has an effect on Cr and on TPO but the analysis of the measured data didn't show significant difference while there was significant difference between the road surfaces. In the present study the mean change in Cr was 5,5% on asphalt, ~11% on fine gravel and ~8% on coarse gravel while former studies found higher differences on different road surfaces while the TPO data showed 4%, 3,5% and 4% of mean change on the same surfaces. The results didn't match with the results of former studies where the relationship of Cr and TPO with the tyre pressure was found to be curvilinear because the present results were closer to linear. The author hypothesises that role of the 150 kg transported weight, which is way higher than in other studies, and the unique structure affects the data more than assumed before.
Final Human Communication Ethics Paper (1)(1)James Price
This document provides a summary and analysis of Sports Illustrated's Swimsuit Edition from 2010 to 2015 and discusses the ethical implications that have evolved over the years. It notes that while the swimsuit edition brings in significant revenue, some argue it objectifies women and sends the wrong messages. However, others counter that the target audience is adult males and models consent to participate. The document also examines Sports Illustrated's code of ethics and whether prioritizing profits overrides responsibility. It concludes that traditions die hard in business and changing a lucrative product risks reduced profits, though character is called into question.
Managers have the biggest impact on employee engagement and company performance. Investing in coaching programs for managers can boost engagement up to 70% and earnings per share by 147%. BetterManager provides personalized executive coaching to help all managers develop skills like coaching, building engagement, and driving results. Their coaching program has helped clients strengthen management skills and improve productivity, engagement, and performance.
The document summarizes key insights from a panel discussion on the evolution of marketing at an event called "Marketing Unbound". Some of the main points are:
1) Modern marketers must develop strategies to reach and engage consumers, help create satisfying products, and drive business performance, not just advertise.
2) Asian consumers are highly connected and have different expectations, wanting to communicate with brands via mobile and social media.
3) Changing consumer behaviors are driving changes in how marketing is managed both internally and with partners. The role of CMOs is evolving.
The document discusses a study that examined the relationship between perceived stress levels of grade 12 technical vocational students at three points in a semester and the students' academic performance. The results showed that students experienced moderate stress overall, and perceived stress levels were significantly different at the beginning versus middle of the semester but not the middle versus end. Perceived stress at the end of the semester was related to academic performance but stress earlier in the semester was not. Most students reported insufficient sleep and eating problems throughout the semester. The study provides insights into managing student stress to improve academic outcomes.
The study examined the effects of team cognition on complex engineering tasks. It analyzed shared mental models within and between teams through repeated measures ANOVA. The results showed that task-related shared mental models increased over time within teams but not between teams, while team-related shared mental models increased both within and between teams. The study provides insights into team cognition dynamics but could be improved by addressing potential biases.
This document discusses the overrated importance of statistics in research. It argues that while statistics courses take up a significant amount of time, they focus too heavily on computation and not enough on research design skills. As an example, it outlines a simple two-group experiment and identifies several fatal flaws in the study's design that statistics cannot overcome, such as a failure to sample from the target population and a lack of random assignment to treatment groups. The document contends that research proficiency requires a broader emphasis on the entire research process beyond just statistical computation.
QUANTITATIVE RESEARCH DESIGN AND METHODS.pptBhawna173140
This document discusses key concepts in quantitative research design and methods. It covers types of quantitative research including exploratory, descriptive, and causal research. It also discusses measurement fundamentals such as concepts, variables, levels of measurement including nominal, ordinal, interval and ratio. Additionally, it covers research validity including construct validity, internal validity, external validity, and statistical validity. The document provides examples and definitions to explain these important quantitative research concepts.
Learning Outcomes1. Describe correlations and regression a.docxSHIVA101531
Learning Outcomes
1. Describe correlations and regression analyses.
2. Analyze the relationship between correlations and predictions.
Introduction
In contrast to Week Three where statistical tests focusing on differences were introduced, in Week Four, you will explore relationships in statistical tests. Correlations and linear regression techniques will be utilized and results will be evaluated and interpreted. The written assignments in Weeks One, Two, and Three prepared you for analyzing and evaluating research articles. In the written assignment this week, you will focus less on actual research and more on the report writing process.
If you work in a social/behavioral sciences field, you will likely be asked to conduct research (i.e., conduct an experiment or study) and create a report based on your findings. Generally speaking, people who investigate a scientific hypothesis have a responsibility to the scientific community to share those results. This is particularly true when that investigation adds to/or contradicts previous research. The research report outlines each step that was done during the research and summarizes the results and conclusions. The goal is to give the reader enough information so that the methods and results can be accurately evaluated, and the conclusions can be replicated if necessary. Although the research report this week will be based on hypothetical and/or fictitious data, the process of creating a correctly formatted research report with all the necessary components will provide you with important skills as you progress through your degree and as you continue into the world of the social/behavioral sciences.
Required Resources
Required Text
Read from the course text, Statistics for the Behavioral & Social Sciences:
· Chapter 8: Correlation
· Chapter 9: Linear Regression
Recommended Resources
Articles
1. Kirwan, J., Lounsbury, J., Gibson, L. (2010). Self-direction in learning and personality: The Big Five and narrow personality traits in relation to learner self-direction. International Journal of Self-Directed Learning, 7(2), 21-34. Retrieved from http://sdlglobal.com/IJSDL/IJSDL7.2-2010.pdf#page=25
· This is an article about personality, self-directed learning, and scale development and the major traits that may affect them. These include: agreeableness, conscientiousness, emotional stability, and openness. It incorporates correlation and regression procedures with tables that display the statistical results.
2. Stark, P.B. (2013). Chapter 9: Regression. Retrieved from http://www.stat.berkeley.edu/~stark/SticiGui/Text/regression.htm
· This website contains several video lectures and examples of how regression is used.
3. Trochim, W. M. (2006). Correlation. In Research Methods Knowledge Base. Retrieved from http://www.socialresearchmethods.net/kb/statcorr.php
· This website contains many tutorials and tools for statistical analyses and methods used in the social sciences. This pa ...
This document discusses quantitative research and different types of variables used in quantitative research. It describes experimental, quasi-experimental, and non-experimental research designs. Experimental research allows controlling variables to determine causation, while quasi-experimental and non-experimental designs observe phenomena naturally. The document also defines independent, dependent, intervening, control, and confounding variables and provides examples of each.
This document provides information about the required textbooks and readings, course description, learning objectives, assessment criteria, and schedule for a health psychology course. The primary textbook is Health Psychology by Brannon, Updegraff, & Feist, along with selected chapters from Health Psychology: A Cultural Approach by Gurung. The course will explore the biopsychosocial model and connections between biological, psychological and social factors that influence health. Assessment includes continual evaluation, a final exam, assignments, and tutorial participation. The course schedule outlines topics to be covered over 14 weeks, including stress, health behavior, physical diseases, and stress management.
Chapter 3 CONSTRUCT-IRRELEVANT VARIANCE IN AC.docxchristinemaritza
This summary provides an overview of the key points about test anxiety from the document:
1) Test anxiety is conceptualized in different ways, including as a stable personality trait, temporary emotional state, or clinical disorder. It occurs when individuals fear performing poorly on tests due to concerns about academic or social consequences.
2) Both situational factors (like social comparisons to peers) and personal factors (like general trait anxiety and self-esteem) contribute to the experience of test anxiety. Higher test anxiety is correlated with lower academic performance and achievement.
3) While test anxiety has substantial negative effects on outcomes like GPA and standardized test scores, some moderating variables may diminish its impact on performance. Further research investig
This study examined the relationship between exercise levels and perceived stress in 112 college students. The students completed a survey assessing their gender, class standing, exercise habits, and perceived stress levels. The results showed that stress levels decreased as exercise duration increased from under 20 minutes to 40 minutes to an hour. However, students exercising over an hour had higher stress levels, possibly due to athletic training regimens. The study suggests moderate exercise may help reduce stress for college students and warrants further research into exercise types and amounts. It also found females reported higher stress than males on average and that stress varied by class year. This initial study provides ground for establishing an educational program on the mental health benefits of physical activity.
This document discusses exploring the impact of integrating related art experiences into core subject teaching to provide differentiated learning opportunities for students. It describes a study where students participated in a basketball activity counting baskets in multiples of 2, then were tested on their understanding of multiples of 2. On average, students scored similarly on pre- and post-tests, though some individual scores improved. The lack of overall significant difference may have been because students' math skills had not progressed enough in basic operations or because many students were absent due to illness impacting results. Further research with a control group and better-timed intervention is needed.
This study examined whether gender and housing location affect student GPA. The author analyzed survey responses from 153 students on their gender, housing location (on or off campus), and GPA. A two-way ANOVA found that gender had a significant effect on GPA, with males and females differing in GPA, but housing location did not significantly impact GPA. There was also no interaction between gender and housing location.
The document summarizes a proposed research study that examines the effects of cooperative group size on mathematics achievement in middle school students. Specifically, it investigates whether groups of three students demonstrate greater achievement than groups of two. The study uses a quasi-experimental design where classes are randomly assigned to work in groups of either two (control) or three (experimental). Pre- and post-tests will measure student achievement over multiple units and the data will be analyzed to compare achievement between the different group sizes. If a difference is found, further analysis will explore potential reasons. The goal is to provide additional evidence around optimal cooperative group size.
Sørg alltid for å få nok søvn før du starter på en lang kjøretur.Vær sikker på at bilen er i førsteklasses stand, at dekkene har nok luft og at bensintanken er full. Også i tåke blir sikten dårligere, og da bør du unngå forbikjøringer.
URL:http://www.nokiantyres.no/innovativt-arbeide/sikkerhet/billige-dekk/
This document provides a brief history of modern research ethics and institutional review boards (IRBs). It describes how early human experimentation often involved abuse and coercion. The Nuremberg Code established informed consent standards after Nazi human experiments. In the US, regulations were established in 1974 requiring IRB oversight of research involving human subjects to protect their rights and welfare. IRBs ensure research proposals meet ethical standards regarding risks, benefits, and voluntary informed consent.
This document is a biography and profile of Nick Boruch. It provides biographical details about Boruch such as being born in Florida, raised in DC, and currently being a student pursuing entrepreneurship. It outlines personal qualities of Boruch such as being passionate, resourceful, inspiring, dedicated. It discusses his work experience owning a landscaping company. It shares Boruch's inspirations and goals such as owning a record label to support musicians. Contact information is provided at the end.
Crea ted and presented by mirna mandelamirnamandela
This document discusses how ICT (information and communication technology) tools can be utilized in education. It identifies common ICT tools like computers, audio devices, the internet, television, telephones, and mobile gadgets. It also explains how ICT tools can be implemented in schools through the use of learning videos, internet applications, CD Roms, and educational software programs to make learning easier for students. The conclusion states that ICT tools are very useful for teaching in the classroom and enable teachers and students to stay up to date.
INFLUENCE OF TYRE PRESSURE ON COEFFICIENT OF ROLLING RESISTANCE AND TOTAL POW...Adam Frank
In this paper a four-wheeled electric bicycle was in focus. We determined the coefficient of rolling resistance (Cr) and total power output (TPO) at five different tyre pressure levels and on three different road surfaces. Cr was estimated using riding velocity and power output and total power output was combined power output of the motor and the driver. The author assumed that the effect of tyre inflation level has an effect on Cr and on TPO but the analysis of the measured data didn't show significant difference while there was significant difference between the road surfaces. In the present study the mean change in Cr was 5,5% on asphalt, ~11% on fine gravel and ~8% on coarse gravel while former studies found higher differences on different road surfaces while the TPO data showed 4%, 3,5% and 4% of mean change on the same surfaces. The results didn't match with the results of former studies where the relationship of Cr and TPO with the tyre pressure was found to be curvilinear because the present results were closer to linear. The author hypothesises that role of the 150 kg transported weight, which is way higher than in other studies, and the unique structure affects the data more than assumed before.
Final Human Communication Ethics Paper (1)(1)James Price
This document provides a summary and analysis of Sports Illustrated's Swimsuit Edition from 2010 to 2015 and discusses the ethical implications that have evolved over the years. It notes that while the swimsuit edition brings in significant revenue, some argue it objectifies women and sends the wrong messages. However, others counter that the target audience is adult males and models consent to participate. The document also examines Sports Illustrated's code of ethics and whether prioritizing profits overrides responsibility. It concludes that traditions die hard in business and changing a lucrative product risks reduced profits, though character is called into question.
Managers have the biggest impact on employee engagement and company performance. Investing in coaching programs for managers can boost engagement up to 70% and earnings per share by 147%. BetterManager provides personalized executive coaching to help all managers develop skills like coaching, building engagement, and driving results. Their coaching program has helped clients strengthen management skills and improve productivity, engagement, and performance.
The document summarizes key insights from a panel discussion on the evolution of marketing at an event called "Marketing Unbound". Some of the main points are:
1) Modern marketers must develop strategies to reach and engage consumers, help create satisfying products, and drive business performance, not just advertise.
2) Asian consumers are highly connected and have different expectations, wanting to communicate with brands via mobile and social media.
3) Changing consumer behaviors are driving changes in how marketing is managed both internally and with partners. The role of CMOs is evolving.
The document discusses a study that examined the relationship between perceived stress levels of grade 12 technical vocational students at three points in a semester and the students' academic performance. The results showed that students experienced moderate stress overall, and perceived stress levels were significantly different at the beginning versus middle of the semester but not the middle versus end. Perceived stress at the end of the semester was related to academic performance but stress earlier in the semester was not. Most students reported insufficient sleep and eating problems throughout the semester. The study provides insights into managing student stress to improve academic outcomes.
The study examined the effects of team cognition on complex engineering tasks. It analyzed shared mental models within and between teams through repeated measures ANOVA. The results showed that task-related shared mental models increased over time within teams but not between teams, while team-related shared mental models increased both within and between teams. The study provides insights into team cognition dynamics but could be improved by addressing potential biases.
This document discusses the overrated importance of statistics in research. It argues that while statistics courses take up a significant amount of time, they focus too heavily on computation and not enough on research design skills. As an example, it outlines a simple two-group experiment and identifies several fatal flaws in the study's design that statistics cannot overcome, such as a failure to sample from the target population and a lack of random assignment to treatment groups. The document contends that research proficiency requires a broader emphasis on the entire research process beyond just statistical computation.
QUANTITATIVE RESEARCH DESIGN AND METHODS.pptBhawna173140
This document discusses key concepts in quantitative research design and methods. It covers types of quantitative research including exploratory, descriptive, and causal research. It also discusses measurement fundamentals such as concepts, variables, levels of measurement including nominal, ordinal, interval and ratio. Additionally, it covers research validity including construct validity, internal validity, external validity, and statistical validity. The document provides examples and definitions to explain these important quantitative research concepts.
Learning Outcomes1. Describe correlations and regression a.docxSHIVA101531
Learning Outcomes
1. Describe correlations and regression analyses.
2. Analyze the relationship between correlations and predictions.
Introduction
In contrast to Week Three where statistical tests focusing on differences were introduced, in Week Four, you will explore relationships in statistical tests. Correlations and linear regression techniques will be utilized and results will be evaluated and interpreted. The written assignments in Weeks One, Two, and Three prepared you for analyzing and evaluating research articles. In the written assignment this week, you will focus less on actual research and more on the report writing process.
If you work in a social/behavioral sciences field, you will likely be asked to conduct research (i.e., conduct an experiment or study) and create a report based on your findings. Generally speaking, people who investigate a scientific hypothesis have a responsibility to the scientific community to share those results. This is particularly true when that investigation adds to/or contradicts previous research. The research report outlines each step that was done during the research and summarizes the results and conclusions. The goal is to give the reader enough information so that the methods and results can be accurately evaluated, and the conclusions can be replicated if necessary. Although the research report this week will be based on hypothetical and/or fictitious data, the process of creating a correctly formatted research report with all the necessary components will provide you with important skills as you progress through your degree and as you continue into the world of the social/behavioral sciences.
Required Resources
Required Text
Read from the course text, Statistics for the Behavioral & Social Sciences:
· Chapter 8: Correlation
· Chapter 9: Linear Regression
Recommended Resources
Articles
1. Kirwan, J., Lounsbury, J., Gibson, L. (2010). Self-direction in learning and personality: The Big Five and narrow personality traits in relation to learner self-direction. International Journal of Self-Directed Learning, 7(2), 21-34. Retrieved from http://sdlglobal.com/IJSDL/IJSDL7.2-2010.pdf#page=25
· This is an article about personality, self-directed learning, and scale development and the major traits that may affect them. These include: agreeableness, conscientiousness, emotional stability, and openness. It incorporates correlation and regression procedures with tables that display the statistical results.
2. Stark, P.B. (2013). Chapter 9: Regression. Retrieved from http://www.stat.berkeley.edu/~stark/SticiGui/Text/regression.htm
· This website contains several video lectures and examples of how regression is used.
3. Trochim, W. M. (2006). Correlation. In Research Methods Knowledge Base. Retrieved from http://www.socialresearchmethods.net/kb/statcorr.php
· This website contains many tutorials and tools for statistical analyses and methods used in the social sciences. This pa ...
This document discusses quantitative research and different types of variables used in quantitative research. It describes experimental, quasi-experimental, and non-experimental research designs. Experimental research allows controlling variables to determine causation, while quasi-experimental and non-experimental designs observe phenomena naturally. The document also defines independent, dependent, intervening, control, and confounding variables and provides examples of each.
This document provides information about the required textbooks and readings, course description, learning objectives, assessment criteria, and schedule for a health psychology course. The primary textbook is Health Psychology by Brannon, Updegraff, & Feist, along with selected chapters from Health Psychology: A Cultural Approach by Gurung. The course will explore the biopsychosocial model and connections between biological, psychological and social factors that influence health. Assessment includes continual evaluation, a final exam, assignments, and tutorial participation. The course schedule outlines topics to be covered over 14 weeks, including stress, health behavior, physical diseases, and stress management.
Chapter 3 CONSTRUCT-IRRELEVANT VARIANCE IN AC.docxchristinemaritza
This summary provides an overview of the key points about test anxiety from the document:
1) Test anxiety is conceptualized in different ways, including as a stable personality trait, temporary emotional state, or clinical disorder. It occurs when individuals fear performing poorly on tests due to concerns about academic or social consequences.
2) Both situational factors (like social comparisons to peers) and personal factors (like general trait anxiety and self-esteem) contribute to the experience of test anxiety. Higher test anxiety is correlated with lower academic performance and achievement.
3) While test anxiety has substantial negative effects on outcomes like GPA and standardized test scores, some moderating variables may diminish its impact on performance. Further research investig
This study examined the relationship between exercise levels and perceived stress in 112 college students. The students completed a survey assessing their gender, class standing, exercise habits, and perceived stress levels. The results showed that stress levels decreased as exercise duration increased from under 20 minutes to 40 minutes to an hour. However, students exercising over an hour had higher stress levels, possibly due to athletic training regimens. The study suggests moderate exercise may help reduce stress for college students and warrants further research into exercise types and amounts. It also found females reported higher stress than males on average and that stress varied by class year. This initial study provides ground for establishing an educational program on the mental health benefits of physical activity.
This document discusses exploring the impact of integrating related art experiences into core subject teaching to provide differentiated learning opportunities for students. It describes a study where students participated in a basketball activity counting baskets in multiples of 2, then were tested on their understanding of multiples of 2. On average, students scored similarly on pre- and post-tests, though some individual scores improved. The lack of overall significant difference may have been because students' math skills had not progressed enough in basic operations or because many students were absent due to illness impacting results. Further research with a control group and better-timed intervention is needed.
This study examined whether gender and housing location affect student GPA. The author analyzed survey responses from 153 students on their gender, housing location (on or off campus), and GPA. A two-way ANOVA found that gender had a significant effect on GPA, with males and females differing in GPA, but housing location did not significantly impact GPA. There was also no interaction between gender and housing location.
The document summarizes a proposed research study that examines the effects of cooperative group size on mathematics achievement in middle school students. Specifically, it investigates whether groups of three students demonstrate greater achievement than groups of two. The study uses a quasi-experimental design where classes are randomly assigned to work in groups of either two (control) or three (experimental). Pre- and post-tests will measure student achievement over multiple units and the data will be analyzed to compare achievement between the different group sizes. If a difference is found, further analysis will explore potential reasons. The goal is to provide additional evidence around optimal cooperative group size.
This document provides an overview of basic statistical concepts including descriptive and inferential statistics, variables and levels of measurement, and methods of data collection and presentation. Descriptive statistics summarize and organize data, while inferential statistics make conclusions about a population based on a sample. There are various methods used to collect both primary and secondary data, including observation, surveys, and existing records. Data is typically presented through tables, diagrams, and graphs. Frequency distributions group and summarize data into classes to aid in analysis and interpretation.
This document provides an overview of basic statistical concepts including descriptive and inferential statistics, variables and levels of measurement, and methods of data collection and presentation. Descriptive statistics summarize and organize data, while inferential statistics make conclusions about a population based on a sample. There are various methods used to collect both primary and secondary data, including observation, surveys, and existing records. Data is typically presented through tables, diagrams, and graphs. Frequency distributions group and summarize data into classes to aid in analysis and interpretation.
The document summarizes a proposed research study that aims to determine whether cooperative groups of three students show greater mathematics achievement than groups of two in middle school classes. The study uses a quasi-experimental design where classes are randomly assigned to work in groups of either two (control) or three (experimental). Students' pre-and post-test scores for each unit will be analyzed to measure the impact of group size on academic achievement. If a difference is found, the results could inform best practices for cooperative learning group sizes in middle school mathematics.
The document examines the relationship between social anxiety and academic performance in grade 12 STEM students. It finds that students have a moderate level of social anxiety that negatively impacts their satisfactory academic performance. A very strong positive correlation was found between higher social anxiety and lower academic performance. The conclusion is that social anxiety hinders students' participation and skill development, affecting their grades. Recommendations include implementing wellness programs, relaxation techniques, and activities to build students' self-esteem and social skills to address this issue.
This document defines key research concepts including variables, hypotheses, and hypothesis testing. It describes:
- Variables can be independent, dependent, or moderating. Independent variables influence dependent variables.
- Hypotheses make predictions about variable relationships. The null hypothesis predicts no relationship; the alternative predicts a relationship.
- Hypothesis testing involves developing hypotheses, collecting data, and determining if results reject the null in favor of the alternative. If p<0.05, the null is rejected.
The document summarizes a proposed research study that aims to determine whether cooperative groups of three students result in greater mathematics achievement than groups of two in middle school classrooms. The study uses a quasi-experimental design where classes are randomly assigned to work in groups of either two (control) or three (experimental). Students' pre- and post-test scores for each unit will be analyzed to measure the impact of group size on academic achievement. If a difference is found, it could provide guidance on optimal cooperative group sizes in middle school mathematics.
- The document analyzes the relationship between college students' GPAs and two factors: time spent studying for exams each week, and frequency of library visits.
- A survey of 38 randomly selected Snow College students found a positive correlation between GPA and exam study time, but not between GPA and library visits.
- The results suggest Snow College could be a viable site for further research, as exam study times and GPAs were above averages, but the small sample limits conclusions.
1) The academic performance of senior high school students is declining due to different stress levels. Stress can negatively impact physical, mental, and academic success.
2) A study found that students reporting higher stress levels had poorer academic performance. Those feeling more anxious about tests performed worse. Those experiencing more negative emotions had lower engagement.
3) The study aims to determine the relationship between stress and academic performance of senior high school students. It seeks to identify stress levels and the factors affecting academic performance. The findings could help students, researchers, parents, and teachers.
1. Exploring the Effects of Exercise on Academic Success
Spencer Nelson, Brandon Yu, Kandace Mok, Katherine Delk
Introduction
In this study, we are using survey sampling techniques on UCLA students to investigate the relationship
between the amount of time spent exercising and academic performance. We are using the variables GPA,
gender, major type, time spent studying in hours per week, and the number of days per week spent exercising.
Ultimately, we would like to see if a student’s GPA is positively affected by devoting time to exercise. Our
goal is to use this project to encourage students to maintain a healthy lifestyle, and potentially demonstrate
the link between exercising and success in the classroom.
Many studies have proven that there are positive by-products from exercising outside of an individual’s
physical health. One such study, conducted by researchers at Purdue University [1], indicated that exercise
leads to reduced stress levels and this, in turn, makes students more awake (thus, allowing them to study
more). An article by the New York times [2] also noted that the more committed a student is to studying,
the more likely they are to be committed to exercising as well - we want to see if commitment to exercise
promotes a strong work ethic in the classroom. Additional research has proven that children who exercise
often are more attentive, have better time management, and have superior memory and problem solving
skills, which lead to higher scores on tests. We would like to test if a similar relationship exists within our
sample of college students, and see how strong that relationship is. Additionally, we want to see if certain
groups, such as different majors, have an influence on academic achievement.
Let’s quickly define a few parameters. By “exercise,” we mean activity requiring physical effort, carried out
especially to improve health or fitness; examples include weight-lifting, yoga, and sports. We have recorded
this variable in days spent exercising per week. Next, we define “academic achievement” strictly as GPA.
We hypothesize that we will observe evidence that increased frequency of exercise has a positive effect on a
college student’s GPA; in addition, we hypothesize that upon subsetting our data by different categories,
such as major type, this trend will still hold. In addition, we also believe that GPA will also be observed to
be strongly dependent on other factors, such as hours spent studying per week.
In total, we collected 151 responses via surveys conducted through a Google form sent out to peers.
Data Analysis
Please See Appendix for Enlarged Graphics for Data Analysis and Modeling Sections
Firstly, we would like to get a quick glimpse at our data in preparation for our modeling. For example, let’s
take a look at the distributions of our various variables of interest. We would like to investigate whether we
can realize any type of relationship between certain categories. Some interesting questions we would like to
answer include: are high levels of exercise tied to high GPAS; are high frequencies of studying tied to high
GPAs; are there differences in the distribution of GPA as you move across different majors?
Humanities Quan Science
Major Type Distribution (1a)
0102030405060
25
64 62
Low Medium High
Exercise Level Distribution (1b)
Frequency
020406080
80
42
29
<= 2 days/wk
3,4 days/wk
>= 5 days/wk
Low Medium High
GPA Level Distribution (1c)
Frequency
010203040506070
25
53
73
< 3.19
3.2−3.59
> 3.59
Low Medium High
Study Frequency Distribution (1d)
Frequency
0102030405060
33
53
65
<10 hr/wk
10−20 hr/wk
>20 hr/wk
1
2. *A note on how “major types” were divided in Fig.1a
We define Humanities as creative-thinking majors, including writing, political science, and linguistics.
Quantitative majors include statistics, mathematics, economics, and engineering. Science majors relate to
subjects tied with the life sciences, including biology, chemistry, and psychology.
Low Exerc. Med Exerc. High Exerc.
GPA and Exercise Levels (Fig. 2a)
010203040
Low GPA (<3.19)
Med GPA (3.2−3.59)
High GPA (>3.6)
Female Male
GPA and Gender (Fig. 2b)
0102030
Humanities Quantitative Science
GPA and Major Type (Fig. 2c)
05152535
(2a)/(2b)/(2c) Here we have created barplots to observe how GPA varies across different categories in
preparation for our chi-squared test of independence. Notice how in Fig. 2a, the distribution of GPA does
not seem to vary very much across different levels of exercise. Similarly in Fig. 2b, we can see that the
distributions of GPA among males and females are not radically different. However, in Fig. 2c, the distribution
of GPA seems to change as you move across different major types. In particular, under Humanities, medium
GPA levels makes the largest chunk, while for Science majors, high GPAs is the most prevalent.
Humanities Quantitative Science
01234567
Major Type and Frequency of Exercise (Fig.3a)
DaysSpentExercisingperWeek
Humanities Quantitative Science
0103050
Major Type and Hours Studied (Fig.3b)
HoursSpentStudyingperWeek
0 1 2 3 4 5 6 7
0103050
Study Hours v. Exercise Days (Fig. 3c)
Days Spent Exercising per Week
HoursSpentStudyingperWeek
(3a)/(3b) We would like to investigate if there are particular differences among our different majors which
may be accounting for the varying distributions of GPA. In Fig. 3a, we notice that the distribution of the
number of days per week spent exercising is quite similar across our three majors; in fact, Quantitative majors
and Science majors have identical distributions. However, by contrast in Fig. 3b, we can see the distribution
of the number of hours per week spent studying varies much more; in particular, Humanities majors seem to
be spending less time studying, whereas Quantitative majors have the highest median in hours spent studying
per week. We will investigate the independence of Major Type and Study Levels in the next section.
Modeling
Now, we would like to perform chi-squared tests to observe if there exists independence between our categories
of interest. For example, we will start by observing if there is any independence between GPA Levels and
Exercise Levels. We noted in our Data Analysis section that we noticed that, upon visual inspection, there
did not seem to be much variation in the distribution of GPA Levels across different Exercise Levels (see Fig.
2a). Thus, we suspect that GPA Levels and Exercise Levels are independent of one another; or in other words,
that we cannot predict GPA from Exercise Levels. More formally, we construct our hypothesis as follows.
Ho : GPA Levels and Exercise Levels are independent of one another.
Ha : GPA Levels and Exercise Levels are NOT independent of one another.
Running a chi-squared test of independence yields the following results:
2
3. 0 5 10 15 20
0.000.10
Chi−Square Density Graph: df = 4
<−−− p = 0.8248
χ2
= 1.5105
−3 −2 −1 0 1 2 3
0.00.20.4
Standard Normal
dnorm(x,0,1)
Low Ex (<= 2)
Med Ex
High Ex (>=5)
Rejection region
At a significance level of α = 0.05, we fail to reject the null hypothesis; there is convincing evidence that
knowing a student’s Exercise Level will not help us predict his or her GPA Level, and that these two variables
are independent. Notice how our standardized residuals, which can be thought of as z-values under a standard
normal curve, stay between our rejection regions.
GPA v. Major Test
Observing Fig.2c from our Data Analysis section, we notice that we do NOT have similar GPA distributions
across our different major types. In particular, medium GPA seems to make up a large proportion of the
observation in Humanities students compared to students studying Quantitative and Science topics. We
would like to test if this implies the two variables are not independent. We set up our hypotheses similarly:
Ho : GPA Levels and Major Types are independent of one another.
Ha : GPA Levels and Major Types are NOT independent of one another.
Running a chi-squared test of independence yields the following results:
0 5 10 15 20
0.000.10
Chi−Square Density Graph
χ2
= 10.589
p = 0.03159
−3 −2 −1 0 1 2 3
0.00.20.4
Standard Normal
dnorm(x,0,1)
−2.344
1.995
2.856
Humanities
Quantitative
Science
Rejection region
Because our p-value is under the significance level of α = 0.05, we can safely reject the null hypothesis; there is
convincing evidence that GPA Levels and Major Type are NOT independent. We are able to pinpoint which
categories are statistically significant. Notice how we have a highly negative residual for Science students
under our Medium GPA category of -2.34 and a highly positive residual for Science students for our High
GPA category of 1.99. This is an indication that our sample data underestimated the expected number of
Science students in the high GPA category, which was counterbalanced by overestimating the number of
Science students in the medium GPA category. Similarly, for Humanities students, our stray residual of 2.86
demonstrates our sample data underestimated the expected number of Humanities students in the Medium
GPA category, which was counterbalanced by overestimates in the Low GPA and High GPA categories.
Different Habits among Students of Different Majors?
3
4. Upon our results which show that GPA and Major are not independent, we would like to investigate if there
are certain habitual differences among students of different majors. In particular, we would like to test if
there exists independence between a student’s major against two factors: his or her level of exercise and how
frequently he or she studies per week. Let’s investigate exercise as our first variable of interest. Again, we set
up the hypotheses:
Ho : Major Types and Exercise Levels are independent of one another.
Ha : Major Types and Exercise Levels are NOT independent of one another.
Running a chi-squared test of independence yields the following results:
0 5 10 15 20
0.000.10
Chi−Square Density Graph: df = 4
χ2
= 6.559
p−value = 0.195
−3 −2 −1 0 1 2 3
0.00.20.4
Standard Normal
dnorm(x,0,1)
Humanities
Quantitative
Science
Rejection region
At a p-value of 0.195, we fail to reject the null hypothesis; there is, in fact, convincing evidence demonstrating
that Major Types and Exercise Levels are independent of one another. This confirms our first chi-squared
test, which showed that a student’s GPA and his or her exercise level were not dependent on one another.
Notice again how our standardized residuals stay outside of the critical regions of our standard normal graph.
If not exercise level, we suspect that there must be another factor influencing the differences in GPA among
different major types. We will now focus our attention on analyzing if there exists independence between a
student’s particular major and how frequently he or she studies per week.
Major Type v. Study Levels
Here, we will be investigating if there is variation in the frequency of a student’s studying based on his or her
major. Again, we set up our hypotheses similarly:
Ho : Major Types and Study Levels are independent of one another.
Ha : Major Types and Study Levels are NOT independent of one another.
Running a chi-squared test of independence produces the following results:
0 5 10 15 20
0.000.10
Chi−Square Density Graph: df = 4
χ2
= 13.123
p = 0.01069
−3 −2 −1 0 1 2 3
0.00.20.4
Standard Normal
dnorm(x,0,1)
−2.548
−1.987 2.478
2.93
Humanities
Quantitative
Science
Rejection region
4
5. At a p-value of 0.01, we reject the null hypothesis; there is convincing evidence that Major Levels and Study
Levels are NOT independent. Thus, we have shown that there does, indeed, exists differences in study habits
among students of different majors. We can observe our residual summary to pinpoint which categories are
contributing to the test’s statistical significance. In particular, notice that in our Quantitative category, our
residual of 2.48 indicates we vastly underestimated the number of students who study frequently, and this
was counterbalanced by by an overestimation of Quantitative students who had low frequencies of studying,
as indicated by the negative residual of -1.99. In addition, the opposite trend occurred among Humanities
students, where our sample data overestimated the expected number of these students with high frequencies
of studying, indicated by the negative residual of -2.55; this was counterbalanced by the underestimation of
Humanities students with low levels of studying, as indicated by the highly positive residual of 2.93.
Conclusion
Overall, our findings in this study reject our initial hypothesis that we would observe differing distributions
of academic performance among students who exercised at different weekly frequencies. Rather, it appears
that GPA distribution varies when we categorize students based upon their field of study. Furthermore, upon
dividing students by major types, we find that it is likely differences in the number of weekly hours dedicated
to studying which accounts for this non-uniform GPA distribution.
Let us revisit some of our statistical results which led to the aforementioned conclusions. After running a
chi-squared test of independence between student’s GPA levels (low, medium, high) and their weekly exercise
frequency, we obtained a p-value of 0.82, a strong indication that the two categories are independent. In
other words, we do not expect to see substantially variable GPA distributions among students who exercise
at different rates. A similar analysis between GPA levels and Major Types yielded an extremely low p-value
of 0.03; again, this was a very good indicator that we expect the distribution of GPAs to change as we move
across different major types. Indeed, our residual analysis proved this to be true; for Science students, a
highly positive residual of 1.99 showed our sample data underestimated the expected number of students
in High GPA category, and this was compensated by overestimating the number of Science students in the
Medium GPA category - indicated by a highly negative residual of -2.34. Similarly, a residual of 2.86 indicated
our sample data underestimated the expected number of Humanities students in the Medium GPA category,
which was offset by an overestimation of Humanities students in the High GPA category.
We were interested in investigating potential reasons as to why GPA distribution varied across Major Types,
so we ran two separate chi-squared tests of independence: Major Type v. Exercise Levels and Major Type v.
Study Frequency. Unsurprisingly, our test between Major Type and Exercise Levels yielded a p-value of 0.195,
demonstrating that knowing a student’s major does not give us information about his or her frequency of
exercise. This adds a level of confirmation to our first chi-squared test which showed GPA Levels and Exercise
Levels were independent. However, running a chi-squared test between Major Type and Study Frequency
yielded a p-value of 0.01, exemplifying that the distribution of student’s study frequency should be expected
to be different among different majors. Indeed, residual analysis demonstrated that we underestimated the
number of Humanities students with Low study frequency and overestimated the number of Humanities
students with High frequency of study. This, indeed, aligns with the fact that the Humanities lacked a high
proportion of its students in the High GPA category, a strong indication that hours spent studying and GPA
are strongly dependent.
Let’s discuss the real-world implications of our results. We have found evidence against the claim that there
is dependency between GPA Level and Exercise Level. And this results does make sense; one would not
expect exercise alone to be a contributor to a high GPA. Some make the claim that students who exercise
more have higher GPAs, because students who exercise more also tend to be more active academically. For
our particular sample, however, as seen in Fig. 3c, whether a student exercises zero days per week or seven
days a week, the distribution of hours spent studying seems fairly uniform. We then conclude that exercise
alone has little effect on GPA, and that higher GPAs are largely a byproduct of simply longer hours dedicated
to studying; studies which claim a relationship exists between exercise and GPA likely derive their results
from samples containing students who BOTH study highly frequently AND exercise highly frequently.
5
6. Appendix
References
[1] A study by Purdue University students investigating the effects of exercise on academic success
http://www.purdue.edu/newsroom/releases/2013/Q2/college-students-working-out-at-campus-gyms-get-better-grades.
html
[2] A study by the New York Times investigating the positive effects of exercise on cognitive abilities and
mental health.
http://well.blogs.nytimes.com/2010/06/03/vigorous-exercise-linked-with-better-grades/
Below are enlarged graphics from our Data Analysis and Modeling Sections
Humanities Quan Science
Major Type Distribution (1a)
0102030405060
25
64 62
Low Medium High
Exercise Level Distribution (1b)
Frequency
020406080
80
42
29
<= 2 days/wk
3,4 days/wk
>= 5 days/wk
Low Medium High
GPA Level Distribution (1c)
010305070
25
53
73
< 3.19
3.2−3.59
> 3.59
Low Medium High
Study Frequency Distribution (1d)
Frequency
0102030405060
33
53
65
<10 hr/wk
10−20 hr/wk
>20 hr/wk
6
7. Low Exerc. Med Exerc. High Exerc.
GPA and Exercise Levels (Fig. 2a)010203040
Low GPA (<3.19)
Med GPA (3.2−3.59)
High GPA (>3.6)
Female Male
GPA and Gender (Fig. 2b)
0102030
Humanities Quantitative Science
GPA and Major Type (Fig. 2c)
05101520253035
7
8. Humanities Quantitative Science
01234567
Major Type and Frequency of Exercise (Fig.3a)
DaysSpentExercisingperWeek
Humanities Quantitative Science
0102030405060
Major Type and Hours Studied (Fig.3b)
HoursSpentStudyingperWeek
0 1 2 3 4 5 6 7
0102030405060
Study Hours v. Exercise Days (Fig. 3c)
Days Spent Exercising per Week
HoursSpentStudyingperWeek
0 5 10 15 20
0.000.050.100.15
Chi−Square Density Graph: df = 4
<−−− p = 0.8248
χ2
= 1.5105
−3 −2 −1 0 1 2 3
0.00.10.20.30.4
Standard Normal
dnorm(x,0,1)
Low Ex (<= 2)
Med Ex
High Ex (>=5)
Rejection region
8
9. 0 5 10 15 20
0.000.050.100.15
Chi−Square Density Graph
χ2
= 10.589
p = 0.03159
−3 −2 −1 0 1 2 3
0.00.10.20.30.4
Standard Normal
dnorm(x,0,1)
−2.344
1.995
2.856
Humanities
Quantitative
Science
Rejection region
0 5 10 15 20
0.000.050.100.15
Chi−Square Density Graph: df = 4
χ2
= 6.559
p−value = 0.195
−3 −2 −1 0 1 2 3
0.00.10.20.30.4
Standard Normal
dnorm(x,0,1)
Humanities
Quantitative
Science
Rejection region
9
10. 0 5 10 15 20
0.000.050.100.15
Chi−Square Density Graph: df = 4
χ2
= 13.123
p = 0.01069
−3 −2 −1 0 1 2 3
0.00.10.20.30.4
Standard Normal
dnorm(x,0,1)
−2.548
−1.987 2.478
2.93
Humanities
Quantitative
Science
Rejection region
10