The hospitality industry is said to be a significant and distinctive economic force. Do you agree with this statement? Why or why not? Which issues can impact hospitality marketing strategies in the next decade? Why? What do you believe will be the most significant issue to impact hospitality marketing strategies in the next decade? Why? There are various sectors of the hospitality industry including airlines, attractions, car rental, cruise lines, foodservice, lodging, and recreation management. Select one of the sectors. Discuss the greatest driving forces or factors that contribute to the sector’s growth. APA format. 1 page

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As requested this Assignment has to be re-writen for free as this exact assignment was written by you and failed I was promised this will be solved by rewriting this assignment as detailed on work that will be sent. I need to pass this one as I will fail overall module if I fail again (I have passed all other assignments for this module). Harvard

STUDENT FEEDBACK FORM

             Institute of Sport and Exercise Science

MODULE: MSPO4001	ASSIGNMENT NUMBER: 02 Data Handling
STUDENT NUMBER:	This assignment is part of a moderated sample   YES
Your feedback comes in two parts. Section 1 (diagnostic comments) is designed to help you identify the strengths of the assignment and to give you guidance about how you can improve the overall quality of future assignments. The second section (marking matrix) explains how your final grade was determined.  This feedback will help clarify those things that you did not understand. Please see your tutor for further clarification or click http://www.worcester.ac.uk/studyskills/documents/Using_feedback_to_improve_your_work_2011.pdffor more advice.
Diagnostic commentsWhat you did well: ·         Provided a basic overview of 5 relevant research articles ·         Demonstrated a basic appreciation of some of the fundamentals of data handling; particularly with regards to the use of quantitative methods ·         Offered some cogent observations and critiques of key data handling approaches in some areas.       In order to improve Greater attention needed on developing critical appraisals of the data handling techniques. Additional explanation and depth of critique needed throughout Aim for greater clarity, concision and coherency in the writing. Further engagement with a broader array of literature would have sharpened the criticality It is advised that you improve the structure of your written work, spelling and grammar, go to http://www.worcester.ac.uk/studyskills/documents/Essay_writing_2011.pdf It is advised that you improve your presentation skills, go to http://www.worcester.ac.uk/studyskills/documents/Making_oral_presentations_2011.pdf For information on all study skills workshops, go to http://www.worcester.ac.uk/studyskills/documents/Student_Workshop_Guide_2011.12_FINAL.pdf Follow this link for a short guide on assignments http://release.worc.ac.uk/watch.php?r=J6PF40FD&s=Y
Use of LiteratureIn some places ok, however a far greater breadth and depth needed in order to demonstrate engagement with research methodology and an engagement with relevant approaches in the chosen field of study.
ReferencingOk, though with some errors and inconsistencies throughout. If problems have been identified with your referencing of material, it is advised that you go to http://www.worcester.ac.uk/studyskills/documents/Plagiarism_Referencing_2011.pdf or contact askalibrarian@worc.ac.uk
First Marker Name:                                          Date:                                                                mark:
Second Marker Name:                                     Date:                                                                mark:
AGREED FINAL MARK:		E (FAIL)

 

 

Your grade is based on assessment of the following information (these do not carry equal weighting and are for guidance only) –

	DISTINCTIONA	MERITB-C	PASSD	FAILE-H
Article selection			At least 5 research articles with full text available as either hyperlink or appendix
Validity, reliability,  credibility, transferability	Validity & reliability or credibility & transferability, of the method, instruments, tools or tests used to collect the data have been critically evaluated through comparison with both research methods texts and research article use to reach a coherent fully justified conclusion	Validity & reliability or credibility & transferability, of the method, instruments, tools or tests used to collect the data have been evaluated through comparison with both research methods texts and research article use to reach a coherent justified conclusion	Validity & reliability or credibility & transferability, of the method, instruments, tools or tests used to collect the data have been described predominantly with some reference to research methods texts and /or research articles to reach a conclusion.
Data preparation	Data preparation methods employed have been critically evaluated through comparison with both research methods texts and research article use to reach a coherent fully justified conclusion	Data preparation methods employed have been evaluated through comparison with both research methods texts and research article use to reach a coherent justified conclusion	Data preparation methods employed have been described predominantly with some reference to research methods texts and/or research articles to reach a conclusion	FAIL
Data analysis	Identified data analysis has been critically evaluated through comparison with both research methods texts and research article use to reach a coherent fully justified conclusion	Identified data analysis has been evaluated through comparison with both research methods texts and research article use to reach a coherent justified conclusion	Identified data analysis has been predominantly described predominantly in relation to research methods texts and/or research articles to reach a conclusion	FAIL
Data interpretation	Interpretation of the data analysis and drawn conclusions have been critically evaluated through comparison with both research methods texts and research article use to reach a coherent fully justified conclusion	Interpretation of the data analysis and drawn conclusions have been evaluated through comparison with both research methods texts and research article use to reach a coherent justified conclusion	Interpretation of the data analysis and drawn conclusions have been predominantly described with reference to research methods texts and/or research articles to reach a conclusion	FAIL
Limitations	Strengths &/or limitations in the articles have been clearly identified and are fully supported by critical evaluation in relation to both research methods texts and research article use to reach a coherent fully justified conclusion regarding the academic merit of each article and any combinations of the five chosen.	Strengths &/or limitations in the articles have been identified and are supported by evaluation in relation to both research methods texts and research article use to reach a coherent justified conclusion regarding the academic merit of each article.	Strengths &/or limitations in the articles have been identified and in relation to research methods texts and/or research articles to reach a conclusion regarding the academic merit of each article.
Proposed data handling & analysis section for the research proposal***	Proposed data handling and analysis techniques will be fully supported from appreciation of previous research texts, findings and analysis.  They will be detailed and where appropriate (quantitative data) proposed statistical tests will be identified in specific relation to their use to answer the aims/question. Handling & analysis of qualitative data will be fully underpinned with both methodological and research supported justification.	Proposed data handling and analysis techniques will be predominantly well justified from previous research texts, findings and analysis.  They will be detailed and where appropriate (quantitative data) proposed statistical tests will be identified in relation to the proposed research aims. Handling & analysis of qualitative data will be underpinned with methodological and/or research supported justification.	Proposed data handling and analysis techniques will be justified, and where appropriate (quantitative data) proposed statistical tests will be broadly identified but not in relation to the aims or research questions of the proposal. Handling & analysis of qualitative data will be appropriate with some methodological and/or research supported justification.
Reference list	The list will be fully in accordance with the HRS.	The list will be predominantly in accordance with the HRS.	The list will be mostly in accordance with the HRS.
Communication	Throughout, the material will be communicated very effectively and will be fully referenced in accordance with the Harvard Referencing system with excellent grammar and spelling throughout.	Throughout, the material will be communicated effectively and will be referenced in accordance with the Harvard Referencing system with very good grammar and spelling throughout.	Throughout, the material will be communicated well and in the appropriate format and will be predominantly referenced in accordance with the Harvard Referencing system with good grammar and spelling throughout.

***You must write a proposed data handling & analysis section to follow on from your proposal (assessment 1) that should be written in the FUTURE tense and should identify everything you intend to do with your data with the rationale for the chosen method of data collection, handling and subsequent analysis clearly evident (max 500 words).

• In the case of quantitative data, this will revolve around the identification of what you are testing for and why the test you will perform on the data is the most appropriate one to do – you MUST explain this and not just put the test.
• For qualitative data, you should identify the approach you will take to organising, analysing and interpreting your data and the development of themes and theories if applicable.

Research Method

Assignment 2: Data Handling

Student ID: 13007727

The choice of data handling methodology is influenced by the collection strategy, type of variables under study, accuracy needed, data collection point, and the skills of the researcher (Guenther 2013). Importantly are the links between the variables, their sources, and the practical methods that will be utilised for its collection as they are crucial in the choice of appropriate data handling methodologies.

In qualitative methodology, data collection embraces techniques that are either unstructured or semi-structured. Notably, the methods constitute of focus groups, direct observations, and individual interviews. In the methodology, the chosen sample size is small and respondents are randomly selected. Premised on Landau et al (2013) assertion, qualitative methodologies are crucial in giving insights to problems and further developing a platform of generating ideas and hypotheses for the study.

On the other hand, quantitative methodologies seek to quantify a study problem by sourcing numerical data that is later transformed into figures that can be statistically interpreted. The methodology embraces structured techniques of data collection and handling. Particularly, surveys that can be online-based, paper-based, or mobile-based are utilised for data collection. As data analysis is a process of applying statistical techniques in describing, illustrating and evaluating data (Weathington et al 2012) this study will be premised on appraising data collection, handling, analysis and interpretation in the selected articles. Identifying limitations borne from the employed data collection, handling analysis and interpretation constitutes the scope of this study. This study will also succinctly propose the data handling methodology for the proposal ‘The effects and importance of Team cohesion on Sports performance’ that I will do later.

Reliability

Reliability of a research methodology refers to how free the method is from measurement errors (

Articles

Interactive effects of team cohesion on the perceived efficacy in Semi-Professional Sport

The research design within the article is well described and methods clearly explained and the choice oOverall the buildup and undertaking of the research was well planned and clearly described.

The Multidimensional Sports Cohesion tool (MSCI: Yukelson et al. 1984) that had previously been translated into Spanish by the researchers was utilized in assessing the team’s cohesion. The inventory had 22 items that sought assess to the cohesion aspects that included teamwork, valued roles, unity of purpose, and attraction. The item responses were scaled on a 5-point Likert scale that ranged from “strongly disagree” having a score of 1 to “strongly agree” having a score of 5. In addition, a sociogram was also developed to assess the team’s cohesiveness.

Additionally, a questionnaire premised on Bandura`s (2006) assertions was used to measure efficacy; measuring both the players’ and the coaches’ perceptions towards team member’s levels of efficacy. Similarly, responses were scaled on a 5-point Likert scale.

In order to effectively analyse the data, descriptive, and correlational analysis were utilized in developing the study’s first hypotheses. Secondly, regression analysis was done to facilitate the verification of assertions raised in the hypotheses. Using an SPSS version 15, a statistics program, data was analysed.

This study establishes that the choice of the methodology was relevant to the study. In seeking to determine team cohesion, a method of assessing the cohesion aspects (MSC) was a prerequisite. The data collection methodology is reliable since the obtained value of Cronbach alpha for the self-efficacy instrument was above 0.8. The descriptive statistics that includes mean, standard deviation, and variance were for the analysis of variables that include cohesion, sociogram, and efficacy levels.  The correlation of the variables (rxy) was also computed to obtain the coaches’ perceptions regarding athletes’ efficacy and that of the teammates. This study establishes that the approaches to data analysis were appropriate to the type of data collected.

Improving the Performance of a Basketball Team via Development of Cohesion in Sport Group

This article was composed using mixed methods consisting of observation and socio metric Survey methods both of Qualitative nature in order to obtain data from the respondents. The researchers physically observed how the players communicated to each other and determined their level of cohesion and socialization.

The socio-metric survey method in data collection was also utilized in assessing relationships between the players, establishing insight to the relationship, examining and evaluating the structure and social status of the players. Similarly, it measured the acceptance or rejection level among the players. Premised Donley & Grauerholz (2012) observations, the indicators for socio-metric test enable classification of individuals based on acceptance, rejection or isolation level in a group.

The socio-gram constituted of putting the subject that met the highest points (the one having the social status highest index) at the center of the concentric circles and the chart of preference marked unilaterally or mutually. Participants were then required to first write on the paper numbers ranging from 1 to 3 in list A: Listing 3 team-mates in the order of socialization and list B: giving a list of 3 team-mates least associated with.

In data analysis, the statistical indicators that sought to expose the relationship between team’s communication, the level of cohesion and socialization and performance of the basketball team were computed. Particularly, a socio metric analysis that reflected all the rejections and elections tables where constructed. A socio-metric that constituted the initial and final tests where also erected. This study establishes that the choice of methodology (socio-metric survey) was good has it enabled the researchers to collect information from players. Based on Kerzner (2013), surveys enable participants to give candid responses.

The approach to data analysis was appropriate to the type of data collected because it enabled the researchers to assess what they intended to. Notably, the analysis exposed that some team-mates were elected more than others; some rejected while others were found isolated. Additionally, this study establishes that the data collection methodology was reliable since the computed Cronbach alpha for the socio-metric survey instrument was 0.7. However, the observation method utilized in observing how the players communicated to each other in determining their level of cohesion and socialization is subject to bias and as such, the data collection methodology cannot be conclusively relied on in addition to not explaining the nature of their Observation method.

Relationship among the Athlete’s Leadership Behaviors and Team Cohesion in Sports

This article sought to examine the influence athlete leadership behaviors have on team cohesion perceptions. Group Environment Questionnaires (GEQs) (Carron et al, 1985) were administered to the participants whose items sought to determine the scale of cohesion and Leadership in Sports and further assess leadership behaviors of athletes. This method is one of the most widely used methods to assess cohesion in sport and according to Carron et al (1998) provides ample evidence of predictive, consistent inventory of factorial validity.

The GEQs contained an 18-item inventory, which sought to assess the core dimensions of cohesion. Similarly, an Individual Attractions questionnaire that had four items was also utilized in determining the feelings of an individual team player.

All items contained in the GEQs and the Individual Attractions questionnaire were scored and scaled on Likert scale of 9-point (‘strongly disagree =1’ to ‘strongly agree = 9’). In data analysis, alpha coefficients based on the cohesion dimension of the Group Integration were computed. It was concluded that individual perceptions regarding Training and Social Support had a positive correlation with the cohesion dimensions. On the other hand, autocratic behavior was found to have a negative relationship with the dimensions of cohesion.

This study established that the choice of the methodology was good. This is because using the GEQs enabled the participants to elicit responses that facilitated the researchers obtain information that was necessary for determining the core dimensions of cohesion. Similarly, the choice of the Individual Attractions questionnaire was good as it enabled the assessment of feelings of individual team players. It is also established that the approach to data analysis utilized (determination of alpha coefficients) was appropriate to the type of data collected as it enabled the correlation between the cohesion of the dimensions be determined.

However, the GEQs and the Individual Attractions questionnaires did not inclusively cover all the aspects of athlete leadership behaviors. As such, the methodology cannot be conclusively said to have elicited all the perceptions regarding team cohesion.

Experimental Examination of Cohesion Performance Relationships in Interactive Team Sports

This article sought to experimentally examine the relationship between sports team cohesion and performance. This study consisted of a large scale of participants carefully selected and a very small fraction eliminated after the initial first phase due to incomplete team assessment results that would of reflected negatively on the reliability of this research if not eliminated. The methods were clearly explained in addition to a good quantity of suitable participants was used for this research.

Quantitative questionnaires based on cohesion producing and cohesion-reducing manipulation were administered to all participants whose participation enabled them to earn an extra credit in their respective psychology courses. This criterion discouraged undergraduate students who had inadequate knowledge or did not know anything at all regarding playing basketball.

Additionally, the undergraduate student participants were presented with Group Environment Questionnaire (GEQ; Carron et al1985) that sought to determine their age, education level, and ability of playing basketball. Besides, the questionnaire sought to quantify amount of leisure time that is spent in playing basketball. Particularly, the undergraduate male participants were required to respond to the statement, ‘Indicate the number that best estimates your skill in basketball’. A 7-point Likert scale that ranged from ‘Never = 1 to Very Good = 7’ was utilized. Additionally, the participants were required to respond to a statement; ‘Indicate the number estimating how often you participate in basketball.’ A 5-point Likert scale that ranged from ‘Play Once daily = 1 to Never Play = 5’ was employed.

Data analysis done was premised on hypotheses one and two:

Hypothesis 1: Does the increase of task cohesion have an influence on performance?

Analysis of Chi-square was executed to determine the CP and CR. In addition, Multivariate Analysis of Variance (MANOVA) was also computed.

was utilized in analyzing this hypothesis.

The choice of the data methodology was good in accessing information necessary for this study. The methodology was reliable because the computed Cronbach’s alphas for the tools ranged from 0.75 to 0 .79. The approach to data analysis is appropriate to the type of data collected. However, this study establishes that rewarding extra credit to participants in their respective psychology courses compromised the reliability of the data collected.

A protective multilevel examination of the relationship between cohesion and team performance in elite youth sport

Within this article the main object how to examine the bidirectional character of the association between cohesion and performance was clearly demonstrated. The second objective was to translate, develop and test a new translation of the Environment questionnaire (YSEQ). The process of the methods used where of clear context, distinguishing the optimal participants by using two countries with very similar languages and cultures.

The translation of the YSEQ was executed with the well-established parallel back translation method (Brislin, 1970) and a team of qualified interpreters and other professionals to deliver.

The research was divided into 3 stages with time gaps of 4 weeks- 4 months between each stage. In stage one a discrepancy within the two new translated YSEQ questionnaires occurred and had to be rectified and slightly changed the contentent of the questionnaire and therefore invalidated part of the first results which where not included in the overall subscale scores (Eys et al 2007).  Never less with a revised translation for stage two and three acceptable reliability coefficients could be obtained.

Confirmatory factor analyses was performed to conduct the gathered data and manifest in descriptive statistics and standardized factor loadings. These where all well explained and demonstrated.

Over the 3 stages participant numbers fluctuated and the lack of competitive sport within 2 stages in addition to the discrepancy within both first translated questionnaires negatively influence the overall results leading to the conclusion that the data is low validity.

 

Proposal Title: The Effects and Importance of Team Cohesion on Sports Performance

This proposal will employ both qualitative and quantitative approaches in data collections. 150 participants from three soccer teams and three basketball teams will be randomly selected. A Multidimensional Sports Cohesion tool will be utilised for data collection. This is because questionnaires are effective in evoking the respondent’s perceptions and experiencesThe anonymity aspect of the questionnaires gives room to respondents to elicit candid responses in regard the effects and importance of team cohesion on sports performance. In addition, the researcher will observe how players relate and communicate to one another.

An Individual Attractions questionnaire also based on the Likert scale (‘strongly = 1 to strongly agree = 5’) will be employed in order to determine the feelings of individual team players towards team cohesion.

By use of SPSS version 21, data will be analyzed where the correlation, descriptive statistics and correlation coefficients will be anlaysed to determine the effects and importance of team cohesion on Sports performance. Particularly, computation of the alpha coefficients will be appropriate to the type of data collected in determining the correlation between the dimensions of team cohesion and team performance.

In order to ensure reliable data handling methodology, reliability test for both the Multidimensional Sports Cohesion and Individual Attractions tools will be conducted by determining Cronbach coefficient value. A coefficient of 0.7 or greater will be considered desirable to regard the research instruments reliable for data collection. Additionally, test-retest will be done to ensure the reliability of a research instrument, therefore, ensure a reliable data collection methodology.

Reference List

Baker, L. (2006). Research methods. Baltimore, MD: Johns Hopkins University Press.

Burns, R., & Burns, R. (2014). Business research methods and statistics using SPSS. Los Angeles: SAGE.

Brislin, R. (1970). Back-Translation for Cross-Cultural Research. Journal of Cross-Cultural Psychology, 1(3), pp.185-216.

Carrón, A. V , Widmeyer, W.N., & Brawley, L.R. (1985). The development of an instrument

to assess cohesion in sports teams: The Group Environment Questionnaire. Journal of

Sport Psychology, 7, 244-266.

Caruana, N. (2011). Research methods. Lausanne: AVA Academia.

Comstock, G. (2013). Research ethics: A philosophy guide to responsible conduct of research study. Cambridge: Cambridge University Press.

Dane, F. C. (2011). Research methods. Pacific Grove, CA: Brooks/Cole Pub.

Dominowski, R. L. (2009). Research methods. Englewood Cliffs, NJ: Prentice-Hall.

Donley, A. M., & Grauerholz, E. (2012). Research methods. New York, NY: Facts on File.

François Vellas. (2011). Indirect Impacts on the Tourism Sector: An Economic Analysis. Hoboken, N.J.: John Wiley & Sons.

Graziano, A. M., & Raulin, M. L. (2012). Research methods: A process of inquiry. New York: Harper & Row.

Guenther, M. (2013). Intersections: Enterprise designs bridge the gaps between business, technology and the people. Waltham, MA: Elsevier.

Hall, C. (2010). Tourism in Australia: The Development, issues and the changes (5th ed.). Frenchs Forest, N.S.W.: Pearson Education Australia.

http://t20.unwto.org/sites/all/files/pdf/111020-rapport_vellas_en.pdf

Huang, Wu and Ho (2014). Ethics, business and society managing responsibly. Los Angeles: Response Books.

Kerzner, H. (2013). Project management case studies (4th ed.). New York: John Wiley & Sons.

Landau, R., & Shefler, G. (2013). Research ethics. Jerusalem: Hebrew University Magnes Press.

Lee, N., & Lings, I. (2010). Business Research: Guide theory and practice. Los Angeles: SAGE.

Lindzey, G. (2008). Research methods. Reading, Mass.: Addison-Wesley.

LLC., D., (2015). Strategic Capabilities: Bridging Strategy and Impact, s.l.: Deloitte.

McBurney, D., Middleton, P., & McBurney, D. (2010). Research methods. Pacific Grove, CA: Brooks/Cole Pub.

McNabb, D. E. (2012). Research methods in public administration and nonprofit management: Quantitative and qualitative approaches. Armonk, NY: M.E. Sharpe.

McNeill, P. (2011). Research methods. London: Tavistock Publications.

Montgomery, T., & Douglas, C. (2014). Statistical Quality Control. John Wiley & Sons.

Patrau, D. (2011). The evaluation of the link between talent and potential of human resources. Vasile Alecsandri the University of Bacau.

Pound, E., & Bell, J. (2015). Factory for management: Leaders’ improved performance on the six sigma world. Waltham, MA: Elsevier

Richard, B., (2010). Research methods. Bridgetown, Barbados: Distance Education Centre, University of the West Indies.

Sivia, D. S. (2013). Data analysis: A Bayesian tutorial. Oxford: Clarendon Press.

Slade, S. (2013). Case-based reasoning: A research paradigm. The New Havens, CT: Yale University, Dept. of Computer Science.

Sobczyk, J., & Kicki, J. (2013). An Economic Evaluation: Risk analysis of the mineral projects. London: Taylor & Francis.

Stanley, B., & Nelton, G. B. (2012). Research ethics: A psychological approach. Lincoln: University of Nebraska Press.

Walton, M. (2009). Research methods. Hull: University of Lincolnshire and Humberside.

Weathington, B., & Cunningham, C. (2012). Understanding business research. Hoboken, N.J.: John Wiley & Sons.

Wheelen, T., & Hunger, J. (2014). Strategic management and business policy (8th ed.). Upper Saddle River, NJ: Prentice Hall.

Yinan, Hao and Zhenming (2013). Strategic capabilities: Relationship to organisations’ success and measurement: Same pointers from the five Australia’s studies, s.l.: Emeralds Insight.

Yukelson, D. (1997). Principles of effective team building interventions in sport: A direct services approach at Penn State University. Journal of Applied Sport Psychology, 9(1), pp.73-96.

 

This is IMPORTANT!!! The Assignment has to be EDITED and UPDATED with the below but not totally rewritten!!

RE-ASSESSMENT REQUIREMENT

Module Code: MSPO4001	Module Title: Research methods.
Assignment no: 002	Title: Data handling	Word length:2500
Tutor: Dr Julia West

Re-assessment question:You must submit a front sheet showing how you engaged with the feedback from your first attempt and detailing the changes you have made in the work as a result of this. These changes should also be highlighted within the work. Select a minimum of five of the research articles (not reviews) that you have used to underpin your research proposal that have gathered, analysed and drawn conclusions from their data, preferably using a range of data collection methods and analysis.  Make sure that for each, the full article is available in the assessment either as a hyperlink or as a full text in the appendix. For each article, appraise their data collection, handling, analysis and interpretation and throughout in relation to supporting academic research methods literature and other research articles that may have previously used similar approaches, to include critical evaluation of: ·         the validity & reliability or credibility & transferability, of the method, instruments, tools or tests used to collect the data; ·         the way in which the collected data has been prepared for analysis; ·         the way in which the data has then been analysed; ·         the appropriateness of the interpretation of the data analysis presented and the conclusions drawn from this; ·         the identified (and unidentified) limitations borne from the employed data collection, handling, analysis and interpretation in order to make judgement regarding the academic merit of each article. ·         Developed from this deeper understanding, you must then write a proposed data handling section to follow on from your proposal (assessment 1) that should be written in the FUTURE tense and should identify everything you intend to do with the raw data once you have collected it with the rationale for the chosen method of data handling and subsequent analysis clearly evident. o   In the case of quantitative data, this will revolve around the identification of what you are testing for and why the test you will perform on the data is the most appropriate one to do – you MUST explain this and not just put the test. o   For qualitative data, you should identify the approach you will take to organising, analysing and interpreting your data and the development of themes and theories if applicable.   Assessment Format and Weighting This is an individual, written assignment & it represents 50% of the marks available for this module. The marking criteria is the same as on the blackboard page for this module.

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To prepare, research the history of your popular culture artifact over time. This time period should be at least 50 years but may be longer. Consider the following: • How has the audience changed over time in relation to your popular culture artifact categories? • How have the popular culture artifact categories either reflected or opposed the audience change? • If the audience has changed over time with regard to one of the popular culture artifact categories, describe the new audience. 400- 500 words Music within Race/ethnicity is the Topic

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Explain the concept of total safety management (TSM). What are some of the positive outcomes of a TSM program? What can a safety and health manager do to keep up with the current trends in the fostering the strategic elements of TSM at their companies?

Your response must be at least 200 words in length. You are required to use at least your textbook as source material for your response. Use of information from research and readings beyond the material presented in your textbook is strongly encouraged. All sources used, including the textbook, must be referenced; paraphrased and quoted material must have accompanying citations.

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How do dietary records change with day of diet diary/24hr recall? Does the accuracy of recording change with exercise intensity?( people after 24hours diet intake seems like have a high mean of Kcal maybe becoz they can remember their meal more accurately) maybe people after 3 days diet intake may easily forgot or underestimate what have they eaten i have compared some data WITH paired T – test to analysis doing high intensity exercise and energy intake (Kcal) for 3 days and energy intake for 24 hours. In the first data is for male and feamle than i analysis the data between male and female separately. MET more high(>3) that means more intense the exercise are. And in my data paried sample test there is a highlighted (sig) that means the closer the number is to 1 it is more likely a random event.(sig)The lower the value the more significant. If u dont understand the data plz contact me . The report is divide into four parts 1. Background Think about the nature of the question you specifically want to ask heres some question example u can use How do dietary records change with day of diet diary/24hr recall? Does the accuracy of recording change with exercise intensity?( people after 24hours diet intake seems like have a high mean of Kcal maybe becoz they can remember their meal more accurately) maybe people after 3 days diet intake may easily forgot or underestimate what have they eaten Does the accuracy of recording change with gender? Are people who conduct the highest intensity exercise more likely to have a higher energy intake than those who do not And u may mention that there is significant difference in physical activity and gender . the Second part is Method those are: diet diary(24 hours and 3 days) we used a website called Nutritics for record our 24hrs and 3 days diet diary statistics analysis for stat i used paried t test and find it is siginificant or not and also use excel graph to find their mean and standard error questionnaire 3. REsult here u can post my paried t test result and my excel graph and excel stat finally 4. discussion What do the data you have presented demonstrate? What are the biological implications of your findings? What are the “other” implications of your findings? How would you take the study further? What would you ask next? harvard

Revised April 2004 1 Guidelines for Data Processing and Analysis of the International Physical Activity Questionnaire (IPAQ) – Short Form, Version 2.0. April 2004 Introduction This document provides an revision to the outline for scoring the short form of the International Physical Activity Questionnaire (IPAQ) . This is available on the website www.ipaq.ki.se. There are many different ways to analyse physical activity data, but to-date there is no consensus on a ëcorrectí method for defining or describing levels of activity based on selfñreport surveys. The use of different scoring protocols makes it very difficult to compare within and between countries, even when the same instrument has been used. IPAQ is an instrument designed primarily for population surveillance of adults. It has been developed and tested for use in adults (age range of 15-69 years) and until further development and testing is undertaken the use of IPAQ with older and younger age groups is not recommended. IPAQ is being used also as an evaluation tool in some intervention studies, but the range of domains and types of activities included in IPAQ should be carefully noted before using it in this context. This document describes the April 2004 revision to the IPAQ short scoring protocol1 . These revisions are have been suggested by the IPAQ scientific group, to examine variation among countries in more detail2 . Given the broad range of domains of physical activity asked in IPAQ, new cutpoints need to be trialed and developed to express physical activity in the population. These cutpoints are preliminary, in the sense that they are not yet supported by epidemiological studies, which have typically used Leisure time physical activity (LTPA) to examine benefits or risks of being active. Hence, ì30 minutes of moderate intensity PA on most days of the weekî was evidence-based, using the estimates of risk (reduction) from these LTPA measures in numerous epidemiological studies. A new set of suggested cutpoints is based on work in the area of total physical activity, specifically total walking, where recommendations of at least 10,000 steps, and possibly 12,500 steps per day are considered ëhigh activeí (Tudor Locke reference). This equates to at least 2 hours of all forms of walking per day, which includes all settings and domains of activity, and could be a population goal for total HEPA (health-enhancing physical activity). With this background, new cutpoints are proposed for expressing physical activity levels in populations using generic physical activity measures such as IPAQ3 . 1 The first version of an IPAQ scoring protocol was in August 2003; this is a revised version, April 2004. This revised version does not change the continuous forms of reporting data, but does suggest a new category for describing the most active groups in populations. The changes from the August 2003 scoring protocol are indicated in this document. 2 Previous scoring algorithms returned high prevalence rates with limited variation among countries; hence a higher cutpoint is sought, as the IPAQ instrument measures total PA, including LTPA as well as incidental, occupational and transport related PA all in one question. This results in much higher prevalence estimates than measures of LTPA alone. 3 This results in changes to the categories used for levels of activity, and to the truncation rules [as greater than two hours per day may be required as usable data for walking and other physical activity behaviors]. 2 Characteristics of the IPAQ short-form instrument: 1) IPAQ assesses physical activity undertaken across a comprehensive set of domains including leisure time, domestic and gardening (yard) activities, work-related and transport-related activity; 2) The IPAQ short form asks about three specific types of activity undertaken in the three domains introduced above and sitting. The specific types of activity that are assessed are walking, moderate-intensity activities and vigorous intensity activities; frequency (measured in days per week) and duration (time per day) are collected separately for each specific type of activity. 3) The items were structured to provide separate scores on walking; moderate-intensity; and vigorous-intensity activity as well as a combined total score to describe overall level of activity. Computation of the total score requires summation of the duration (in minutes) and frequency (days) of walking, moderate-intensity and vigorous-intensity activity; 4) Another measure of volume of activity can be computed by weighting each type of activity by its energy requirements defined in METS (METs are multiples of the resting metabolic rate) to yield a score in METñminutes. A MET-minute is computed by multiplying the MET score by the minutes performed. MET-minute scores are equivalent to kilocalories for a 60 kilogram person. Kilocalories may be computed from MET-minutes using the following equation: MET-min x (weight in kilograms/60 kilograms). The selected MET values were derived from work undertaken during the IPAQ Reliability Study undertaken in 2000-2001. Using the Ainsworth et al. Compendium (Med Sci Sports Med 2000) an average MET score was derived for each type of activity. For example; all types of walking were included and an average MET value for walking was created. The same procedure was undertaken for moderate-intensity activities and vigorous-intensity activities. These following values continue to be used for the analysis of IPAQ data: Walking = 3.3 METs, Moderate PA = 4.0 METs and Vigorous PA = 8.0 METs 4 . Analysis of IPAQ Both categorical and continuous indicators of physical activity are possible from the IPAQ short form. However, given the non-normal distribution of energy expenditure in many populations, the continuous indicator is presented as median minutes or median METñminutes rather than mean minutes or mean MET-minutes. Categorical score Regular participation is a key concept included in current public health guidelines for physical activity.5 Therefore, both the total volume and the number of day/sessions are included in the IPAQ analysis algorithms. There are three levels of physical activity suggested for classifying 4 Note that there is still some debate about whether 8 Mets for vigorous is sustainable, in occupational settings for several hours; we have no data on this, but it is likely to be less than that, maybe 7 METs or even less; however, for the moment, we suggest keeping with the compendium value of * METs. 5 Pate RR, Pratt M, Blair SN, Haskell WL , Macera CA, Bouchard C et al. Physical activity and public health. A recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. Journal of Amercian Medical Association 1995; 273(5):402-7. and U.S.Department of Health and Human Services. Physical Activity and Health: A Report of the Surgeon General. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, The Presidents’ Council on Physical Fitness and Sports: Atlanta, GA:USA. 1996. Revised April 2004 3 populations; these are the new proposed levels, which take account of the concept of total physical activity of all domains. The proposed levels are: [i] ëinactive [ii] ëminimally activeí6 [iii] ëHEPA activeí (health enhancing physical activity; a high active category). The criteria for these three levels are shown below. 1. Inactive (CATEGORY 1) This is the lowest level of physical activity. Those individuals who not meet criteria for Categories 2 or 3 are considered ëinsufficiently active’ [CATEGORY 1]. 2. Minimally Active (CATEGORY 2) The minimum pattern of activity to be classified as ësufficiently activeí is any one of the following 3 criteria: a) 3 or more days of vigorous activity of at least 20 minutes per day OR b) 5 or more days of moderate-intensity activity or walking of at least 30 minutes per day OR c) 5 or more days of any combination of walking, moderate-intensity or vigorous intensity activities achieving a minimum of at least 600 MET-min/week. Individuals meeting at least one of the above criteria would be defined as achieving the minimum recommended to be considered ëminimally active’ [CATEGORY 2]. This category is more than the minimum level of activity recommended for adults in current public health recommendations, but is not enough for ìtotal PAî when all domains are considered. IPAQ measures total physical activity whereas the recommendations are based on activity (usually leisure-time or recreational) over and above usual daily activities. 3. HEPA active (CATEGORY 3) A separate category labeled ëHEPA’ level, which is a more active category [CATEGORY 3] can be computed for people who exceed the minimum public health physical activity recommendations, and are accumulating enough activity for a healthy lifestyle. This is a useful indicator because it is known that higher levels of participation can provide greater health benefits, although there is no consensus on the exact amount of activity for maximal benefit. Also, in considering lifestyle physical activity, this is a total volume of being active which reflects a healthy lifestyle. It is at least 1.5 ñ 2 hours of ëbeing activeí throughout the day, which is more than the LTPA-based recommendations of 30 minutes7 . In the absence of any established criteria, the IPAQ scientific group proposes this new cutpoint, which equates to approximately at least 1.5 -2 hours of total activity per day, of at least moderateintensity activity. It is desirable to have a ëHEPAí activity category, because in some populations, a large proportion of the population may be classified as ìminimally activeí because the IPAQ instrument assess all domains of activity. Category 3 sets a higher threshold of activity and provides a useful mechanism to distinguish variation in sub-population groups. 6 “Minimally active” implies some physical activity but is not an optimal level of total HEPA. 7 As Tudor-Locke and others have indicated, there is a basal level of around 1 hour of activity just in activity of daily living, and an additional 0.5 – 1 hour of LTPA makes a healthy lifestyle amount of total PA – hence, these new cutpoints are still consistent with the general LTPA based public health recommendations of at least half an hour per day of additional activity or exercise. 4 The two criteria for classification as ëHEPA activeí are: a) vigorous-intensity activity on at least 3 days achieving a minimum of at least 1500 MET-minutes/week OR b) 7 or more days of any combination of walking, moderate-intensity or vigorous intensity activities achieving a minimum of at least 3000 MET-minutes/week8 Continuous score Data collected with IPAQ can be reported as a continuous measure and reported as median METminutes. Median values can be computed for walking (W), moderate-intensity activities (M), and vigorous-intensity activities (V) using the following formulas: MET values and Formula for computation of Met-minutes Walking MET-minutes/week = 3.3 * walking minutes * walking ëdaysí Moderate MET-minutes/week = 4.0 * moderate-intensity activity minutes * moderate days Vigorous MET-minutes/week = 8.0 * vigorous-intensity activity minutes * vigorous-intensity days A combined total physical activity MET-min/week can be computed as the sum of Walking + Moderate + Vigorous MET-min/week scores. The MET values used in the above formula were derived from the IPAQ validity and reliability study undertaken in 2000-2001 9 . A brief summary of the method is provided above (see page 1). As there are no established thresholds for presenting MET-minutes, the IPAQ Research Committee proposes that these data are reported as comparisons of median values and interquartile ranges for different populations. IPAQ Sitting Question The IPAQ sitting question is an additional indicator variable and is not included as part of any summary score of physical activity. Data on sitting should be reported as median values and interquartile range. To-date there are few data on sedentary (sitting) behaviors and no wellaccepted thresholds for data presented as categorical levels. Data Processing Rules In addition to a standardized approach to computing categorical and continuous measures of physical activity, it is necessary to undertake standard methods for the cleaning and treatment of IPAQ datasets. The use of different approaches and rules would introduce variability and reduce the comparability of data. There are no established rules for data cleaning and processing on physical activity. Thus, to allow more accurate comparisons across studies IPAQ has established and recommends the following guidelines: 1. Data cleaning • time should be converted from hours and minutes into minutes 8 Note: this replaces the previous IPAQ short form cutpoint of 1500 mets.mins/ week 9 Craig CL,Marshall A , Sjostrom M et al. International Physical Activity Questionnaire: 12 country reliability and validity Med Sci Sports Exerc 2003;August. Revised April 2004 5 • ensure that responses in ëminutesí were not entered in the ëhoursí column by mistake during self-completion or during data entry process, values of ë15í, ë30í, ë45í, ë60í and ë90í in the ëhoursí column should be converted to ë15í, ë30í, ë45í, ë60í and ë90í minutes, respectively, in the minutes column. • time should be converted to daily time [usually is reported as daily time, but a few cases will be reported as optional weekly time ñ eg. VWHRS, VWMINS ñ convert to daily time] • convert time to mets-mins [see above; days x daily time] • must have the number of days for the day variables; for the ëtimeí variables, either daily or weekly time is needed ñ if ëdonít knowí or ërefused ë or data are missing in walking, moderate or vigorous days or minutes, then that case is removed from analysis 2. Maximum Values for excluding outliers This rule is to exclude data which are unreasonably high; these data are to be considered outliers and thus are excluded from analysis. All Walking, Moderate and Vigorous time variables which total at least or greater than ë16 hoursí should be excluded from the analysis. The ëdaysí variables can take the range 0-7 days, or 8,9 (donít know or refused); values greater than 9 should not be allowed and those data excluded from analysis. 3. Truncation of data rules This rule is concerned with data truncation and attempts to normalize the distribution of levels of activity which are usually skewed in national or large population data sets. It is recommended that all Walking, Moderate and Vigorous time variables exceeding ë 4 hoursí or ë240 minutesí are truncated (that is re-coded) to be equal to ë240 minutesí in a new variable10. This rule permits a maximum of 28 hours of activity in a week to be reported for each category of physical activity. This rule requires further testing, but is an initial manner proposed for classifying these population data. When analysing IPAQ data and presenting the results in categorical variables, this rule has the important effect of preventing misclassification in the ëhigh activeí category. For example, an individual who reports walking for 2.5 hours every day and nothing else would be classified as ëHEPA activeí (reaching the threshold of 7 days, and ≥ 3000 MET.mins. Similarly, someone who reported walking for 90 minutes on 5 days, and 4 hours (240 mins) of moderate activity on another day and 70 minutes of vigorous activity on another day, would also be coded as ëHEPA activeí because this pattern meets the ë7 dayî and ì3000 MET-minî criteria for ëHEPA activeí. 4. Minimum Values for Duration of Activity Only values of 10 or more minutes of activity will be included in the calculation of summary scores. The rationale being that the scientific evidence indicates that episodes or bouts of at least 10 minutes are required to achieve health benefits. Responses of less than 10 minutes [and their associated days] should be re-coded to ëzeroí. Summary of Data Processing Rules 1- 4 above Data management rules 2, 3, and 4 deal with first excluding outlier data, then secondly, recoding high values to ë4 hoursí, and finally describing minimum amounts of activity to be included in 10 Note that this is a different truncation rule to the earlier scoring protocol; we have previously used 2 hours as a truncation point for LTPA measures. This higher truncation point is proposed in order to allow people who walk for 2.5 hours per day and do nothing else to be categorized as ‘HEPA’ active; if data were truncated, these individuals would be recoded to 2 hours per day, and over 7 days, total 2772 MET.mins, due to the truncation rule. The new truncation rule allows 2.5 hours to be counted in full. The initial purpose of truncation was to normalize the distributions, and was based on expert judgments. It is now suggested that 4 hours / day be proposed as a truncation threshold for more inclusive ‘lifestyle PA measures’ such as IPAQ. 6 analyses. These rules will ensure that highly active people remain highly active, while decreasing the chances that less active individuals are coded as highly active. 5. Calculating Total Days for ‘minimally Active’ [category 2] and ‘HEPA Active’ [category 3] Presenting IPAQ data using categorical variables requires the total number of ëdaysí on which all physical activity was undertaken to be assessed. This is difficult because frequency in ëdaysí is asked separately for walking, moderate-intensity and vigorous-intensity activity, thus allowing the total number of ëdaysí to range from a minimum of 0 to a maximum of 21ídaysí per week. The IPAQ instrument does not record if different types of activity are undertaken on the same day. In calculating ‘minimal activity’, the primary requirement is to identify those individuals who undertake a combination of walking and/or moderate-intensity activity on at least ë5 daysí/week. Individuals who meet this criterion should be coded in a new variable called ìat least five days”. Below are two examples showing this coding in practice: i) an individual who reports ë2 days of moderateí and ë3 days of walkingí should be coded as a value indicating ìat least five days”; ii) an individual reporting ë2 days of vigorousí, ë2 days walkingí and ë2 days moderateí should be coded as a value to indicate ìat least five days” [even though the actual total is 6]. The original frequency of ëdaysí for each type of activity should remain in the data file for use in the other calculations. The same approach as described above is used to calculate total days for computing the ëHEPA active’ category. The primary requirement according to the stated criteria is to identify those individuals who undertake a combination of walking, moderate-intensity and or vigorous activity on at least 7 days/week. Individuals who meet this criterion should be coded in a value in a new variable to reflect “at least 7 daysî. Below are two examples showing this coding in practice: i) an individual who reports ë4 days of moderateí and ë3 days of walkingí should be coded as the new variable “at least 7 daysî. ii) an individual reporting ë3 days of vigorousí, ë3 days walkingí and ë3 days moderateí should be coded as “at least 7 daysî [even though the total adds to 9] . Summary: The algorithm(s) in Appendix 1 and Appendix 2 to this document show how these rules work in an analysis plan, to develop the categories 1 [inactive], 2 [minimally], and 3 [HEPA] levels of activity. A short form [ëat a glanceí] and a diagram showing these analytic steps for ësufficient physical activityí and ëhigh activeí categories are shown as appendix 1 at the end of this document. IPAQ Research Committee April 2004 Revised April 2004 7 APPENDIX 1 At A Glance IPAQ Scoring Protocol (Short Versions) Categorical Score- three levels of physical activity are proposed 1. Inactive • No activity is reported OR • Some activity is reported but not enough to meet Categories 2 or 3. 2. Minimally Active Any one of the following 3 criteria • 3 or more days of vigorous activity of at least 20 minutes per day OR • 5 or more days of moderate-intensity activity or walking of at least 30 minutes per day OR • 5 or more days of any combination of walking, moderate-intensity or vigorous intensity activities achieving a minimum of at least 600 MET-min/week. 3. HEPA active Any one of the following 2 criteria • Vigorous-intensity activity on at least 3 days and accumulating at least 1500 METminutes/week OR • 7 or more days of any combination of walking, moderate-intensity or vigorous intensity activities achieving a minimum of at least 3000 MET-minutes/week Continuous Score Expressed as MET-min per week: MET level x minutes of activity x events per week Sample Calculation MET levels MET-min/week for 30 min episodes, 5 times/week Walking = 3.3 METs 3.3*30*5 = 495 MET-min/week Moderate Intensity = 4.0 METs 4.0*30*5 = 600 MET-min/week Vigorous Intensity = 8.0 METs 8.0*30*5 = 1,200 MET-min/week ___________________________ TOTAL = 2,295 MET-min/week Total MET-min/week = (Walk METs*min*days) + (Mod METs*min*days) + Vig METs*min*days) Please review the document “Guidelines for the data processing and analysis of the International Physical Activity Questionnaire (Short Form)” for more detailed description of IPAQ analysis and recommendations for data cleaning and processing [www.ipaq.ki.se]. Revised April 2004 1 START HERE Vigorous days [VDAY] ≥3 YES NO Vigorous time ≥20mins YES Minimally active CATEGORY 2 Total days of moderate [mDAY] PLUS walking [wDAY] ≥ 5 YES NO Moderate time PLUS Walk time totals ≥ 30mins per day YES NO Inactive CATEGORY 1 NO Days of Walking [WDAY] PLUS Moderateintensity [mDAY] PLUS Vigorous [VDAY] ≥ 5 AND Sum of METmins ≥ 600 NO YES Vigorous days [VDAY] ≥3 AND Vig METmins ≥ 1500 YES HEPA ACTIVE CATEGORY 3 NO Days of Walking [WDAY] PLUS Moderate-intensity [mDAY] PLUS Vigorous [VDAY] ≥ 7 AND Sum of METmins ≥ 3000 YES NO APPENDIX 2: Flow chart algorithm for the analysis of IPAQ short form

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