Indicate the strength and direction of the relationship between body mass index and physical health component subscale?

A study to compare between DAX and CT scan to diagnose osteoporosis in elderly women ACKNOWLEDGMENTS. 2 ABSTRACT.. 3 LIST OF ABBREVIATIONS. 7 CHAPTER ONE: INTRODUCTION.. 8 1.0 Overview.. 8 1.1 Statement of the Problem.. 9 1.2 Research Question. 9 1.3 Aims and Objectives. 10 1.4 Importance of this Review.. 10 1.5 Summary. 10 CHAPTER TWO: LITERATURE REVIEW 12 2.0 Overview.. 12 2.1 Ultrasonography: A Major Advance in Healthcare Practice. 12 2.2 The Prevalence of WRMSDs in Sonographers. 13 2.2.1 Signs and Symptoms of WRMSDs in Sonographers. 14 2.2.2 Factors Predisposing Sonographers to WRMSD.. 15 2.2.3 Preventing the Onset and Reducing the Severity of WRMSDs in Sonographers. 18 2.2.3.1 Risk Assessments. 18 2.2.3.2 Ultrasound Equipment 18 2.2.3.3 Education. 18 2.2.3.4 The Work Environment 19 2.3 Summary. 19 CHAPTER THREE: METHODOLOGY.. 20 3.0 Overview.. 20 3.1 Criteria for Formulating the Research Question. 20 3.2 Study Design. 22 3.3 The Search Strategy and Study Selection. 25 3.4 Inclusion and Exclusion Criteria. 26 3.5 Final Study Selection. 27 3.6 Data Extraction. 27 3.7 Quality of the Chosen Studies. 28 3.8 Summary. 29 CHAPTER FOUR: RESULTS. 30 4.0 Overview.. 30 4.1 Study Selection. 30 4.2 Quality Assessment of the Chosen Studies. 33 4.3 Characteristics of the Selected Studies. 33 4.4 Prevalence and Development of WRMSDs in Sonographers. 37 4.4.1 Techniques and Protocols. 38 4.4.2 Sample Size and Correlations. 38 4.5 Factors Influencing WRMSDs in Sonographers. 40 4.6 Summary. 43 CHAPTER FIVE: DISCUSSION.. 44 5.0 Overview.. 44 5.1 Summary of the Evidence. 44 5.2 Limitations. 46 5.3 Findings within the Context of the Current Evidence. 47 5.4 Research Recommendations. 48 CHAPTER SIX: CONCLUSION.. 50 References. 51 Appendices. 57 List of Tables 22 Defined Elements of the PIO Framework Used for this Research Study. 3.1 32 Examples of Reasons for Exclusion of Studies 4.1 35 Characteristics of the Selected Studies 4.2 List of Figures 31 The Study Selection Process using the PRISMA Statement. 4.1 40 Total Sample Size and Distribution Across and Within All Six Papers 4.2 List of Appendices Critical Appraisal Skills Programme (CASP).. 57 muscle pain, which is an indicator of various diseases and disorders. 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September 28, 2020
Research on personality disorders in DSM-IV-TR.
September 28, 2020

Indicate the strength and direction of the relationship between body mass index and physical health component subscale?

Week Six – Correlations Exercises

Correlations are used to describe the strength and direction of a relationship between two variables. A correlation between two variables is known as a bivariate correlation. In this module the Pearson Product-Moment Correlation will be used when running a correlation matrix. The Pearson correlation coefficient ranges from a value of -1.0 to 1.0. A correlation coefficient is never above 1.0 or below -1.0. A perfect positive correlation is 1.0 and a perfect negative correlation is -1.0. The size of the coefficient determines the strength of the relationship and the sign (i.e., + or -) determines the direction of the relationship. The closer the value is to zero the weaker the relationship and the closer the value is to 1.0 or -1.0 the stronger the relationship. A correlation coefficient of zero indicates no relationship between the variables.

A scatterplot is used to depict the relationship between two variables. The general shape of the collection of points indicates whether the correlation is positive or negative. A positive relationship will have the data points group into a cluster from the lower left hand corner to the upper right hand corner of the graph. A negative relationship will be depicted by points clustering in the lower right hand corner to the upper left hand corner of the graph. When the two variables are not related the points on the scatterplot will be scattered in a random fashion.

Using Polit2SetB dataset, create a correlation matrix using the following variables: Number of visits to the doctor in the past 12 months (docvisit), body mass index (bmi), Physical Health component subscale (sf12phys) and  Mental Health component subscale (sf12ment). Run means and descriptives for each variable as well as the correlation matrix.

 

Follow these steps using SPSS:

 

  1. Click Analyze, then correlate, then bivariate.
  2. Select each variable and move them into the box labeled “Variables.”
  3. Be sure the Pearson and two-tailed box is checked.
  4. Click on the options tab (upper right corner) and check “means and standard deviations.” The exclude cases pairwise should also be checked. Click continue.
  5. Click OK

 

To run descriptives for docvisit, bmi, sf12phys and sf12ment do the following in SPSS:

 

  1. Click Analyze then click Descriptives Statistics, then Descriptives.
  2. Click the first continuous variable you wish to obtain descriptives for (docvisit) and then click on the arrow button and move it into the Variables box. Then click bmi and then click on the arrow button and move it into the Variables box. Then click sf12phys and then click on the arrow button and move it into the Variables box. Then click sf12ment and then click on the arrow button and move it into the Variables box.
  3. Click the Options button in the upper right corner. Click mean and standard deviation.
  4. Click continue and then click OK.

 

 

Assignment: Answer the following questions about the correlation matrix.

 

  1. What is the strongest correlation in the matrix? (Provide correlation value and names of variables)
  2. What is the weakest correlation in the matrix? (Provide correlation value and names of variables)
  3. How many original correlations are present on the matrix?
  4. What does the entry of 1.00 indicate on the diagonal of the matrix?
  5. Indicate the strength and direction of the relationship between body mass index and physical health component subscale?
  6. Which variable is most strongly correlated with body mass index? What is the correlational coefficient? What is the sample size for this relationship?
  7. What is the mean and standard deviation for bmi and doctor visits?

 

Part II

 

Using Polit2SetB dataset, create a scatterplot using the following variables: x-axis = body mass index (bmi) and the y-axis = weight-pounds (weight).

 

Follow these steps in SPSS:

 

  1. Click Graphs, then click on Legacy Dialogs, then click “Scatter/Dot”.
  2. Click “Simple Scatter” and then click “Define.”
  3. Click on weight-pounds and move it to the Y-axis box and then click on body mass index and move it to the x-axis box.
  4. Click OK.

 

To run descriptives for bmi and weight do the following in SPSS:

 

  1. Click Analyze then click Descriptives Statistics, then Descriptives.
  2. Click the first continuous variable you wish to obtain descriptives for (body mass index) and then click on the arrow button and move it into the Variables box. Then click weight-pounds and then click on the arrow button and move it into the Variables box.
  3. Click the Options button in the upper right corner. Click mean and standard deviation.
  4. Click continue and then click OK.

Assignment:

  1. What is the mean and standard deviation for weight and bmi?
  2. Describe the strength and direction of the relationship between weight and bmi?
  3. Describe the scatterplot? What information does it provide to a researcher?

 

 

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