Class,

This information will be helpful throughout the class. These are issues I know are common mistakes/problems that are often a source of confusion.

The items listed below will not be in all case studies, however, a good portion of them will be in most. Be aware that I will write comments on your paper, and point out mistakes while grading, as some students have overlooked feedback posted on their paper.

- Please write all responses about a data set, statistical analysis, or findings from an analysis in past tense.

- For skewness and kurtosis, +/-1.96 is a standard value used for these two calculations

- The alpha level for this course is 0.05.

- For a result to be statistically significant the 'p-value' must be 0.05 or below. This is the SIG value you see in SPSS.

- If a result is '.000' then it should be written as 'p<0.001.'

- For the Assumption of Normality to be met, then the 'Sig.' value under the 'Shapiro-Wilk' column must be greater than 0.05.

- For Levene's Test to be met, the 'Sig' value must be greater than 0.05.

- Means and standard deviations should be written as 'means+ SD (e.g. 150+ 5.5).

- The write-up must be written in past tense and in paragraph format. This means taking all the questions in the write-up and forming a single written paragraph for your responses. Ideally, I want to see a scientific abstract, where you use the questions as a guide. Don't just "answer the questions."

- When the write-up question asks if the findings are practical,  this is not actually asking your "opinion." You should use your knowledge, and reference any information that is not well-known in our field. You can use textbook or scientific references. For example, you can tell me "the results of this study align/support (or do not support) current research or accepted knowledge." Be sure to tell me "why." Explain what concept the study supports/doesn't support. Explain why or why not the study makes sense, how you know, etc.

-The results/conclusions/application of the Case study must refer to our field's understanding of physiology and human performance. Sometimes the findings won't be in agreement with our understanding of physiology and therefore, the student will need to provide a rationale for why or why not. Do not assume that these studies are fact. Think critically and determine "does this make sense, how do I know, etc." Use the stats, the data, and ex phys knowledge to explain the results.

I will give this example, because several case studies are related to thermoregulation:

Do not say "I think this makes sense because I feel like I work harder in a hot environment and my  body temperature must be higher." Do not say, "hot yoga is harder than regular yoga classes," or anything like that. Instead, examine data, outliers, results.

If you see a body temperature of 50 degrees (you won't, but for example) you should immediately know that is impossible and the data "doesn't make sense." If you see a heart rate of 300 bpm, that doesn't make sense. This is a step you should always take, because mistakes happen.

Next, consider what the results are indicating. If the results indicate that heart rate goes down as intensity increases, you would immediately know that "doesn't make sense." Explain why. Try to figure out why the stats didn't match what you know. Is it because of a research design flaw, is it because an assumption was violated, etc.  Another example- if results indicate that core body temperature increases significantly during one exercise environment vs another, (or if it results say there's no statistical difference in core temp in the heat) you should immediately consider how highly regulated core temp is. What factors are not being considered? What was the case study considering a "hot" environment vs cold? Why would core temp stay relatively the same from one environmental condition to another? Thermoregulation. The body may have to decrease the intensity of exercise, the duration, etc., but body temp will only get "so high" before fatigue sets in, or before heat stroke occurs.  The conclusion/application is not your "opinion." Explain the statistical and scientific application, not your life experience. I want you to begin to "think" like a researcher.

A list of common mistakes I see:

When you see "Sig" in SPSS, that is your p-value. This is what you compare to the alpha level (always .05 for the course) in order to determine the significance of a particular test.

Again- WRITE IN PAST TENSE, as if an abstract. DO NOT ONLY ANSWER THE QUESTIONS...the questions are to GUIDE your write-up. There is no 8 line maximum for case studies, but do not feel you must write more if you can clearly and include all that is required. Most students need more than 8 lines to elaborate on "what the study means."

Cite your work, if not common knowledge.

Pretend that you are writing an abstract for a study. Pretend the case study is your study and your data if that helps. Read a journal article in our field and look at those abstracts. You need to know how to write an abstract for other courses for your degree, and this class helps you learn how to write the "methods" and "results" section of your abstract.

Please include your p-value (sig) after your statements about significance. Review an abstract and you will see that this is the norm.

TELL me what the data means based on knowledge of physiology from your classes, your textbooks, journal articles. Cite if needed. Do not tell me what you think. Tell me what you know.

In summary...I KNOW THIS IS REPETITIVE but it will help.

• Make sure you elaborate on your knowledge of physiology and how it relates to the data/case study. Even if you "mess up" the stats, if I can see that you understand the study and the concepts, I will be lenient in grading, or may ask you to resubmit the case study because you made a mistake...because I can see that you understand.

• Make sure you write scientifically and in the past tense. Do not just answer the questions for the write-up. Those are a guide...

• Explain the data and the results based on scientific evidence, and not your opinion. Point out items in the data/results that can be used to support your position.

• Write/report your p-values in the write-up to reference significance. Write them as p=.005 (for example). Write your significance level as p<.05 (for example).

• Read a scientific paper in our field if you need help with your write-up.

• *You will need to know how to read, write, and interpret data, not just for this class, but to complete your degree.

Class,

This information will be helpful throughout the class. These are issues I know are common mistakes/problems that are often a source of confusion.

The items listed below will not be in all case studies, however, a good portion of them will be in most. Be aware that I will write comments on your paper, and point out mistakes while grading, as some students have overlooked feedback posted on their paper.

- Please write all responses about a data set, statistical analysis, or findings from an analysis in past tense.

- For skewness and kurtosis, +/-1.96 is a standard value used for these two calculations

- The alpha level for this course is 0.05.

- For a result to be statistically significant the 'p-value' must be 0.05 or below. This is the SIG value you see in SPSS.

- If a result is '.000' then it should be written as 'p<0.001.'

- For the Assumption of Normality to be met, then the 'Sig.' value under the 'Shapiro-Wilk' column must be greater than 0.05.

- For Levene's Test to be met, the 'Sig' value must be greater than 0.05.

- Means and standard deviations should be written as 'means+ SD (e.g. 150+ 5.5).

- The write-up must be written in past tense and in paragraph format. This means taking all the questions in the write-up and forming a single written paragraph for your responses. Ideally, I want to see a scientific abstract, where you use the questions as a guide. Don't just "answer the questions."

- When the write-up question asks if the findings are practical,  this is not actually asking your "opinion." You should use your knowledge, and reference any information that is not well-known in our field. You can use textbook or scientific references. For example, you can tell me "the results of this study align/support (or do not support) current research or accepted knowledge." Be sure to tell me "why." Explain what concept the study supports/doesn't support. Explain why or why not the study makes sense, how you know, etc.

-The results/conclusions/application of the Case study must refer to our field's understanding of physiology and human performance. Sometimes the findings won't be in agreement with our understanding of physiology and therefore, the student will need to provide a rationale for why or why not. Do not assume that these studies are fact. Think critically and determine "does this make sense, how do I know, etc." Use the stats, the data, and ex phys knowledge to explain the results.

I will give this example, because several case studies are related to thermoregulation:

Do not say "I think this makes sense because I feel like I work harder in a hot environment and my  body temperature must be higher." Do not say, "hot yoga is harder than regular yoga classes," or anything like that. Instead, examine data, outliers, results.

If you see a body temperature of 50 degrees (you won't, but for example) you should immediately know that is impossible and the data "doesn't make sense." If you see a heart rate of 300 bpm, that doesn't make sense. This is a step you should always take, because mistakes happen.

Next, consider what the results are indicating. If the results indicate that heart rate goes down as intensity increases, you would immediately know that "doesn't make sense." Explain why. Try to figure out why the stats didn't match what you know. Is it because of a research design flaw, is it because an assumption was violated, etc.  Another example- if results indicate that core body temperature increases significantly during one exercise environment vs another, (or if it results say there's no statistical difference in core temp in the heat) you should immediately consider how highly regulated core temp is. What factors are not being considered? What was the case study considering a "hot" environment vs cold? Why would core temp stay relatively the same from one environmental condition to another? Thermoregulation. The body may have to decrease the intensity of exercise, the duration, etc., but body temp will only get "so high" before fatigue sets in, or before heat stroke occurs.  The conclusion/application is not your "opinion." Explain the statistical and scientific application, not your life experience. I want you to begin to "think" like a researcher.

A list of common mistakes I see:

When you see "Sig" in SPSS, that is your p-value. This is what you compare to the alpha level (always .05 for the course) in order to determine the significance of a particular test.

Again- WRITE IN PAST TENSE, as if an abstract. DO NOT ONLY ANSWER THE QUESTIONS...the questions are to GUIDE your write-up. There is no 8 line maximum for case studies, but do not feel you must write more if you can clearly and include all that is required. Most students need more than 8 lines to elaborate on "what the study means."

Cite your work, if not common knowledge.

Pretend that you are writing an abstract for a study. Pretend the case study is your study and your data if that helps. Read a journal article in our field and look at those abstracts. You need to know how to write an abstract for other courses for your degree, and this class helps you learn how to write the "methods" and "results" section of your abstract.

Please include your p-value (sig) after your statements about significance. Review an abstract and you will see that this is the norm.

TELL me what the data means based on knowledge of physiology from your classes, your textbooks, journal articles. Cite if needed. Do not tell me what you think. Tell me what you know.

In summary...I KNOW THIS IS REPETITIVE but it will help.

• Make sure you elaborate on your knowledge of physiology and how it relates to the data/case study. Even if you "mess up" the stats, if I can see that you understand the study and the concepts, I will be lenient in grading, or may ask you to resubmit the case study because you made a mistake...because I can see that you understand.

• Make sure you write scientifically and in the past tense. Do not just answer the questions for the write-up. Those are a guide...

• Explain the data and the results based on scientific evidence, and not your opinion. Point out items in the data/results that can be used to support your position.

• Write/report your p-values in the write-up to reference significance. Write them as p=.005 (for example). Write your significance level as p<.05 (for example).

• Read a scientific paper in our field if you need help with your write-up.

• *You will need to know how to read, write, and interpret data, not just for this class, but to complete your degree.