Our HS2135 Statistics project blog

Hello and welcome!

We hope that you will enjoy viewing our humble HS2135 STATISTICS project bloggy. =)

Here are our group members:

From left to right: Yanan, Gelinda, Robin & Shakinah

[ Kindly note ]

Just so our dear readers won't hard a hard time scrolling up from the bottom to follow our progress, we have edited our blog entry dates so that you can scroll down to read our entries in natural sequence - from top to bottom!

(Blogger seems to be unable to place the earliest post at the top.)

How we went about it

Body temperature is a measure of the body's ability to generate and get rid of heat. It is one of the 5 parameters taken when we measure a person's vital signs (the other 4 being Blood Pressure, Pulse Rate, Respiration Rate & Pain).

There are various ways to measure body temperature, via:

• Oral: in the mouth, under the tongue
• Axilla: under the armpit
• Rectal: in the anus
• Tympanic: from the eardrum in the ear canal (infrared)
• Skin: on the forehead
  

Our Research Question:
Is a person's oral temperature related to his/her axilla temperature?

Defining the Variables:

• Variable 1: Oral temperature.
• Variable 2: Axillary temperature.
• Extraneous variables: Gender.
  

Conceptualization:

• Oral temperature: temperature taken under the tongue of the subject.
• Axilla temperature: temperature taken under the axilla/armpit of the subject.

Literature Review:

• Body Temperature: Normal Body Temperature, Fever, and Thermometer Types
  

• Rectal, ear, oral, and axillary temperature comparison

• Normal oral, rectal, tympanic and axillary body temperature in adult men and women: a systematic literature review.

• Temperature of a Healthy Human (Body Temperature)

• How reliable is axillary temperature measurement?

• Is axillary temperature an appropriate surrogate for core temperature?

• A comparison between infrared tympanic thermometry, oral and axilla with rectal thermometry in neutropenic adults

• Comparison of Axillary Temperature with Rectal or Oral Temperature
  and Determination of Optimum Placement Time in Children
  

Operationalization:

1. We gathered our samples by measuring the oral temperature & axilla temperature of our schoolmates using an electronic digital thermometer.

2. Calibration - The temperatures are measured using the SAME BRAND of electronic thermometers. They have been pre-checked to ensure they register readings that are are within ±0.1°C when measuring from the SAME cup of warm water at the SAME time.

3. Thermosheaths were also used for both oral and axilla temperature taking to maintain consistency.

4. We took measurements of both temperature routes for each individual TWICE to obtain an AVERAGE.

5. The data was collected from a group of healthy males & females from 17 to 24 years old.

What Can Affect the Test:

Inaccurate temperature readings can be caused by:

• Not keeping your mouth closed around the thermometer when taking an oral temperature.
• Not leaving a thermometer in place long enough before reading it.
• Not putting the proper thermometer in the right place.
• Not following the instructions for proper use that come with the thermometer.
• A weak or dead thermometer battery.
• Taking an oral temperature within 20 minutes after smoking or drinking a hot or cold liquid.
• Taking a temperature by any method within an hour of exercising vigorously or taking a hot bath.

Precautions Taken During Operationalization:

1. Fresh batteries were inserted in all test electronic thermometers.
2. We reminded all subjects to place the thermometer under their tongues and keep their mouth closed around the thermometer.
3. We only remove the thermometer from under the tongue or armpit when it beeped to indicate that it was in place long enough to register a stable temperature.
   
4. The temperatures were taken at the end of the first morning lectures at Nanyang Polytechnic so that:
   • There is at least an hour's gap from any consumed hot food or drink (to minimize the influence of hot food on body temperature), and
   • The subjects would be at rest (to minimize the impact of any physical exertion on body temperature).

Collecting our data from our classmates and friends

The approach & rationale for our statistical analysis

Data Collection

Overall, the data collected was good and there was no rejection.

Hypothesis Testing

1. Null Hypothesis: There is NO significant relationship between a person's oral temperature and axilla temperature.
2. Alternative Hypothesis: There IS a significant relationship between a person's oral temperature and axilla temperature.
   

Criteria to "Reject" or "Accept" the Null Hypothesis:

1. The decision rests on the p-value test statistic in relation to the user-defined α (alpha).

2. Alpha refers to the significance level. At this critical region, the range of values of the test statistic indicates that there IS a significant relationship and that the null hypothesis is rejected.

3. If the test statistic falls in the critical region (ie. p-value is ≤ α), it would lead to the rejection of the Null Hypothesis. In other words:

• If p is ≤ α, we would REJECT the Null Hypothesis.
• If p is > α, we cannot reject the null hypothesis and we've to ACCEPT the Alternative Hypothesis.

In our study, we have set α at 0.05.

Choosing an appropriate statistical test

The statistical test depends whether the research question is about:

• Difference, or
• Correlation.
  

For our study, it is about correlation.

source: Chia, C. Y. (2008). Statistics in health sciences. (4th ed.). Singapore: McGraw Hill Education

Since both of our variables in question are scale variables, and guided by the above decision path, we use Pearson's r to do the testing.

What is Pearson's r?

1. Pearson's r let us know the strength and direction of the linear association between two scale variables.

2. This correlation coefficient indicates the strength of the correlation.

3. Its limit ranges from -1 to +1. The + or - values indicates the direction of the correlation and lets us know how the variables are related.

4. Values near -1 indicates a strong negative relationship and is visually represented by a downward slope of the samples in a graph, while values near +1 indicates a strong positive relationship and an upward slope of the graph.

5. The closer the correlation coefficient approaches zero, the weaker the relationship between the two variables.

Calculations using SPSS and interpreting the results

The table we created via SPSS for our data entry (Variable View):

The data we entered using SPSS (Data View):

From our data above, we generated a Scatter Plot for oral temperature vs axilla temperature for [Females] via SPSS:

Below is the Scatter Plot for oral temperature vs axilla temperature for [Males] via SPSS:

We also generated Pearson's R coefficients for [Females] & [Males] using SPSS in the following table:

Our table above show Pearson's correlation coefficients of 0.964 for [Females] & 0.960 for [Males].

INTERPRETING PEARSON'S r

1. The association for [Males] is r=0.960, p=0.000, N=9 and the [Female]'s association is r=0.964, p=0.000, N=21.
   
2. Since they all exceed 0.8, they indicate that there are VERY STRONG, SIGNIFICANT and POSITIVE associations between a person's oral & axilla temperatures for both [Males] and [Females] respectively.

From our data, we ALSO generated a Scatter Plot for oral temperature vs axilla temperature for BOTH [Male + Female] via SPSS:

The COMBINED table above shows the Pearson's correlation coefficient of 0.956 for [Males + Females] (ie. ALL samples). It indicates a VERY STRONG, SIGNIFICANT and POSITIVE RELATIONSHIP between oral temperature and axilla temperature for [Males + Females].

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COMPUTING THE REGRESSION LINES FOR [Female], [Male] and [Male + Female] (ie. y = mx + c)

Since a relationship exists between oral and axilla temperatures, we now perform linear regressions using SPSS to construct equations that can predict a person's oral temperature from his/her axilla temperature.

The data table below is for [Female] samples.

The linear equation for [Females] is

Oral Temp Female = 1.073 x (Axilla Temp Female) - 2.239 °C

The data table below is for [Male] samples.

The linear equation for [Males] is

Oral Temp Male = 1.116 x (Axilla Temp Male) - 3.631 °C

The data table below is for [Male + Female] samples.

The linear equation for [Male + Female] is

Oral Temp = 1.084 x (Axilla Temp) - 2.567 °C

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HYPOTHESIS TESTING


We had earlier selected the critical value or common significance level or α at 0.05.

Since the p values (red arrows) for [Males], [Females] and [Males + Females] are ALL = 0.000 which is less that our alpha of 0.05, WE REJECT OUR NULL HYPOTHESIS.

Thus, we ACCEPT our ALTERNATIVE HYPOTHESIS that THERE IS A SIGNIFICANT RELATIONSHIP BETWEEN A PERSON'S ORAL AND AXILLA TEMPERATURE (specifically a positive one).

Discussion

The average normal human oral temperature is 36.9°C.

In general, for the same individual:

• Axilla (armpit) temperature is usually (0.3°C) to (0.6°C) LOWER than an oral temperature.
• Oral temperature is about (0.3°C) to (0.6°C) LOWER than a rectal or ear (tympanic) temperature.
• Conversely, Tympanic (ear) and Rectal temperatures are (0.3°C) to (0.6°C) HIGHER than oral temperature.
  

The following table shows the "normal ranges" for human temperature taken using different routes as reported by Sund-Levander M, Forsberg C, Wahren LK.:

While debate remains as to whether "core body temperature" taken using the rectal route best reflect the body's "true" internal temperature, factors like:

• embarrassment to the patient,
• inconvenience to the patient and the nurse,
• ease of use for the nurse,
• expediency and
• speed & accuracy in obtaining temperature readings, etc
  

all mean that tympanic thermometers are favoured in adult wards as the temperature can be obtained in ONE second instead of waiting for a minute or even longer for an electronic oral thermometer.

However, axilla termperature is taken using electronic thermometers for children under 6 months in pediatric wards since their ear canals are still not large enough to accomodate a regular tympanic thermometer.

Our reflection

Gelinda:
Overall, I find that doing this blog is interesting, like the way we will be graded with a blog. I've learned quite a few things, such as collecting data as samples, keying data into SPSS and using SPSS to analyze the data. I believe this module will prepare me well for my final year project. Therefore this is really a good experience for me to deal with this project.

Robin:
Among the things I learned from this statistics module are:

• The different types of data and how to collect, collate & handle them.
• How to use different techniques appropriately to interpret and make sense of the data collected.
• How to use SPSS, a software program, to help me analyze the data.

With evidence-based nursing becoming increasingly important, the knowledge I learned from this module has helped to build a foundation for me to collect and analyze data from meaningful research studies commissioned by my employers in the future to illuminate new and emerging nursing trends.

Yanan:
Through this statistics project, I have learned how to collect date correctly, and analyze & interpret the data holistically. I also learned teamwork while learning from each other. Statistics is related to all aspects of our lives, it is important to master this subject to develop ourselves!

Shakinah:
Our statistics project is really interesting. This project enabled me to analyze the data we collected. It is a new experience as I have not done this before. Getting samples from our friends was fun. At the same time I think that this project will help be useful for me in my final year project.

ACKNOWLEDGEMENT:

Our project group would like to ♥♥♥THANK♥♥♥ our Lecturer, Ms Chia Choon Yee for her invaluable feedback and helpful suggestions during our consultation with her, as well as her interesting lectures and excellent textbook which she authored. Chia, C. Y. (2008). Statistics in health sciences. (4th ed.). Singapore: McGraw Hill Education (ISBN 978-007-126949-0. Comes with CD-ROM). Statistics is a new subject for many of us. Without her patient explanation & guidance backed by clear examples, both during her lectures and in her well-written & generously illustrated textbook, we would not have been able to complete our Statistics blog project.