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Created: Sep 20, 2019
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Depression scores in Older Adults
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Introduction

Depression is a mood disorder characterized by persistently low mood and a feeling of sadness and loss of interest. It is a persistent problem, not a passing one.  There are more than 3 million cases of Depression per year.  Depression is an extremely complex disease. No one knows exactly what causes it, but it can occur for a variety of reasons. Some people experience depression during a serious medical illness. Others may have depression with life changes such as a move or the death of a loved one. Still others have a family history of depressionA study was done on healthy individuals 65 years of age or older to examine the relationship between location, health status/comorbidities, and depression.

Methods

Sociologists and physicians at the Wentworth Medical Center in upstate New York investigated and gathered information on 20 healthy individuals in three different geographical locations: Florida, New York and North Carolina. Individuals were given a standardized test to measure depression on this test, higher scores indicate higher levels of depression. Similarly, samples of 20 individuals with one or more comorbidities (arthritis, hypertension, and/or heart ailment) were taken from the three geographic locations.  The Health Status individuals were also given the standardized test to measure depression.

Analysis

A QQ-Plot for residuals was used to check normality.  Almost all points on the QQ plot are on or close to the diagonal line which is interpreted as no evidence of lack of normality.  

The two-way ANOVA design with interaction was used to compare the location as well as comorbidities on depression scores.  The (p-value = 0.262), there an insignificant interaction. Analyzing the p values for Location (p-value = 0.0516) and Health Status/Comorbidities (p-value <0.0001), indicates there is no significant difference in the population means based on location. However, with a p value less than .05 there is a significance difference based on health status.  A Tukey Comparison is used and needed here to compare the six different treatment combinations.

Discussion/Conclusion:

With 95% confidence we estimate that average scores for depression are 6.62 to 8.78 higher for individuals with comorbidities than for individuals that are healthy.

Analyzing the p values for Location (p-value = 0.0516) with 95% confidence we can estimate that there is no significant difference in location and depression scores in the individuals that were tested.

In the individuals that were tested in this study, there is statistical evidence that a person with a Health Status/Comorbidity is more likely to rate themselves at a higher level of depression than a Healthy participant. As displayed above, while there is not a significance to the geographical location of the participant, Florida residents on average scored lower on the depression evaluation than North Carolina or New York residents.

Result 1: Two Way ANOVA Depression   [Info]

Two Way Analysis of Variance results:


Responses: Score
Row factor: Location
Column factor: Health

ANOVA table


SourceDFSSMSF-StatP-value
Location254.01666727.0083333.04364370.0516
Health11778.71778.7200.44662<0.0001
Interaction224.0512.0251.35513050.262
Error1141011.68.8736842
Total1192868.3667

Tukey HSD results (95% level) for Location:


Florida subtracted from
DifferenceLowerUpperP-value
New York1.60.0182111423.18178890.0468
North Carolina0.475-1.10678892.05678890.7563
New York subtracted from
DifferenceLowerUpperP-value
North Carolina-1.125-2.70678890.456788860.2138

Tukey HSD results (95% level) for Health:


CMB subtracted from
DifferenceLowerUpperP-value
Healthy-7.7-8.777393-6.622607<0.0001

Tukey HSD results (95% level) for Location*Health:


Florida,CMB subtracted from
DifferenceLowerUpperP-value
Florida,Healthy-8.95-11.680654-6.2193455<0.0001
New York,CMB0.75-1.98065453.48065450.9676
New York,Healthy-6.5-9.2306545-3.7693455<0.0001
North Carolina,CMB-0.55-3.28065452.18065450.9919
North Carolina,Healthy-7.45-10.180654-4.7193455<0.0001
Florida,Healthy subtracted from
DifferenceLowerUpperP-value
New York,CMB9.76.969345512.430654<0.0001
New York,Healthy2.45-0.280654475.18065450.1054
North Carolina,CMB8.45.669345511.130654<0.0001
North Carolina,Healthy1.5-1.23065454.23065450.6053
New York,CMB subtracted from
DifferenceLowerUpperP-value
New York,Healthy-7.25

Result 2: QQ Plot   [Info]
Right click to copy

HTML link:
<A href="https://www.statcrunch.com/5.0/viewreport.php?reportid=90666">Depression scores in Older Adults</A>

Comments
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By nku.dr.nolan
Sep 23, 2019

Hi Karly - pretty good. One important comment. Do not do this: "while there is not a significance to the geographical location of the participant, Florida residents on average scored lower on the depression evaluation than North Carolina or New York residents". Saying there is no statistical significance means that there is no evidence of any difference between FL/NC/NY. so to turn around and say there is, that is just based on sample means alone which is not meaningful. Hope this helps!
By robinsons1963
Sep 21, 2019

Karly,

Did you look at a scatter plot to check for constant variability? I believe you have close to 10 comparisons in this research study. Did you find any limitations to the study? I feel like this is always one we go towards but having three geographic locations, they could of obtain many more participants.
By kelseywhitford
Sep 20, 2019

Karly,
I did the same study for my report. I also chose to mention the geographical location in my report. Technically since it's significance value is over 0.05, it wasn't needed but I thought I would add it in for the sake of my own learning to see what Dr. Nolan said since I felt like this weeks work may have been a little harder for me. I'm not sure why you stated that the Tukey pairwise comparisons were used to for "the six different treatment combinations." I think there were a lot more than just six, but we were really only looking at four of them. I think a better statement would have been "tukey pairwise comparisons were used to better look at the differences in our main effects."

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