Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Obesity01:24

Obesity

1.1K
The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
1.1K
Coefficient of Correlation01:12

Coefficient of Correlation

8.2K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
8.2K
Correlations02:20

Correlations

35.7K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
35.7K
Correlation01:09

Correlation

14.5K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
14.5K
Correlation and Regression00:53

Correlation and Regression

3.0K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.0K
Cause and Effect01:53

Cause and Effect

12.0K
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
12.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Studies of encephalitis on Taiwan. VI. Infections in American servicemen on Taiwan and Okinawa.

Report (U.S. Naval Medical Research Laboratory)·2014
Same author

Studies of encephalitis on Taiwan. VI. Infections in American servicemen on Taiwan and Okinawa.

Report (U.S. Naval Medical Research Laboratory)·2014
Same author

Use of purified antigen for detection of Paragonimus westermani and Clonorchis sinensis in peoples of Hsinchu Hsien, Taiwan (Formosa).

Report (U.S. Naval Medical Research Laboratory)·2014
Same author

Cholera: physiological alterations and their correction.

Report (U.S. Naval Medical Research Laboratory)·2014
Same author

Profiling overweight patients in the U.S. Navy: health conditions and costs.

Military medicine·1991
Same author

Health profile of U.S. Navy pilots of electronically modified aircraft.

Aviation, space, and environmental medicine·1988

Related Experiment Video

Updated: Jan 10, 2026

Influence of Emotional Factors on the Efficacy of Acupuncture Treatment for Overweight Complicated with Hyperlipidemia: A Retrospective Cohort Study
03:05

Influence of Emotional Factors on the Efficacy of Acupuncture Treatment for Overweight Complicated with Hyperlipidemia: A Retrospective Cohort Study

Published on: November 21, 2025

456

Correlates of obesity

A Hoiberg, S P Berard, R H Watten

    Journal of Clinical Psychology
    |October 1, 1980
    PubMed
    Summary

    Eating behaviors like food obsession and history of overweight are key factors linked to obesity in Navy weight-reduction programs. These eating patterns significantly correlate with obesity across different participant groups.

    Area of Science:

    • Behavioral Science
    • Obesity Research
    • Psychology

    Background:

    • Understanding eating behaviors is crucial for effective weight management interventions.
    • Previous research indicates a complex interplay between psychological factors and obesity.
    • Navy weight-reduction programs offer a unique setting to study these relationships in a large, diverse population.

    Purpose of the Study:

    • To develop composite scores from eating behavior questionnaire items.
    • To examine the relationship between these composite scores and an obesity index.
    • To identify key eating behavior correlates of obesity within distinct Navy program subsamples.

    Main Methods:

    • Item and scale analyses were conducted on questionnaire data from 1,878 participants.

    More Related Videos

    Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
    14:56

    Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

    Published on: May 6, 2022

    5.0K
    Assessment of Child Anthropometry in a Large Epidemiologic Study
    09:36

    Assessment of Child Anthropometry in a Large Epidemiologic Study

    Published on: February 2, 2017

    27.7K

    Related Experiment Videos

    Last Updated: Jan 10, 2026

    Influence of Emotional Factors on the Efficacy of Acupuncture Treatment for Overweight Complicated with Hyperlipidemia: A Retrospective Cohort Study
    03:05

    Influence of Emotional Factors on the Efficacy of Acupuncture Treatment for Overweight Complicated with Hyperlipidemia: A Retrospective Cohort Study

    Published on: November 21, 2025

    456
    Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
    14:56

    Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

    Published on: May 6, 2022

    5.0K
    Assessment of Child Anthropometry in a Large Epidemiologic Study
    09:36

    Assessment of Child Anthropometry in a Large Epidemiologic Study

    Published on: February 2, 2017

    27.7K
  • Composite scores were calculated for eating behaviors, weight history, activities, and feelings about food.
  • Multiple regression analyses were used to determine the association between composites and obesity index.

    • Main Results:

    • The strongest correlates of obesity across subsamples were Overweight History, Food Obsession, and Activities composites.
    • Significant multiple R values were observed: .49 (Marine recruits), .46 (other males), and .32 (females).
    • Emotional Eater composite was a significant correlate for women; personality characteristics were associated with obesity in both male and female subsamples.

    Conclusions:

    • Specific eating behavior composites, including history of overweight and food obsession, are significantly associated with obesity.
    • These findings highlight the importance of addressing psychological and behavioral aspects of eating in weight-reduction strategies.
    • Further research is warranted to explore the predictive value of these eating behavior variables for weight loss and maintenance.