Odds Ratio in Logistic Regression Quantitative Research

Exclusively available on Available only on IvyPanda® Made by Human No AI

In logistic regression, odds ratio indicates the nature and the degree of association between a dependent variable and independent variables. Macdonald (2015) states that odds ratio applies to the prediction of a dependent variable using independent variables in logistic regression analysis. In this case, the dependent variable is burnout while the independent variables are coping style and stress from teaching.

The dependent variable is a binary categorical variable because it comprises burnout and no burnout. The independent variables contain continuous data, which indicate the degree of coping style and stress from teaching. High scores and low scores of coping style indicate high and low ability to cope with stress respectively.

Similarly, high scores and low scores of stress from teaching show high and low levels of stress that emanate from teaching. The odds ratio in this case originates from the comparison of the probability of burnout and the probability of no burnout. In this view, the logistic analysis applies these variables in demonstrating odds ratio.

Table 1 indicates that there is significant difference between logistic regression model with a constant only and the logistic regression model with all independent variables, namely, coping style and stress from teaching, (χ2(2) = 137, p = 0.000).

Table 1. Omnibus Tests of Model Coefficients

Chi-squaredfSig.
Step 1Step137.0152.000
Block137.0152.000
Model137.0152.000

Table 2

Model Summary
Step-2 Log likelihoodCox & Snell R SquareNagelkerke R Square
1393.092.254.375

The model summary table (Table 2) shows that coping style and stress from teaching explain 25.4% (Cox & Snell R Square) or 37.5% (Nagelkerke R Square) of the variation in burnout. The explanatory power is important in interpreting odds ratio, which describe the nature and the strength of the relationship between burnout, the dependent variable, and coping style and stress from teaching, the independent variables.

Table 3. Classification Table

ObservedPredicted
BurnoutPercentage Correct
Not Burnt OutBurnt Out
Step 1BurnoutNot Burnt Out3202892.0
Burnt Out655445.4
Overall Percentage80.1

Table 3 is a contingency table, which shows the distribution for 80.1% of the total lecturers (N = 467). In this view, the table suggests that the model can predict 80.1% of the total lecturers, which is very significant.

From the logistic regression model, it is evident that coping style (Wald χ2(1) = 71.287, p = 0.000) and stress from teaching (Wald χ2(1) = 5.760, p = 0.016) are significant predictors of burnout. Thus, the statistical significance of coping style and stress from teaching validates the use of these predictors in the analysis of odds ratio.

In interpreting odds ratio, Field (2013) states that odds ratio greater than one indicates that a predictor variable increases a criterion variable while odds ratio less than one indicates that the predictor variable reduces criterion variable. From Table 4, the odds ratio for coping style and burnout is 1.111 (95% CI, 1.084 to 1.138).

The odds ratio suggests that a unit increase in coping ability increases burnout by 11.1%. The odds ratio for stress from teaching and burnout is 0.968 (95% CI, 0.943 to 0.994). The odds ratio suggests that a unit increase in stress from teaching results in a reduction of burnout by 3.2%.

Table 4. Variables in the Equation
a. Variable(s) entered on step 1: cope, teaching.

BS.E.WalddfSig.Exp(B)95% C.I. for EXP(B)
LowerUpper
Step 1aCope.105.01271.2871.0001.1111.0841.138
Teaching-.032.0135.7601.016.968.943.994
Constant-2.077.59212.3081.000.125

References

Field, A. (2013). Discovering statistics using SPSS (4th ed.). London: SAGE Publisher.

Macdonald, S. (2015). Essentials of Statistics with SPSS. Raleigh: Lulu.com Publisher.

More related papers Related Essay Examples
Cite This paper
You're welcome to use this sample in your assignment. Be sure to cite it correctly

Reference

IvyPanda. (2019, June 24). Odds Ratio in Logistic Regression. https://ivypanda.com/essays/odds-ratio/

Work Cited

"Odds Ratio in Logistic Regression." IvyPanda, 24 June 2019, ivypanda.com/essays/odds-ratio/.

References

IvyPanda. (2019) 'Odds Ratio in Logistic Regression'. 24 June.

References

IvyPanda. 2019. "Odds Ratio in Logistic Regression." June 24, 2019. https://ivypanda.com/essays/odds-ratio/.

1. IvyPanda. "Odds Ratio in Logistic Regression." June 24, 2019. https://ivypanda.com/essays/odds-ratio/.


Bibliography


IvyPanda. "Odds Ratio in Logistic Regression." June 24, 2019. https://ivypanda.com/essays/odds-ratio/.

If, for any reason, you believe that this content should not be published on our website, please request its removal.
Updated:
This academic paper example has been carefully picked, checked and refined by our editorial team.
No AI was involved: only quilified experts contributed.
You are free to use it for the following purposes:
  • To find inspiration for your paper and overcome writer’s block
  • As a source of information (ensure proper referencing)
  • As a template for you assignment
Privacy Settings

IvyPanda uses cookies and similar technologies to enhance your experience, enabling functionalities such as:

  • Basic site functions
  • Ensuring secure, safe transactions
  • Secure account login
  • Remembering account, browser, and regional preferences
  • Remembering privacy and security settings
  • Analyzing site traffic and usage
  • Personalized search, content, and recommendations
  • Displaying relevant, targeted ads on and off IvyPanda

Please refer to IvyPanda's Cookies Policy and Privacy Policy for detailed information.

Required Cookies & Technologies
Always active

Certain technologies we use are essential for critical functions such as security and site integrity, account authentication, security and privacy preferences, internal site usage and maintenance data, and ensuring the site operates correctly for browsing and transactions.

Site Customization

Cookies and similar technologies are used to enhance your experience by:

  • Remembering general and regional preferences
  • Personalizing content, search, recommendations, and offers

Some functions, such as personalized recommendations, account preferences, or localization, may not work correctly without these technologies. For more details, please refer to IvyPanda's Cookies Policy.

Personalized Advertising

To enable personalized advertising (such as interest-based ads), we may share your data with our marketing and advertising partners using cookies and other technologies. These partners may have their own information collected about you. Turning off the personalized advertising setting won't stop you from seeing IvyPanda ads, but it may make the ads you see less relevant or more repetitive.

Personalized advertising may be considered a "sale" or "sharing" of the information under California and other state privacy laws, and you may have the right to opt out. Turning off personalized advertising allows you to exercise your right to opt out. Learn more in IvyPanda's Cookies Policy and Privacy Policy.

1 / 1