According to Polit & Beck (2016), “ANCOVA offers post hoc statistical control” (p. 427). Furthermore, “ANCOVA can result in more precise estimates of group differences because, even with randomization, there are typically slight differences between groups. ANCOVA adjusts for initial differences so that the results more precisely illuminate the effect of an intervention”. It “allows researchers to control confounding variables statistically” (p. 427). It is useful when random sampling is unavailable or not an option. ANCOVA can sometimes improve the internal validity of a study.
I believe an example of ANCOVA could be in a study researching different types of diets. Suppose I was trying to see how much weight people could lose on three different types of diets. The dependent variable would be weight lost and the independent variable would be the type of diet. I may not be able to randomly assign people to each different type of data due to health reasons. For example, a diabetic may not be able to be on a no carb diet or a person with a history of gallstones may not be able to be on a higher fat diet, etc. This is why ANCOVA is helpful when randomization is not possible.
A covariance could be a participant’s height, because these could influence the amount of weight lost. By utilizing ANCOVA, I would obtain the means of each groups’ weight loss. I could then adjust the data obtained from the participants’ weight loss while accounting for height. This would then increase the validity of the study.
Polit, D. F., & Beck, C. T. (2016). Nursing research: Generating and assessing evidence for nursing practice (10th ed.). Philadelphia, PA: Lippincott Williams & Wilkins.
When comparing analysis of more than two groups the probability of error increases. ANOVA is the most used statistical test in evidence base practice when there are more than two groups being compared. ANOVA is analysis of variance with a single dependent variable. ANCOVA is the analysis when there is a single dependent variable with potential covariances, hence the CO in ANCOVA- the analysis of covariances (Houser, 2018).
ANCOVA is used when the researcher is finding if there is an impact from an extraneous variable has an impact, like if socioeconomics impacted the outcomes in a treatment group (Houser). Another example of ANCOVA use would be determining length of stays for a patient who has family support all, some, or none of the time while in the hospital. Three groups are being studied but what about the variable of why the family isn’t present? ANCOVA would be the analysis of covariance in this analysis.
According to Northern Arizona University (NAU), ANCOVA is used in experimental studies when researchers want to remove the effects of some antecedent variable (NAU, n.d.). It is another way to remove the variables the researchers do not want to study in the research. NAU also points out, “the covariate role is to reduce the probability of a Type II error when tests are made of main or interaction effects, or when comparisons are made within planned or post hoc investigations. Since the probability of a Type II error is inversely related to statistical power, the ANCOVA will be more powerful than its ANOVA counterpart, presuming that other things are held constant and that a good covariate has been used within the ANCOVA” (Northern Arizona University, Nov. 12, 2017).
ANCOVA provides the best estimates of how the comparison groups would have performed if they had all possessed identical (statistically equivalent) means on the control variable (NAU, n.d.) This is why post hoc is referred to it is post outcomes of the impact of variables.
An example of ANCOVA being applied is in the study by Edwards, Beck, & Lim (2014) studying the effects of an aquarium on dementia residents and staff satisfaction. The ANCOVA was used in a mixed-model approach to see the differences in the pre-and posttest of the residents’ behaviors and staff job satisfaction. The co-variances were identified as differences in the facilities and the characteristics of the residents.
“ANCOVA adjusts for initial differences so that the results more precisely illuminate the effect of an intervention” (Polit & Beck, 2017). This means that after a study is done, the confounding variables can be removed. After they are removed, the results are then statistically analyzed so that the results are more reliable to report. Post hoc means that the procedure is done after the study is complete, so this cannot be done beforehand.
In a study that uses ANCOVA, covariate effects are removed to change the significance of the study outcome. An example that was used in the textbook is the level of anxiety after using biofeedback therapy. The covariate that was removed was the patients’ pretest anxiety scores. This result would change the outcome of the study but are not important in finding out how effective biofeedback is on anxiety. Removing this data will help improve the results of the study and will be more valid.
ANCOVA is one of many ways to control confounding variables as long as it is done properly. This form uses statistical analysis which is very useful for providing data results. ANCOVA can be very useful in validating research studies. The important thing to know is what covariate can be removed to improve the validity of the study and which covariates need to be kept. Removing the wrong covariate can diminish the results and make them unreliable.
Polit, D.F. & Beck, T.B. (2017). Nursing research: Generating and assessing evidence for nursing practice
(10 th Ed.) Philadelphia, PA. Wolters Kluwer Health.
The analysis of variance (ANOVA) and analysis of covariance (ANCOVA) are among the most popular statistical methods in psychology and related sciences. Most ANOVA or ANCOVA studies test the null hypothesis that the population group means are all equal. If the test result is statistically signiﬁcant, the study reports that there is some difference among the groups. (ANCOVA) allows the researcher to examine the effect of a treatment apart from the effect of one or more potentially confounding variables. Potentially confounding variables that are generally of concern include pretest scores, age, education, social class, and anxiety level. These variables would be confounding if they were not measured and if their effects on study variables were not statistically removed by performing regression analysis before performing ANOVA (Grove, Gray, & Burns, 2015).
ANCOVA allows researchers to control confounding variables statistically, and when experimental control through randomization is lacking, ANCOVA offers post hoc statistical control (Polit & Beck, 2017). When the difference among groups caused by these confounding variables is removed or eliminated, the effect of the treatment can be examined more precisely (Grove, Gray, & Burns, 2015).
An example of research that utilized and ANCOVA method is a study by Parnan, Tafazoli, and Azmoude (2017). The authors used the ANCOVA approach to analyze and compare the sexual function of women with or without diabetes. Given the significant differences between the groups in terms of BMI, ANCOVA was applied to control the effect of this variable.
Grove, S., Gray, J., Burns, N. (2015). Understanding nursing research: Introduction to qualitative research (6th Ed). St. Louis, MO: Elsevier/Saunders. Retrieved from https://pageburstls.elsevier.com/#/books/9781455770601/