NURS 8201 t-Tests and ANOVA

NURS 8201 t-Tests and ANOVA

NURS 8201 t-Tests and ANOVA

Statistics play an important role in analyzing the primary data collected to examine a problem in clinical practice. Despite its complexity, it allows the researcher to deduce the meaning of the data to support evidence-based practice (Weaver et al., 2017). The precision required in nursing studies has led to a demanding task among nursing scholars. These demands have compelled them to use inferential statistical analysis to achieve the accuracy and dependability of the results.

My topic of interest is to examine the prevalence of obesity among children. The increase in cases of childhood obesity has been drawing more attention from scholars because of its negative effect on health outcomes among children (Smith et al., 2021). The number of children with obesity has doubled in the last two decades, calling for effective intervention that would counter this menace. The increased rate of children with obesity calls for accurate studies that would reveal the underlying problem and propose an effective EBP practice that would act as an effective intervention for the problem.

Article Summary

The study authored by Katzmarzyk et al. (2019) focuses on the effect of lifestyle behavior and environment on childhood obesity. The study’s primary objective was to examine the relationship between lifestyle behaviors and obesity. The study termed as ISCOLE was a multi-national study carried out on children aged 9-11 years from 12 countries across the continent. The primary focus of the study was on the result gained from the primary data collected for this study. 7372 children aged between 9-11 years participated in the study. The study used ISCOLE design and methods, which was a multi-national study done in 12 countries.

Inferential statistics separated the data from countries where the reading on the Human Development Index (HDI) produced a range of 0.509 in Kenya to 0.929 in Australia (Katzmarzyk et al., 2019). The descriptive statistics effectively organized data from each country and showed how the variables considered in the study changed in each country. The study also went further to correlate obesity and lifestyles behavior at different levels, where it found that children with active school transport had lower chances of becoming obese. For instance, the odds ratio was 0.72 at a 95% confidence interval. In essence, inferential statistics was important in breaking down the data from the 12 countries into meaningful pieces that readers could easily understand.

This study was important in revealing how various factors such as average income in a country affect the lifestyle behaviors in families that further relay more information on childhood obesity. The analysis of the big sampled data from the countries resulted in reliable information that could be applied to all the countries included in the study (Katzmarzyk et al., 2019). Inferential statistics in the study strengthened the results by revealing the relationship between dependent variables and multiple independent variables considered in the study. The analysis used in the study strengthened the application of the evidence-based practice as it showed the effect that lifestyle changes had on childhood obesity. For example, the study proved that increasing physical activity among children during school hours and at home reduces their chances of becoming obese. Therefore, if children and parents in the selected countries with high childhood obesity could adopt the EBP practice of increasing physical activity, then the prevalence rates in those countries could decrease drastically.

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The importance of inferential statistics could also be evident in the correlation of the variables that had a greater effect on childhood obesity and those variables that had a comparatively lower impact on obesity. For example, the study found a high correlation between physical activity and obesity. On the other hand, the study found that school transport and obesity did not differ by country or sex.

 

Reference

Katzmarzyk, P. T., Chaput, J. P., Fogelholm, M., Hu, G., Maher, C., Maia, J., … & Tudor-Locke, C. (2019). International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): contributions to understanding the global obesity epidemic. Nutrients11(4), 848. https://dx.doi.org/10.3390%2Fnu11040848

Smith, H. J., Piotrowski, J. I., & Zaza, S. (2021). Ethics of implementing US Preventive Services Task Force recommendations for childhood obesity. Pediatrics148(1). https://doi.org/10.1542/peds.2020-048009.

Weaver, K. F., Morales, V. C., Dunn, S. L., Godde, K., & Weaver, P. F. (2017). An introduction to statistical analysis in research: with applications in the biological and life sciences. John Wiley & Sons.

Excellent Post, Daisy. Analysis of variance (ANOVA) is one of the most frequently used statistical methods in medical

NURS 8201 t-Tests and ANOVA
NURS 8201 t-Tests and ANOVA

research. The need for ANOVA arises from the error of alpha level inflation, which increases Type 1 error probability and is caused by multiple comparisons. ANOVA uses the statistic F, the ratio of between and within-group variances. (Kim T. K. 2017). The main interest of analysis is focused on the differences of group means; however, ANOVA focuses on variances. The ANOVA test allows a comparison of more than two groups simultaneously to determine whether a relationship exists between them. You’re testing groups to see if there’s a difference between them. Examples of when you might want to test different groups: A group of psychiatric patients is trying three various therapies: counseling, medication, and biofeedback. You want to see if one therapy is better than the others.

 

Reference

Kim T. K. (2017). Understanding one-way ANOVA using conceptual figures. Korean journal of anesthesiology70(1), 22–26. https://doi.org/10.4097/kjae.2017.70.1.22

Thank you for your post this week. True, statistics are essential in quantitative research; they can also facilitate data analysis in a clinical setting. However, only when statistics are done appropriately, they can benefit science and advance nursing practice. Please let me add a few comments regarding the study you discussed in your post.

To begin with, the article does not include a detailed description of statistics or data analysis. According to Katzmarzyk et al. (2019), the design and methods of the study had been discussed elsewhere. I found this information in an earlier publication. In it, Katzmarzyk et al. (2013) say that the study would involve descriptive and inferential statistics, namely, multilevel random-effects models and covariate-adjusted models. The choice of the model looks appropriate, since the ISCOLE study involves numerous independent variables, such as physical activity, dietary patterns, and so on. Complex statistical models are well-suited to analyze complex correlational or causal links among variables. However, they also have limitations. For example, they can be time consuming.

The main question is whether the inferential statistics used in the study were sufficient and effective enough to identify cause-and-effect relationships. Katzmarzyk et al. (2019) note that their statistical models had limitations, making causal inferences problematic or questionable. They could have used ANOVA or t-test, but they would not be suitable in the analysis of causal relationships. According to Schober et al. (2018), correlations do not imply causality; nor do they say enough about the strength of the relationship between the independent and dependent variable. Besides, in the discussed study, data were collected at a single point of time, which could limit their utility in identifying cause-and-effect relationships.

Notwithstanding these limitations, the use of inferential statistics was beneficial, as it added to and expanded descriptive statistical results. The researchers were able to demonstrate the complexity of factors affecting childhood obesity rates across countries. They may still need additional data to address weaknesses in method and design, such as using other inferential statistics to validate the initial findings.

References

Katzmarzyk, P.T., Barreira, T.V., Broyles, S.T., Champagne, C.M., Chaput, J.P., Fogelholm,

M., Hu, G., Johnson, W.D., Kuriyan, R., Kurpad, A., Lambert, E.V., Maher, C., Maia, J., Matsudo, V., Olds, T., Onywera, V., Sarmiento, O.L., Standage, M., Tremblay, M.S., Tudor-Locke, C., Zhao, P., & Church, T.S. (2013). The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): design and methods. BMC Public Health, 13, 900. https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-13-900

Katzmarzyk, P. T., Chaput, J. P., Fogelholm, M., Hu, G., Maher, C., Maia, J., Olds, T.,

Sarmiento, O.L., Standage, M., Tremblay, M.S., & Tudor-Locke, C. (2019). International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): contributions to understanding the global obesity epidemic. Nutrients,11(4), 848. https://dx.doi.org/10.3390/nu11040848

Schober, P., Boer, C., & Schwarte, L.A. (2018). Correlation coefficients: Appropriate use and

interpretation. Anesthesia & Analgesia, 126(5), 1763-1768. https://doi.org/10.1213/ANE.0000000000002864