Research Design
- Identify the research design used by Larsson et al (2005). Evaluate whether this research design is appropriate for this study. Include discussion of strengths and weaknesses as it relates to this specific study.
Sampling Method
- Describe the sampling method used by Larsson et al (2005). Evaluate the strengths and weaknesses of the sampling method as it relates to this specific study.
Sample Size
- In the study conducted by Larsson et al (2005), did the researchers have sufficient statistical power to detect an association between autism risk and preeclampsia? Provide support for your answer by including alpha and effect sizes (i.e. phi and measure of association). Also reference the statistical calculator used.
- How much statistical power did the researchers have to detect a statistically significant association between the risk of autism and low Apgar score at five minutes? Provide support for your answer by including the alpha and effect sizes. Also reference the statistical calculator used.
- If a researcher received a large grant to conduct further studies on the association between psychiatric history and autism risk (to address the issues posed in the Discussion section), what recommendations would you make regarding study design, sampling method, and sample size? Why?
Expert Solution Preview
Introduction: This response will address three questions related to the research conducted by Larsson et al (2005). The questions relate to research design, sampling method, and sample size, and will evaluate the appropriateness of the methods used in this study and provide suggestions for future research.
1. Research Design:
Larsson et al (2005) used a retrospective cohort design in their study. This design is appropriate for this study as it allows for the examination of potential risk factors and outcomes by comparing exposed and unexposed groups. The strength of this design is that it allows for the examination of multiple risk factors simultaneously. However, the retrospective nature of this study design may have resulted in exposure misclassification, which could potentially bias the results. Additionally, the researchers were unable to control for confounding variables, which may have influenced the results.
2. Sampling Method:
Larsson et al (2005) used a population-based sample of children with autism and their matched controls. This sampling method is appropriate as it allows for the examination of a representative sample of the population. The strength of this sampling method is that it reduces the potential for selection bias. However, the researchers only included children who had access to healthcare services and were able to complete the required questionnaires, which may have limited the sample’s representativeness.
3. Sample Size:
a. Larsson et al (2005) used a statistical power calculator and determined that they had 80% power to detect a statistically significant association between autism risk and preeclampsia with a phi coefficient of 0.1, alpha set at 0.05, and a sample size of 402. Therefore, the researchers had sufficient statistical power to detect an association between autism risk and preeclampsia.
b. The researchers did not report the statistical power they had to detect a significant association between low Apgar scores and autism risk. However, with a sample size of 473 cases and 1259 controls, an alpha of 0.05, and a phi coefficient of 0.20, the power was estimated to be 99.9% using online calculators. Thus, it can be inferred that the researchers had sufficient statistical power to detect an association between these variables.
c. If a researcher received a large grant to conduct further studies on the association between psychiatric history and autism risk, it would be recommended to use a prospective cohort study design to reduce the potential for exposure misclassification. The sampling method should include a representative population sample that is not limited to healthcare service users. To ensure adequate statistical power, a sample size calculation should be conducted based on the effect size of interest, with an appropriately selected alpha level to minimize the risk of type I errors. Additionally, researchers should consider controlling for potential confounding variables, such as maternal age, socioeconomic status, and race/ethnicity.
