Simply reporting measures of central tendency or measures of variability will not tell the whole story. Using the following information, what else does a psychologist need to know or think about when interpreting this information?
A school psychologist decided to separate some classes by gender to see if learning improved. She looked at student scores on the final exam and obtained the following information: Students in boy-girl classrooms obtained an average of 71.4 on their final exams with a standard deviation of 10.8 whereas students in single-gendered classrooms obtained an average of 75.9 on their final exams with a standard deviation of 8.2. She concludes that the single-gendered classrooms lead to better learning.
What is your question? You are lacking n for each group.
Z = (mean1 - mean2)/standard error (SE) of difference between means
SEdiff = √(SEmean1^2 + SEmean2^2)
SEm = SD/√n
If only one SD is provided, you can use just that to determine SEdiff.
Find table in the back of your statistics text labeled something like "areas under normal distribution" to find the proportion/probability related to your Z score.
what else does a psychologist need to know or think about when interpreting this information?
A school psychologist decided to separate some classes by gender to see if learning improved. She looked at student scores on the final exam and obtained the following information: Students in boy-girl classrooms obtained an average of 71.4 on their final exams with a standard deviation of 10.8 whereas students in single-gendered classrooms obtained an average of 75.9 on their final exams with a standard deviation of 8.2. She concludes that the single-gendered classrooms lead to better learning.
When interpreting the information provided, there are several factors that a psychologist needs to consider in addition to simply looking at the measures of central tendency and variability.
1. Sample size: It is important to know the number of students in each group to determine if the sample is representative of the population. A larger sample size increases the generalizability of the results.
2. Randomization: The psychologist should ensure that the assignment of students to different classrooms was done randomly. If there was any bias in the assignment process, it could affect the validity of the conclusions.
3. Confounding variables: The psychologist should consider other factors that might explain the differences in final exam scores. Factors such as the quality of teaching, classroom environment, student motivation, or prior knowledge could contribute to the observed differences.
4. Statistical significance: It is valuable to assess whether the observed differences are statistically significant. This can be done by conducting hypothesis tests or calculating confidence intervals. Statistical significance helps determine if the results are likely due to the intervention (single-gendered classrooms) rather than random chance.
5. Replication: The psychologist should consider whether similar results have been found in previous research. Replicating the study with different samples or in different settings increases confidence in the findings.
6. Potential biases: The psychologist should be aware of any potential biases or conflicts of interest that could influence their interpretation of the results. It is important to approach the analysis with objectivity and transparency.
Taking all these factors into account will provide a more comprehensive understanding of the results and the conclusions drawn from them.