Opinions for module 6.1 and 6.2

| November 19, 2015

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Connie Moore

1 posts

Re:Module 6 DQ 1
In Module 4, we considered Mary’s interest in doing a study to see if learning of 6th graders on a math lesson is affected by background noise level. There, she was planning to use 2 noise conditions and then analyze her outcomes using a t-test for independent groups. Describe the study Mary might plan where she would use a one-way analysis of variance (ANOVA) for independent groups instead of a t-test to study differences between noise levels. What is her independent variable here? Describe the conditions she could create for this study. What is her dependent variable? Describe a way to measure the DV so that each participant would have one score at the end that would be on a continuous scale of measurement. Support your responses.

The independent variable is the background noise level.

The one-way ANOVA requires only one independent variable which has at least 3 levels.

For the one-way analysis of variance (ANOVA), Mary must consider at least 3 levels of background noise level. The levels could be low, medium and high. The conditions she could come up with is considering the grades of each student’s exam on the math lesson where 0 is the lowest grade and 100 is the highest. If data is collected for the dependent variable in this way, then each student would have one score for the math lesson on a continuous scale of measurement. The dependent variable is learning of 6th graders on a math lesson. To use one-way ANOVA, the dependent variable must be in a continuous scale. Measuring the DV with ANOVA will determine whether the mean of the dependent variable is the same in two or more unrelated, independent groups.

The one-way analysis of variance (ANOVA) is used to determine whether the mean of a dependent variable is the same in two or more unrelated, independent groups. However, it is typically only used when you have three or more independent, unrelated groups, since an independent-samples t-test is more commonly used when you have just two groups. If you have two independent variables, you can use a two-way ANOVA.

Reference:

Laerd Statistics. Retrieved from: https://statistics.laerd.com/stata-tutorials/one-way-anova-using-stata.php

 

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Danielle Davis

1 posts

Re:Module 6 DQ 2
 

Consider Mary’s study of whether the learning of 6th graders on a math lesson is affected by background noise level. Describe the pros and cons of Mary using a repeated measures design for this study. What would you probably choose to do? Support your responses.

 

Repeated measures ANOVA (Analysis of Variance) is also referred to as a “within-subjects ANOVA,” “dependent groups,” and “ANOVA for correlated samples” (Haneef, 2014).
According to Ho (2010), one of the major pros of utilizing such a design would allow Mary to have multiple observations of the independent variable on each participant, e.g., a pre-test, subsequent post-test and follow-up, as well as, having the dependent variable measured on the same individual at three different times, e.g., pre-treatment, at outcome, and at a follow-up (let’s say 6 months later).

 

Additionally, as stated by Gravetter and Wallnau (2010), the repeated measures employs the same participants in every treatment condition and means less participants
needed, thus Mary will not need to have a large sample size; however, on the other hand, where the smaller sample size can be a pro, the con would revolve around order effects within that small sample (Ho, 2010). Order effects refer to the order of the treatments given having an effect on the participants’ performance. For instance, Mary’s participant performance within the second, and or third, condition may become better because the participants already know what to expect or what to do, or it may become worst if they become tired (Ho, 2010).

 

I believe that I would probably go with the repeated measures as not as many people are needed, thus less time-consuming, and if I was to be concerned with order effects, I could look at counter-balancing, which is simply alternating the order of the conditions, providing for an equally distributed practice effect across conditions (Ho, 2010).

 

Danielle

REFERENCES:

 

Gravetter, F. J. &Wallnau, L. B. (2010).Statistics for the behavioral sciences (9th ed.). Belmont, CA: Wadsworth Cengage Learning.

 

Haneef, A. (n.d.). Repeated measures ANOVA. Retrieved from: http://www.aamnahaneefrepeatedmeasures.com.

 

Ho, S. (2010). ANOVA: Analysis of variance. Retrieved from: http://www.twu.seanho.com.

 

 

 

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Sonja DamnjanovicRadovanovic

1 posts

Re:Re:Module 6 DQ 2
Order effect would be one of the con of repeated measure design. What could a researcher do to counter this problem?

“Consider Mary’s study of whether the learning of 6th graders on a math lesson is affected by background noise level. Describe the pros and cons of Mary using a repeated measures design for this study. What would you probably choose to do? Support your responses.

The first pro for Mary to use repeated measure would allow her to remove the effects that come from individual differences.
Hence, factors such as age among other important factors would remain similar. The second pro is that Mary would obtain few participants. Hence, it is easier and faster to recruit the 6th-grade students. However, the con of repeated measure approach would involve having the trouble of counter problems of order effect (Mackey &Gass, 2011). The effects could also come from repetition
when Mary gives the 6th graders more time to adjust. The second con is that Mary will have to change the wording for participants to memorize. I would choose to use ANOVA to test two different groups. I will make different observations for the two groups. It will be easy to analyze data and make inferences (Sadooghi-Alvandiet al., 2012).

References

Mackey, A., &Gass, S.M. (2011).Research methods in second language acquisition: A practical guide. Hoboken, New Jersey: John Wiley & Sons

Sadooghi-Alvandi, S.M., Jafari, A.A., &Mardani-Fard, H.A. (2012).One-way ANOVA with unequal variances.Communications in Statistics: Theory & Methods, 41(22), 4200-4221”

 

 

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