Give an example of a situation related to your current job (or the j.pdf

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A professor would use a paired-samples t-test to compare the average midterm scores between two sections of a statistics course - one that meets three times a week for an hour, and one that meets once a week for three hours. The professor chose this example because comparing exam scores between sections could reveal differences in teaching strategy or methods between the sections. If differences were found, the professor would reevaluate their teaching for the lower performing section to help improve student performance and fulfill their job duties of effective instruction.

Education

Give an example of a situation related to your current job (or the job you would like to have after
graduation) in which you would use a paired-samples t-test. Why did you choose this example?
How would this information help you to perform your job duties?
Solution
Let's say I'm new professor teaching a college statistics course that is offered in
two sections: Section 1 meets early afternoons for an hour, 3 times per week. Section 2 meets
only on Thursday evenings, but for three hours. I would use the independent samples t-test to
check for significant differences between average midterm scores between the two class sections.
If it was shown that differences indeed existed between the exam scores, I would likely
reevaluate teaching strategy and methods for the class section that performed worse.

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Discussion: Discuss, elaborate and give example on the topic below. Please use the Reference/Module I provided below. Professor wont consider outside sources. Please be careful with spelling and Grammar. Author: Jackson, S.L. (2017) Statistics plain and simple. (4th ed.). Boston, MA: Cengage Learning Topic: When there is a between-subjects design, use a one-way between-subjects ANOVA, which uses only one independent variable. 275 words. Reference/Module Module 13: Comparing More Than Two Groups Using Designs with Three or More Levels of an Independent Variable Comparing More than Two Kinds of Treatment in One Study Comparing Two or More Kinds of Treatment with a Control Group Comparing a Placebo Group to the Control and Experimental Groups Analyzing the Multiple-Group Design One-Way Between-Subjects ANOVA: What It Is and What It Does Review of Key Terms Module Exercises Critical Thinking Check Answers Module 14: One-Way Between-Subjects Analysis of Variance (ANOVA) Calculations for the One-Way Between-Subjects ANOVA Interpreting the One-Way Between-Subjects ANOVA Graphing the Means and Effect Size Assumptions of the One-Way Between-Subjects ANOVA Tukey's Post Hoc Test Review of Key Terms Module Exercises Critical Thinking Check Answers Chapter 7 Summary and Review Chapter 7 Statistical Software Resources In this chapter, we discuss the common types of statistical analyses used with designs involving more than two groups. The inferential statistics discussed in this chapter differ from those presented in the previous two chapters. In Chapter 5, single samples were being compared to populations (z test and t test), and in Chapter 6, two independent or correlated samples were being compared. In this chapter, the statistics are designed to test differences between more than two equivalent groups of subjects. Several factors influence which statistic should be used to analyze the data collected. For example, the type of data collected and the number of groups being compared must be considered. Moreover, the statistic used to analyze the data will vary depending on whether the study involves a between-subjects design (designs in which different subjects are used in each group) or a correlated-groups design. (Remember, correlated-groups designs are of two types: within-subjects designs, in which the same subjects are used repeatedly in each group, and matched-subjects designs, in which different subjects are matched between conditions on variables that the researcher believes are relevant to the study.) We will look at the typical inferential statistics used to analyze interval-ratio data for between-subjects designs. In Module 13 we discuss the advantages and rationale for studying more than two groups; in Module 14 we discuss the statistics appropriate for use with between-subjects designs involving more than two groups. MODULE 13 Comparing More Than Two Groups Learning Objectives •Explain what additional information can be gained by using des ...

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Week 6 DQ 1. What is your research question? Is there a difference between the math utility of a male and a female? 2. What is the null hypothesis for your question? Hn There is no difference in the math utility between male and female. Alternative hypotheses can also be created in the case the null hypothesis is proven incorrect. Two alternative hypotheses are: Ha1 Feales have a higher math utility. Ha2 Males have a higher math utility. 3. What research design would align with this question? According to Frankfort-Nachmias and Leon-Guerrero (2015) a descriptive research design would be best for this type of study. 4. What comparison of means test was used to answer the question (be sure to defend the use of the test using the article you found in your search)? The independent-samples T test was used to analyze the means for this data. 5. What dependent variable was used and how is it measured? The dependent variable is the student’s math utility. It is measured from -3.51 to 1.31(University high school longitudinal study dataset. (2009). 6. What independent variable is used and how is it measured? Either male (1) of female (2) (University high school longitudinal study dataset. (2009). 7. If you found significance, what is the strength of the effect? The significance was 0.0000. This is much better than the standard of .05 significance as outlined by Frankfort-Nachmias and Leon-Guerrero (2015). 8. Identify your research question and explain your results for a lay audience, what is the answer to your research question? My research question was “Is there a difference between the math utility of a male and a female?” Based on the analysis of the means (or average) through testing using the independent-samples T test there was no measurable difference between the math utility of male or females. This leads us to accept the null hypothesis of “There is no difference in the math utility between male and female” as true. Group Statistics T1 Student's sex N Mean Std. Deviation Std. Error Mean T1 Scale of student's mathematics utility Male 9453 .0140 1.01962 .01049 Female 9349 -.0481 .97291 .01006 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper T1 Scale of student's mathematics utility Equal variances assumed 17.400 .000 4.276 18800 .000 .06216 .01454 .03367 .09066 Equal variances not assumed 4.277 18775.932 .000 .06216 .01453 .03367 .09065 University high school longitudinal study dataset. (2009). References Frankfort-Nachmias, C., & Leon-Guerrero, A. (2015). Social statistics for a diverse society (7th ed.). Thousand Oaks, CA: Sage Publications. University high school longitudinal study dataset. (2009). Retrieved from class.waldenu.edu The t Test for Related Samples The t Test for Related Samples Program Transcript MAT.

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