BUSI 820 Week 4 Discussion

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Feb 20, 2024

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WEEK 4 DISCUSSION 1 D4.5.1 Compare and Contrast a Between-Groups Design and a Within-Subjects Design. In a between-groups design, different participants receive different treatments or levels of the independent variable, resulting in distinct groups. For instance, if comparing two math curricula for 8th graders, each curriculum would be assigned to a different set of students (Morgan et al., 2020). In such designs, provided that group assignment is randomized, causal estimates are derived by contrasting the behavior of individuals in one experimental condition with those in another (Charness et al., 2012). In a within-subjects design, the same participants undergo multiple treatments or are measured on the same variable at different times. This design, also known as a repeated measures design, is often exemplified by pretest-posttest setups or longitudinal studies. For example, monitoring 20 people's blood pressure and cholesterol levels weekly over 10 weeks (Morgan et al., 2020). In such designs, as long as there is independence in the various exposures, causal estimates can be derived by observing how individual behavior is affected when the experimental conditions change (Charness et al., 2012). D4.5.2. What Information About Variables, Levels, And Design Should You Keep in Mind in Order to Choose an Appropriate Statistic? We want to know how many variables there are in the research question or hypothesis. This information will lead us to use whether bivariate (2 variables) or complex statistics (more than 2 variables) should be used. Next, we want to know whether the independent variable is normal, or has a few (2 to 4) levels, or has more than 5 levels; whether the design is between groups or within subjects; whether there are one or more than one dependent variable; whether the measurement level of the dependent variable is normal, ordinal, dichotomous, or nominal. The answers to these questions will lead us to select appropriate statistics (Morgan et al., 2020).
WEEK 4 DISCUSSION 2 D4.5.3. Provide an Example of a Study, Including the Variables, Level of Measurement, and Hypotheses, For Which a Researcher Could Appropriately Choose Two Different Statistics to Examine the Relations Between the Same Variables. Explain Your Answer. Hypothesis: Different levels of weekly study hours have an impact on exam scores. The dependent variable is the exam scores (continuous variable, measured in percentage points). The independent variable is time studying per week (measured in hours). One can make the time spent on studying per week an ordinal variable with 3 levels (low, medium, and high) based on certain thresholds in the hours spent on studying per week. In this case, one could utilize one-way ANOVA to compare the mean exam scores across the three levels of time spent on studying. This would allow us to determine if there is a significant difference in exam scores among the different levels of time spent on studying. Alternatively, one can make the time spent on studying per week an ordinal variable with 5 levels (very low, low, medium, high, and very high) based on certain thresholds in the hours spent on studying per week. In this case, one could employ a Pearson correlation or bivariate regression to assess the linear relationship between the ordered study hours variable and exam scores. This would help determine if there's a significant predictive relationship between the levels of study hours and exam scores. Both approaches provide valuable insights into how different levels of study hours relate to exam scores, but they utilize different statistical methods to do so. D4.5.6. What Statistic Would You Use If You Wanted to See If There Was a Difference Between Three Ethnic Groups on Math Achievement? Why?
WEEK 4 DISCUSSION 3 One-way ANOVA. There are 2 variables: one dependent variable and one independent variable. The independent variable has 3 levels. The design is between groups. The dependent variable is normal/sale. Morgan et al. (2020) recommends using one-way ANOVA in this case. D4.5.8. What Statistic Would You Use If You Had One Independent Variable, Geographic Location (North, South, East, West), and One Dependent Variable (Satisfaction with Living Environment, Yes or No)? Chi-Square. There are 2 variables: one dependent variable and one independent variable. The independent variable has 4 levels. The design is between groups. The dependent variable is dichotomous. Morgan et al. (2020) recommends using Chi-Square in this case. D4.5.9. What Statistic Would You Use If You Had Three Normally Distributed (Scale) Independent Variables (Weight of Participants, Age of Participants, and Height Of Participants), Plus One Dichotomous Independent Variable (Academic Track) and One Dependent Variable (Positive Self-Image), Which Is Normally Distributed? Explain your answer. Multiple regression. We have 5 variables: 1 dependent variable and 4 independent variables. The dependent variable is normal. Among the 4 independent variables, one is dichotomous and 3 are scale variables. Morgan et al. (2020) recommends using Multiple regression in this case.
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WEEK 4 DISCUSSION 4 References Charness, G., Gneezy, U., & Kuhn, M. A. (2012). Experimental methods: Between-subject and within-subject design.  Journal of Economic Behavior & Organization, 81 (1), 1- 8.  https://doi.org/10.1016/j.jebo.2011.08.009 Morgan, G. A.; Barrett, K. C.; Leech, N. L.; Gloeckner, G. W. (2020) IBM SPSS for introductory statistics: Use and interpretation, sixth edition . Taylor and Francis. Kindle Edition.