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.
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at random from the Halifax School District database.
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B
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t=
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- Individuals with strong religious beliefs often turn to their faith to cope with stressful life events. Relying on God's love and caring is referred to as positive religious coping. Andrea Phelps and her colleagues studied the relationship between positive religious coping and the type of care received by terminally ill cancer patients. Most of the patients in the study were Christian. Suppose another researcher conducts a similar study, but uses a sample of patients whose religious traditions are more varied than the patients in the Phelps study. She samples 300 terminally ill patients to see whether their practicing a religion is related to how likely they are to have a living will. The following table shows the results of the study. Living Will(Yes). Living Will(No) Practices Religion(Yes). 90 69 Practices Religion(No) 90 51 Find the chi-square test…arrow_forwardStudy 2: Pill Appearance and Perceived Pain. Does the shape or color of a pain pill influence its effectiveness? Although logically it shouldn’t, whether we believe a drug will work does have a powerful effect on our perceptions (e.g., placebo effect). In this experiment, 4 groups of adult patients were given the same amount of Advil after dental surgery for pain relief, but the color and shape of the pill varied. Researchers hypothesized that an unusual shape or color would lead people to believe the pills were new and special and thus would expect them to be more effective than common round, white pills. Researchers also wanted to know if there is an interaction between shape and color Data Labels ShapePill {1=Round; 2=Diamond} ColorPill {1=White; 2=BlueGreen} Gender {0=Woman; 1=Man; 2=Nonbinary person) Descriptions of the Variables and Descriptive Statistics: Referring to the JASP output, and using sentences, present the descriptive statistics of each group: for example:…arrow_forwardComputer-mediated communication (CMC) is a form of interaction that heavily involves technology (e.g., instant messaging, email). A study was conducted to compare relational intimacy in people interacting via CMC to people meeting face-to-face (FTF).Participants were 48 undergraduate students, of which half were randomly assigned to the CMC group and half assigned to the FTF group. The variable of interest, relational intimacy score, was measured (on a 7-point scale) for each participant after each of three different meeting sessions. Summary statistics for the first meeting session are given here. The researchers hypothesized that, after the first meeting, the mean relational intimacy score for participants in the CMC group would be lower than the mean relational intimacy score for participants in the FTF group. Test the researchers’ hypothesis using α = .10. CMC FTF…arrow_forward
- Computer-mediated communication (CMC) is a form of interaction that heavily involves technology (e.g., instant messaging, email). A study was conducted to compare relational intimacy in people interacting via CMC to people meeting face-to-face (FTF). Participants were 48 undergraduate students, of which half were randomly assigned to the CMC group and half assigned to the FTF group. The variable of interest, relational intimacy score, was measured (on a 7-point scale) for each participant after each of three different meeting sessions. Summary statistics for the first meeting session are given here. The researchers hypothesized that, after the first meeting, the mean relational intimacy score for participants in the CMC group would be lower than the mean relational intimacy score for participants in the FTF group. Test the researchers’ hypothesis using α = .10. CMC FTF Number…arrow_forwardComputer-mediated communication (CMC) is a form of interaction that heavily involves technology (e.g., instant messaging, email). A study was conducted to compare relational intimacy in people interacting via CMC to people meeting face-to-face (FTF). Participants were 48 undergraduate students, of which half were randomly assigned to the CMC group and half assigned to the FTF group. The variable of interest, relational intimacy score, was measured (on a 7-point scale) for each participant after each of three different meeting sessions. Summary statistics for the first meeting session are given here. The researchers hypothesized that, after the first meeting, the mean relational intimacy score for participants in the CMC group would be lower than the mean relational intimacy score for participants in the FTF group. Test the researchers’ hypothesis using α = .10. CMC FTF Number…arrow_forwardA researcher is interested in exploring the relationship between calcium intake and weight loss. Two different groups, each with 2828 dieters, are chosen for the study. Group A is required to follow a specific diet and exercise regimen, and also take a 500500-mg supplement of calcium each day. Group B is required to follow the same diet and exercise regimen, but with no supplemental calcium. After six months on the program, the members of Group A had lost a mean of 15.215.2 pounds with a standard deviation of 2.52.5 pounds. The members of Group B had lost a mean of 12.312.3 pounds with a standard deviation of 1.91.9 pounds during the same time period. Assume that the population variances are not the same. Construct a 95%95% confidence interval to estimate the true difference between the mean amounts of weight lost by dieters who supplement with calcium and those who do not. Let Population 1 be the amount of weight lost by Group A, who took a 500500-mg supplement of calcium each day,…arrow_forward
- Seattle Grace Medical Center. As part of a long-term study of individuals 65 years of age or older, doctors at the Seattle Grace Medical Center in Washington state investigated the relationship between state of residence and depression. A sample of 60 individuals, all in reasonably good health, was selected; 20 individuals were residents of Texas, 20 were residents of Washington state, and 20 were residents of South Carolina. Each of the individuals sampled was given a standardized test to measure depression. The data collected follow; higher test scores indicate higher levels of depression. These data are contained in the attached data file SeattleGrace1. A second part of the study considered the relationship between state of residence and depression for individuals 65 years of age or older who had a chronic health condition such as diabetes and/or high blood pressure. A sample of 60 individuals with such conditions was identified. Again, 20 were residents of Texas, 20 were residents…arrow_forwardSeattle Grace Medical Center. As part of a long-term study of individuals 65 years of age or older, doctors at the Seattle Grace Medical Center in Washington state investigated the relationship between state of residence and depression. A sample of 60 individuals, all in reasonably good health, was selected; 20 individuals were residents of Texas, 20 were residents of Washington state, and 20 were residents of South Carolina. Each of the individuals sampled was given a standardized test to measure depression. The data collected follow; higher test scores indicate higher levels of depression. These data are contained in the attached data file SeattleGrace1. A second part of the study considered the relationship between state of residence and depression for individuals 65 years of age or older who had a chronic health condition such as diabetes and/or high blood pressure. A sample of 60 individuals with such conditions was identified. Again, 20 were residents of Texas, 20 were residents…arrow_forwardSeattle Grace Medical Center. As part of a long-term study of individuals 65 years of age or older, doctors at the Seattle Grace Medical Center in Washington state investigated the relationship between state of residence and depression. A sample of 60 individuals, all in reasonably good health, was selected; 20 individuals were residents of Texas, 20 were residents of Washington state, and 20 were residents of South Carolina. Each of the individuals sampled was given a standardized test to measure depression. The data collected follow; higher test scores indicate higher levels of depression. These data are contained in the attached data file SeattleGrace1. A second part of the study considered the relationship between state of residence and depression for individuals 65 years of age or older who had a chronic health condition such as diabetes and/or high blood pressure. A sample of 60 individuals with such conditions was identified. Again, 20 were residents of Texas, 20 were residents…arrow_forward
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