BUSI820DB4

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Feb 20, 2024

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School of Business, Liberty University Faizan Malik Week 4 Discussion Assignment Author Note: Faizan Malik I have no known conflict of interest to disclose. Correspondence concerning this article should be addressed to Faizan Malik: Fmalik@Liberty.edu D4.5.1 Compare and contrast a between-groups design and a within-subjects design. D4.5.1a. In a between-groups design, researchers will divide participants into separate groups, who are then exposed to a single but unique condition of the variable tested, whereas in a within-subjects design where each participant experiences all levels of the independent variable, acting as their control (Morgan et al., 2020). A between-groups design carries the benefit of random assignment, however, individual differences among participants can influence results. Within-subjects designs can reduce such variability, however, can result in order effects which can require methods such as counterbalancing to mitigate. As explained by Depuy and Berger (2014), “When subjects are consecutively given more than one treatment in an experiment, including for crossover design, earlier treatments may affect the results observed during later treatments. To counteract this, the treatment order may be varied between different subjects, or counterbalanced (Depuy & Berger, 2014). D4.5.2. What information about variables, levels, and design should you keep in mind to choose an appropriate statistic? D4.5.2.a. When conducting a study, researchers must first determine the types of variables being used, as this will influence other factors within the study such as the statistical methods (Morgan et al, 2020). Researchers should then evaluate the number of levels that exist for the independent variable, as this will determine the appropriate statistical test for comparing the means or effects across these levels. If a variable carries more than two levels, methods such as Analysis of Variance (ANOVA) may be required (Morgan et al., 2020). Experimental design is also vital, including factors such as sample size, with researchers needing to identify how variables and participants interact, such as the use of between-group and within-subject design. Researchers may also look to consider the nature of the relationship between variables guides the choice between association-based tests or difference-based tests (Morgan et al., 2020)

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. D4.5.3.a. Introduction: This study explores the ever-evolving landscape of telemedicine that has led to the emergence of various business models, each with unique strategies for service provision and revenue generation and how ensuring the sustainability of these telemedicine business models is crucial for their long-term success and contribution to accessible healthcare. Hypothesis: The hypothesis for the between-groups design is as follows: "Telemedicine platforms operating under the Hybrid Model will exhibit higher sustainability scores compared to those under the Subscription-Based Model and Fee-for-Service Model." For the within-subjects design, the hypothesis would focus on changes in sustainability scores over time within the same telemedicine platform: "Telemedicine platforms transitioning from a Fee-for-Service Model to a Hybrid Model will experience a significant increase in sustainability scores after the transition." Variables and Level of Measurement: The independent variable in this study is the "Telemedicine Business Model," which comprises three levels: (1) Subscription-Based Model, (2) Fee-for-Service Model, and (3) Hybrid Model. The dependent variable, "Sustainability Score," will be measured on an interval scale, representing a composite score based on key performance indicators related to financial viability, patient retention, and market expansion. The choice of statistical methods aligns with the research objectives and the study design. In the between-groups design, the focus is on comparing multiple telemedicine business models operating independently. Thus, the researchers will aim to determine if there are significant differences in sustainability scores among these distinct business models, warranting the use of between-group comparative statistics. On the other hand, the within-subjects design investigates the impact of a specific change within the same telemedicine platform, such as transitioning from one business model to another. The researcher seeks to identify if the transition to a Hybrid Model significantly influences the sustainability scores within the platform, making within-subjects comparative statistics appropriate for this longitudinal analysis. 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? D4.5.6.a.To evaluate the difference between three ethnic groups on a math achievement test, researchers can use a statistical method known as Analysis of Variance (ANOVA), as it is specifically designed to compare the means of three or more groups. ANOVA would allow researchers to assess the variance between the means of the three ethnic groups, or the between-group variance, and compare it to the variance within each group, or the within-group variance (Morgan et al., 2020). Buckless and Ravenscroft (1990) explain

the additional benefits of ANOVA including efficient comparison of multiple groups, control of errors, clear interpretation of results, and results that are widely accepted and understood (Buckless & Ravenscroft, 1990). 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 the living environment, Yes or No)? D4.5.8.a Given that the independent variable (geographic location) is nominal and has four unordered levels (North, South, East, West) and the dependent variable (satisfaction with the living environment) is dichotomous with two levels (yes and no), researchers can utilize a Chi-Square test. Morgan et al. (2020) explain that the Chi-Square test compares observed frequencies of varying categories along with the expected frequencies, and assesses the difference to determine if there is a statistically significant association between the variables (Morgan et al., 2020). 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? D4.5.9.a Given the study contains three normally distributed independent variables (weight of participants, age of participants, and height of participants), one dichotomous independent variable (academic track), and one normally distributed dependent variable (positive self-image), multiple regression analysis would be appropriate. Multiple regressions allow researchers to examine the relationship between multiple independent variables and a single dependent variable and assess the unique contribution of each independent variable to the variation in the dependent variable while controlling for the effects of other independent variables (Morgan et al., 2020). References Buckless, F. A., & Ravenscroft, S. P. (1990). Contrast coding: A refinement of ANOVA in behavioral analysis. Accounting Review , 933-945. DePuy, V., & Berger, V. W. (2014). Counterbalancing. Wiley StatsRef: Statistics Reference Online . Morgan, G., Leech, N., Gloeckner, G., Barrett, K. (2020). IBM SPSS for Introductory Statistics (5th Ed.). New York, NY

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