Read Morgan Leech Gloeckner Barrett Chapters 5 And 6

Read Morgan Leech Gloeckner Barrett Chapter 5 And Chapter 6watc

Read: Morgan, Leech, Gloeckner, & Barrett: Chapter 5 and Chapter 6 Watch: Internal Consistency DISCUSSION ASSIGNMENT INSTRUCTIONS The student will complete Integrating Faith and Learning discussion. In the thread for each short-answer discussion the student will post short answers to the prompted questions. The answers must demonstrate course-related knowledge and support their assertions with scholarly citations in the latest APA format. Minimum word count for all short answers cumulatively is 200 words. The minimum word count for Integrating Faith and Learning discussion is 600 words.

For each thread the student must include a title block with your name, class title, date, and the discussion forum number; write the question number and the question title as a level one heading (e.g. D1.1 Variables) and then provide your response; use Level Two headings for multi part questions (e.g. D1.1 & D1.1.a, D1.1.b, etc.), and include a reference section. The student must then post 1 reply to another student’s post. The reply must summarize the student’s findings and indicate areas of agreement, disagreement, and improvement.

It must be supported with scholarly citations in the latest APA format and corresponding list of references. The minimum word count for Integrating Faith and Learning discussion reply is 250 words. Respond to the following short answer questions from Chapter 5 of the Morgan, Leech, Gloeckner, & Barrett textbook: D4.5.1 Compare and contrast a between-groups design and a within-subjects design. D4.5.2. What information about variables, levels, and design should you keep in mind in order to choose an appropriate statistic? 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. D4.5.6. What statistic would you use if you wanted to see if there was a difference between three ethnic groups on math achievement? Why? D4.5.8. 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)? 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.

Paper For Above instruction

Completing research studies in psychology necessitates a precise understanding of study designs and statistical methods to accurately interpret data and draw meaningful conclusions. Among the fundamental design structures are between-groups and within-subjects designs, each serving different research purposes and bearing specific advantages and limitations. Additionally, selecting appropriate statistical analyses depends critically on the nature of variables involved, their levels of measurement, and the research hypotheses formulated.

Comparison of Between-Groups and Within-Subjects Designs

The between-groups design involves comparing different groups of participants subjected to varied experimental conditions. In this design, each participant is assigned to only one experimental condition, and the data collected reflect differences across these independent groups. This approach allows researchers to assess the effect of the independent variable on the dependent variable across distinct populations, making it suitable for studies where individual performance or characteristics are expected to differ based on group membership (Morgan et al., 2019).

In contrast, the within-subjects design involves measuring the same participants across multiple conditions or times, effectively serving as their own control. This approach reduces variability caused by individual differences, thereby increasing statistical power and sensitivity (Morgan et al., 2019). For instance, in a study examining the effect of sleep deprivation on cognitive performance, the same participants could be tested with and without sleep deprivation, providing direct comparisons within individuals.

Each design has advantages: between-groups designs reduce potential order effects and carry-over influences but require larger sample sizes to account for variability between groups. Within-subjects designs enhance statistical power and control for individual differences but can be susceptible to order effects, which necessitate counterbalancing strategies (Field, 2018).

Choosing Appropriate Statistics Based on Variables, Levels, and Design

When selecting statistical tests, it is crucial to consider the types of variables involved. Nominal variables, such as ethnicity or gender, require non-parametric tests like chi-square, whereas ordinal and interval/ratio variables may qualify for parametric tests if they meet certain assumptions of normality and homogeneity of variance (Tabachnick & Fidell, 2019?).

The levels of variables—such as whether a variable is dichotomous or multicategory—modify the choice of tests. For example, comparing two groups on a continuous outcome would typically involve an independent samples t-test, while comparisons across more than two groups might require ANOVA. The design also influences test selection; for between-groups independent designs, t-tests or ANOVAs are appropriate, whereas within-subjects or repeated-measures designs often use paired t-tests or repeated-measures ANOVA.

Alternative Statistical Approaches for the Same Variables

Consider a hypothetical study examining the relationship between hours of study (continuous variable) and exam scores (continuous variable). One could use Pearson’s correlation coefficient to assess the linear relationship between these two variables. Alternatively, if examining whether hours of study predict categorical success (pass/fail), logistic regression would be suitable. Both tests analyze the same variables but serve different analytical purposes—correlation quantifies the association strength, while regression assesses predictive power (Cohen et al., 2018).

Analyzing Differences Across Ethnic Groups on Math Achievement

To determine whether three ethnic groups differ significantly on math achievement scores, a one-way ANOVA would be appropriate. This test compares the means across more than two independent groups, testing the null hypothesis that group means are equal. It’s suitable because it accounts for variability both within and between groups and can handle continuous dependent variables like test scores (Field, 2018).

Analyzing the Effect of Geographic Location on Satisfaction with Living Environment

If the independent variable is geographic location with four categories (North, South, East, West) and the dependent variable is dichotomous (Yes/No for satisfaction), a chi-square test of independence would be appropriate. This non-parametric test evaluates the association between two categorical variables, identifying whether location influences living satisfaction (Morgan et al., 2019).

Regression Analysis with Multiple Predictors and a Dichotomous Variable

When evaluating relationships involving multiple independent variables—such as weight, age, height, and a dichotomous academic track—and a normally distributed dependent variable (positive self-image), multiple regression analysis is suitable. Multiple regression can incorporate continuous predictors and a categorical predictor (via dummy coding), providing a comprehensive understanding of their combined and individual effects on the outcome (Cohen et al., 2018). Given the assumptions of normality and linearity, this method offers robust insights into the relative importance of each predictor.

Conclusion

Understanding the differences between study designs and aligning the choice of statistical tests with variable types and research questions are fundamental to rigorous psychological research. Selecting appropriate analytical methods facilitates valid interpretations, ultimately advancing scientific knowledge and informing practice. Careful consideration of design and statistical options ensures that data analysis accurately reflects underlying phenomena, supporting evidence-based conclusions in psychology.

References

• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2018). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Routledge.
• Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). SAGE Publications.
• Morgan, G. Leech, Gloeckner, & Barrett. (2019). Research methods in applied settings: An integrated approach to design and analysis. Routledge.
• Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson Education.