Poster Draft
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- READ-POSTERDRAFTASSIGNMENT.pdf
- PosterDraftGradingRubric.pdf
- PosterDraftSample.pdf
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- Read-WarnerIIChapter7.pdf
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HowtoDownloadHayesProcessMacroinSPSS.docx
PosterDraftAssignmentInstructions.docx
EDCO 745
Poster: Draft Assignment Instructions
Overview
Scholarly dissemination is essential for any doctoral level student. Posters are often a way to ease into scholarly communication. Building a poster is one of the ways scholars participate in the dissemination of knowledge.
Instructions
1. Your poster submission must have a central focus, as developed from the topic selected in Module 2, and that focus must be evident throughout the poster. Specifically, your introduction, analysis, and results must be focused on a set of research questions and/or hypotheses that are obvious in your theoretical diagram.
2. The focus must comprehensively place the problem/question in appropriate scholarly context (scholarly literature, theory, model, or genre).
3. All elements of method/techniques must be articulated in some way (i.e., graph or bullets).
4. Interpretation is explicitly linked to a theoretical framework or scholarly model.
5. Implications, consequences, and/or questions raised by the project must be thoroughly explored.
6. Limitations must be fully articulated.
7. Presentation of the material must be professional and compelling.
See Research Poster Examples for examples.
Note: Your assignment will be checked for originality via the Turnitin plagiarism tool.
PosterExample1.pptx
Engaging Introduction goes here
Present the background of the problem much like you did in Topic 4, Literature Review, Synthesis.
Present the need for further research much like you did in Topic 4, Literature Review, Conclusion.
Keep the presentation relatively concise, so that it fits within the allotted space.
Concise and Informative Title
Names of Contributors
Research Question & Hypotheses
Explain results here.
Describe the outcome of the tests. As with all scholarly writing, one begins with a narrative description of the results, then one presents the figures and/or tables that support the results. The write up for the results often tend to be very brief, as one is only presenting the outcome of the statistical test—the interpretation comes later in the Discussion section. If you are using the Week 7 assignment, you may copy and paste directly from it into this section.
The sample section describes the participants of the study from whom data were collected. Descriptions of the sample include how many total participants were in the study and listing of the relevant characteristics. For example, if the sample was 1,300 of which 704 were male and 596 were female, then one would describe the sample size and present a frequency table of the genders. One may use a bar chart to visually present the information; however, keep in mind the frequency values (the numbers for each category) must still be presented.
Talk about independent variables here.
Identify the variable (independent, predictor, antecedent, X) by name, identify the instrument used to measure the variable, and identify the variable level of measurement. For example,
In any research presentation, one must present the variables that are being examined. One must identify the variables, describe how the variables were measured, and describe the level of measurement for each variable. Identify the role of each variable in the study. A simple format for this was described in Week 2: Quiz: Pick Topic Assignment. If using the analysis from the Week 7 Regression Data Output and Write-up assignment, you should be able to copy and paste directly from the previous work.
For more information on variables, see Warner Chapter 1, p. 19
Measures
Provide the references used for this poster. This includes references used in the Introduction, as well as any references to tools used in the statistics (e.g., Hayes, 2018). Be sure correct APA (7th ed.) formatting is used.
RQ: To what extent, if any, does gender moderate the predictive effect between Stress (X) and Depression (Y)?
H0: Gender does not significantly moderate the predictive effect between Stress (X) and Depression (Y).
H1: Gender significantly moderates the predictive effect between Stress (X) and Depression (Y).
Sample
Introduction
Results
References
Procedure
Describe your procedure or method here.
Present an overview of the important steps one completes to prepare the data and test the hypothesis. Although the course spends time on ensuring the data are prepared and appropriate for hypothesis, such data preparation is not the main part of an analysis. As such, it is adequate to describe that data were retrieved and tests of assumptions were completed. The reader will expect that tests of assumptions met the assumptions unless you describe otherwise. For example, if there was a minor deviation of normality, then one would describe it and address how that deviation was handled. Otherwise, the adage of no news is good news is applicable. DO NOT PRESENT ANY DATA SCREENING FIGURES OR TABLES AS PART OF THE POSTER ASSIGNMENT.
With the data prepared, one should then describe how the data were used to test the hypothesis. The writeup for this section may be copied directly from the writeup for the Week 7 Regression Data Output and Write-up assignment with some minor editing.
Describe the statistical test you will complete. If using a moderation or mediation analysis, include a figure of the model being used.
Figure 1
Simple moderation model
Discuss the study and its findings here.
Limitations
What were your study’s limitations?
Future Research
What would you recommend future researchers do in light of your research findings?
Discussion
Leave yourself notes if you would like. (also, present this at research week!)
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PosterExample2.pptx
Shame is a complex emotion manifested in negative evaluations of oneself while also provoking acute, physiological changes in the body that can jeopardize one’s health (López-Castro et al., 2019). Because of its impact on people’s lives, there has been growing interest in the scholarly and psychological research about different conceptualizations of shame and various aspects of psychosocial functioning and well-being (Velotti et al., 2017). The conceptualization of shame occurred alongside efforts to understand its causes, associations, predictors, and relationships.
Most researchers treat shame as distinguishable, with an understanding that shame is a unique phenomenon that can lead to predictable human behaviors and behavioral outcomes (Shen, 2018). Shame, as experienced by different genders, has been a subject of interest, but there have been mixed findings about the possible relationship between variables.
Gender as a Possible Moderator
of the Predictive Effect Between Anger and Shame
Shannon Goodwin
Research Question and Hypotheses
The Experiential Shame Scale (ESS) has been validated among diverse populations. The 25 items (written in a 4-point Likert-type scale format) are answered through the self-reported perceptions of respondents. There are four areas of characterological shame (personal habits, manners, self-appraisal, and abilities). There are three areas of behavioral shame (doing or saying something wrong or stupid and competitive failures). Bodily shame pertains to feeling ashamed of one’s body or any of one’s body parts. The questions also encompass experiential, cognitive, and behavioral domains (Vizin et al., 2016).
PROMIS-ANGER is a 5-point Likert, with item ratings summed for a total score for the five-item scale ranging from 5 to 25. Higher scores are associated with higher levels of anger severity (Kaman et al., 2022).
There were 1304 people who provided answers to the question of gender. Fifty-four percent of the respondents selected male (n = 704) and 45.7% selected female (n = 596). Four respondents chose the other category.
Gender is a categorical variable that involves a nominal level of measurement.
Sexual activity is a categorical variable that involves an ordinal level of measurement.
Shame is a continuous variable that involves an interval level of measurement, as assessed by the Experience of Shame Scale (ESS) (Andrews et al., 2002).
Anger is a continuous variable that involves an interval level of measurement, as assessed by the PROMISANG (an Anger Scale) (Shepherd & Evans, 2020).
Variables
Shame is a dependent variable measured by ESS with a continuous, interval level of measurement.
Gender is an independent, dichotomous variable.
Gender contains two separate and distinct group where membership data in one group (i.e., females) cannot contribute to membership data in the other group (i.e., males).
A boxplot was generated for the dependent variable ESS for both groups of independent variable and there were no significant outliers for males or females.
Normality is observed with histograms of ESS distribution for males and females.
Levene’s test for ESS was not statistically significant, F(1,1298) = .40, p = .53, indicates homogeneity of variances.
Boxplot
Research problem: It is not known to what extent, if any, the relationship between anger and shame is moderated by gender.
RQ1: To what extent, if any, does gender moderate the predictive relationship between anger and shame?
Ho1: Gender does not significantly moderate the predictive relationship between anger and shame.
Ha1: Gender significantly moderates the predictive relationship between anger and shame.
Sample
Introduction
Instruments
References
Data Assumptions
Because the results of the simple moderator analysis revealed that gender does not significantly moderate the predictive effect between Anger (X) and Shame (X), there is a justification for failing to reject the null hypothesis. While the results show a significant predictive effect between the variables (Anger and Shame), the extent of such an effect is not moderated by a person’s gender. Limitations to these findings include a secondary dataset that was impossible to further assess and that did not allow one to draw conclusions about the context, possible geographical influences, and other demographic factors that might have informed conclusions about the results of the study. Future research could include consideration of the other demographic variables, as well as the relationships of shame to particular phenomenon suspected to somehow relate to the construct.
What is also known is that shame, as experienced by different genders, has been a subject of interest, but there have been mixed findings about the possible relationship between variables. According to Dolezal and Lyons (2017), when individuals are stigmatized or marginalized for reasons out of their control (such as race or gender), shame may become a chronic experience because of persistent threats and feelings of social devaluation. This study did not account for the possible influence of stigmatization, marginalization, the persistence of threats, or other social experiences that might be more or less gender or demographically influenced or otherwise related to anger and shame. Additional research could follow these interests.
Given the historical discrimination of women as a social group, it may be possible that women experience shame at greater intensity, frequency, and/or duration than men. This study did not account for intensity, frequency, or duration, which could also be the subjects in future studies.
Discussion
Model
Gender (W)
Anger (X) PROMIS-ANG
Shame
(Y) ESS
Data screening and assumption tests were conducted to examine whether the variables, Shame (interval level of measurement) and Gender (categorical), as provided in the EDCO-745 course dataset, agree with the null hypothesis, which states there is no significant difference in Shame between the male and female gender.
In additions, a simple moderator analysis was performed using Hayes’ (2018) Model 1 using three variables, gender, anxiety, and shame. All data were derived from the EDCO 745 course dataset and were screened. Assumptions for multiple regression were tested. Tests of assumptions demonstrated that data were appropriate for running a moderator analysis. The moderator analysis occurred to test the null hypothesis using Hayes’ (2018) PROCESS v4.0.
Procedure
Cundill, B., & Alexander, N. D. (2015).Sample size calculations for skewed distributions. BMC Medical Research Methodology, 15, 28-33. https://doi.org/10.1186/s12874-015-0023-0
Dolezal, L., & Lyons, B. (2017). Health-related shame: An affective determinant of
health? Medical Humanities, 43(4), 257–263. https://doi.org/10.1136/medhum-2017-011186
Eslinger, P., Anders, S., Ballarini, T., Boutros, S., Krach, S., Mayer, A., Moll, J., Newton, T., Schroeter, M. L., Oliveira-Souza, R., Raber, J., Sullivan, G. B., Swain, J., Lowe, L., & Zahn, R. (2021). The neuroscience of social feelings: Mechanisms of adaptive social functioning. Neuroscience and Biobehavioral Reviews, 128, 592–620. https://doi.org/10.1016/j.neubiorev.2021.05.028
Kaman, A., Otto, C., & Devine, J. (2022). Assessing anger and irritability in children: psychometric evaluation and normative data for the German version of the PROMIS® Parent Proxy Anger Scale. Qualitative Life Research, 31, 831–839. https://doi.org/10.1007/s11136-021-03001-1
Shanks, D. R. (2017). Regressive research: The pitfalls of post hoc data selection in the study of unconscious mental processes. Psychonomic Bulletin & Review, 24(5), 752–775. https://doi.org/10.3758/s13423-016-1170-y
Shen, L. (2018) The evolution of shame and guilt. PLoS One, 13(7). Article e0199448.
https://doi.org/10.1371/journal.pone.0199448
Velotti, P., Garofalo, C., Bottazzi, F., & Caretti, V. (2017) Faces of shame: Implications for self-esteem, emotion regulation, aggression, and well-being. The Journal of Psychology, 151(2), 171-184, DOI: 10.1080/00223980.2016.1248809
Vizin, G., Urbán, R., & Unoka, Z. (2016). Shame, trauma, temperament, and psychopathology:
Construct validity of the Experience of Shame Scale. Psychiatry Research, 246, 62-69.
https://doi.org/10.1016/j.psychres.2016.09.017.
Warner, R. (2020). Applied statistics I (3rd ed.). SAGE Publications.
Results include a significant predictive effect between Anger and Shame, t = 25.1366, p < .0000, b = .0816. For the interaction between Gender and Anxiety on Shame, there was no statistical significance, t = -1.1031, p = .2702, b = .0071.
Model Summary for Moderator Analysis
R R-sq MSE F df1 df2 p
.5730 .3284 .3553 211.8591 3.0000 1300.0000 .0000
Model Coefficients
Coeff SE t p LLCI ULCI
Constant 2.5584 .0165 154.7494 .0000 2.5260 2.5908
PROMISAn .0816 .0032 25.1366 .0000 .0753 .0880
Gender .0478 .0328 1.4581 .1450 -.0165 .1121
Int_1 -.0071 .0064 -1.1031 .2702 -.0197 -.0055
Results
Leave yourself notes if you would like. (also, present this at research week!)
1
GENDER
Percent
Male Female Other 0.54 0.45700000000000002 3.0000000000000001E-3
