Quantitative Methods Proposal

Quantitative Methods Proposal

Title Provide the working title of your study. It is helpful if this is the same title that you submit for publication of your final manuscript, but it is not a requirement. Impact of Fictionalized Television LGBTQ Portrayals on Appalachian LGBTQ Viewers Authors The author who submits the preregistration is the recipient of the award money and must also be an author of the published manuscript. Additional authors may be added or removed at any time. Don Lowe, University of Kentucky Dr. Mina Tsay, Boston University Rationale Sell us the study. Justify why we should care about it and why there is a need for it in the first place. Due to a serious lack of adequate role models for members of the LGBTQ community in Appalachian areas, a study of how portrayals of LGBTQ community members on fictionalized television programming fill in the gaps is essential. This study will look at how members of the LGBTQ community view, process, and interact with portrayals of LGBTQ community members on fictionalized television. It will also look at how these viewers use portrayals to negotiate their own LGBTQ identities. Hypotheses Provide one or multiple specific and testable hypotheses. Please state if the hypotheses are directional or non-directional. If directional, state the direction. A predicted effect is also appropriate here. Include rationale and theoretical frameworks used to make the hypotheses. To guide this study and frame the hypotheses, the authors used exemplification theory in an attempt to explain how portrayals are more effective in information transfer than base rate information. The authors believe exemplification theory can be extended from its traditional use in televised news exemplars to include fictionalized television programming exemplars as well. Directional hypotheses were developed to test relationships between audiences and programming. Hypothesis 1: A positive relationship will exist between exposure to LGBTQ characters on fictionalized television and identity negotiation of LGBTQ community members. Hypothesis 2: The positive relationship between exposure to LGBTQ characters on fictionalized television and identity negotiation of LGBTQ community members will be stronger for Appalachian LGBTQ community members than for LGBTQ community members from urban areas. Data collection procedures Please describe the process by which you will collect your data. If you are using human subjects, this should include the population from which you obtain subjects, recruitment efforts, payment for participation, how subjects will be selected for eligibility from the initial pool (e.g. inclusion and exclusion rules), and your study timeline. For studies that don’t include human subjects, include information about how you will collect samples, duration of data gathering efforts, source or location of samples, or batch numbers you will use. Population University students from the University of Kentucky who come from counties in Kentucky considered to be part of Appalachia— Adair, Bath, Bell, Boyd, Breathitt, Carter, Casey, Clark, Clay, Clinton, Cumberland, Edmonson, Elliott, Estill, Fleming, Floyd, Garrard, Green, Greenup, Harlan, Hart, Jackson, Johnson, Knott, Knox, Laurel, Lawrence, Lee, Leslie, Letcher, Lewis, Lincoln, McCreary, Madison, Magoffin, Martin, Menifee, Metcalfe, Monroe, Montgomery, Morgan, Nicholas, Owsley, Perry, Pike, Powell, Pulaski, Robertson, Rockcastle, Rowan, Russell, Wayne, Whitley, and Wolfe—determined by The Appalachian Regional Commission and university students from Boston University who come from any county or state outside Appalachia—once again determined by The Appalachian Regional Commission. Recruitment Initial recruitment for the University of Kentucky students will be inclusion in the SONA data collections process used by the Department of Communication. Initial recruitment for Boston University students will be conducted by Dr. Mina Tsay, Assistant Professor of Communication. Inclusion/exclusion Subjects will then be divided into two groups based on hometown—Appalachian or Non-Appalachian. For both groups, inclusion will be based on membership to any of the LGBTQ communities. This includes those subjects who identify as lesbian, gay, bisexual, transgender, queer/questioning, intersex, and asexual/a gender/a romantic. The initialism has been shortened for the sake of limited publication space but will include anyone identifying in any of these categories. This study will begin recruitment in January 2018 and following sample selection and finalization, experiments will be conducted in March 2018. Data analysis and reporting of results will follow hopefully, by May 2018. Sample size/rationale Describe the sample size of your study. How many units will be analyzed in the study? This could be the number of people, birds, classrooms, plots, interactions, or countries included. If the units are not individuals, then describe the size requirements for each unit. If you are using a clustered or multilevel design, how many units are you collecting at each level of the analysis? What is the inclusion and exclusion criteria? This could include a power analysis or an arbitrary constraint such as time, money, or personnel. With a sample size of (N=200; 100 in each group), this study size is large enough to achieve a power score of .80 (Cohen, 1988). This is important as confidence levels, margin of error, and effect sizes, as well as power, are affected by the sample size. Limited time and resources always impact sample and this study is no different. University students produce results that are not generalizable and are often referred to as convenience samples but provide valid insights nonetheless. Manipulated variables Describe all variables you plan to manipulate and the levels or treatment arms of each variable. For observational studies and meta-analyses, simply state that this is not applicable. Not applicable Measured variables Describe each variable that you will measure. This will include outcome measures, as well as any predictors or covariates that you will measure. If they are from existing scale, please cite them. If they are original, please include the items. You do not need to include any variables that you plan on collecting if they are not going to be included in the confirmatory analyses of this study. Make sure to comment on what level each variable will be measured (e.g., nominal or interval). HERE FIND A SCALE TO BE USED OR CONDUC EFA OR CFA using attached Examples Indices If any measurements are going to be combined into an index (or even a mean), what measures will you use and how will they be combined? Include either a formula or a precise description of your method. If your are using a more complicated statistical method to combine measures (e.g. a factor analysis), you can note that here but describe the exact method in the analysis plan section. Study type Observational Study design Describe your study design. Examples include two-group, factorial, randomized block, and repeated measures. Is it a between (unpaired), within-subject (paired), or mixed design? Describe any counterbalancing required. Typical study designs for observation studies include cohort, cross sectional, longitudinal, and case-control studies. A cross sectional design was chosen for comparison of the two populations. The study will compare many different variables but focus on hometowns and the relationship to identity negotiation. REWRITE THIS A Web-based questionnaire was employed in the current study because research suggests that participants tend to be more open and honest in their responses on Web-based questionnaires than on paper-and-pencil questionnaires, especially when the questionnaire contains items measuring sensitive or socially taboo vari- ables like attitudes toward sexualities (Wright, Aquilino, & Supple, 1998). Following face-to-face recruitment announcements, students were sent an email that included a link to the online survey. Once participants provided electronic consent, they navigated to the online questionnaire, which took approximately 20 minutes to complete. Following completion, participants were provided with a 10-digit code that they redeemed for extra credit in their respective classes. Randomization If you are doing a randomized study, how will you randomize, and at what level? Statistical models What statistical model will you use to test each hypothesis and why? Please include the type of model (e.g. ANOVA, multiple regression, SEM, etc) and the specification of the model (this includes each variable that will be included as predictors, outcomes, or covariates). Please specify any interactions that will be tested and remember that any test not included here must be noted as an exploratory test in your final article. Transformations If you plan on transforming, centering, recoding the data, or will require a coding scheme for categorical variables, please describe that process. Follow-up analyses If not specified previously, will you be conducting any confirmatory analyses to follow up on effects in your statistical model, such as subgroup analyses, pairwise or complex contrasts, or follow-up tests from interactions? Remember that any analyses not specified in this research plan must be noted as exploratory. Inference criteria What criteria will you use to make inferences? Please describe the information you’ll use (e.g. specify the p-values, Bayes factors, specific model fit indices), as well as cut-off criterion, where appropriate. Will you be using one or two tailed tests for each of your analyses? If you are comparing multiple conditions or testing multiple hypotheses, will you account for this? Data exclusion How will you determine which data points or samples (if any) to exclude from your analyses? How will outliers be handled? Missing data How will you deal with incomplete or missing data? Exploratory analysis If you plan to explore your data set to look for unexpected differences or relationships, you may describe those tests here. An exploratory test is any test where a prediction is not made up front, or there are multiple possible tests that you are going to use. 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