While each paper focuses on a specific part of your manuscript, all papers have


While each paper focuses on a specific part of your manuscript, all papers have supporting materials to help you craft your paper. Each paper includes paper instructions (which are long and detailed but worth reading!), a grading rubric, a checklist, and an example paper from a prior semester. If you look over ALL FOUR items, your chances of getting a good grade will improve dramatically!
Paper III Instructions (Filters, Summer 2022)
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Paper III Grading Rubric
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Paper III Checklist
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Paper III Example Paper #1 – Counterfactual Thinking
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Paper III Example Paper #2 – Facebook Apologies
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After some feedback on the direction of study two, it looks like we are going with the idea of including Twitter User Gender as our new independent variable in study two for this methods section. That is, in some conditions participants will see the photo of a female Twitter User (The same “Katie” photos from study one or a new male Twitter User. To avoid potential issues with regard to the user’s gendered name, we will refer to both the female and male Twitter user as “Chris”, a gender-neutral name that will make it easier to write dependent variable questions that refer to the Twitter user. I’ll refer to this independent variable as the Twitter User Gender.
We will continue to focus on the same Filter manipulation from study one for our first independent variable, but we will only keep the “Filtered” versus “Unfiltered” levels of that independent variable (We will drop the “Control” condition. The filtered and unfiltered photo conditions provide a better contrast, so keeping these conditions seems more informative). I’ll call this the Filter condition.
Consider our new independent variable again (Twitter User Gender). Here we will include the photo of a male or a female Twitter user. That is, after completing the informed consent form (an electronic version on canvas), students will read the Twitter page where “Chris” will note that they just completed a workout and are ready for their “close-up”.
1). For the Female condition, participants will see the same photos we used in study one (pictures of “Katie”, though now called “Chris”).
2). For the Male condition, participants will see the new photos of a male “Chris”
This gives us a 2 (Filter Condition: Filtered versus Unfiltered) X 2 (Twitter User Gender: Male versus Female user) factorial design. That is, there will be four conditions:
Condition #1 – Female Photos and Filtered (Both photos are the same)
Condition #2 – Female Photos and Unfiltered (Sarah provides a different second photo)
Condition #3 – Male Photos and Filtered (Both photos are the same)
Condition #4 – Male Photos and Unfiltered (Sarah provides a different second photo)
As you begin writing your study two literature review for Paper III, keep this new “Twitter User Gender” independent variable in mind. You’ll need to find prior research that looks at gender and use that literature to help support or justify your study predictions. Good keywords for PsycInfo might be “gender”, “body image”, “visual feedback”, “stereotyped attitudes”, “photographs”, “body dissatisfaction”, and the like.
For your hypothesis, remember that you will need to focus on both main effects (the effect of each independent variable on its own) and an interaction (the influence of both independent variables interacting together). Each of your scaled dependent variables—like “Chris seems insecure about their appearance” and “Sarah seems supportive of Chris”, both on 1 to 6 agreement scales—will need its own main effect and interaction hypotheses. I’ll give you an example below, but you will need to think about the hypothesis for your second dependent variable yourself.
1). Main Effect, Filter Condition (Filtered versus Unfiltered). DV = “Insecure”
In general, we predicted that if participants saw a Twitter user’s friend post an unfiltered (and less flattering) photo of the user that differed from a filtered photo the user originally posted (i.e. the unfiltered condition), then they would more strongly agree that the original Twitter user seemed insecure about their, at least when compared to participants who saw the friend repost the original filtered photo (i.e. the filtered condition).
(Note #1: This prediction is identical to our study one prediction for the Filter condition. The only thing that differs is the lack of the “control” condition. Also note that this prediction ONLY looks at the independent variable “Filter Condition” and ignores the Twitter user’s gender)
(Note #2: This prediction only looks at “Insecure”, but you’ll also need to look at a second dependent variable (like dissatisfaction, or the supportive nature of the Twitter user’s friend Sarah)
2). Main Effect, Twitter User Gender (Male versus Female). DV = “Insecure”
We also predicted that participants could rate the Twitter user as more insecure in the female photo condition than in the male photo condition


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