Assignent Solutionnurs

As I briefly mentioned last week, Twitter User Gender is our new independent variable in Study Two. 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 (via Qualtrics, forthcoming), participants 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) 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, then they would more strongly agree that the original Twitter user seemed insecure, at least when compared to participants who saw the friend repost the original filtered photo.
(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 for Paper IV.)
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.
(Note#1: The reasoning behind this prediction is based on research showing more body dissatisfaction when it comes to females compared to males. But other research suggests that it might be based on the type of body dissatisfaction. Females tend to want “thinness” while males want “muscularity”, and they may feel less secure based on their own gender ideals. If you find studies that support a different “male versus female” outcome in terms of insecurity, then feel free to alter this prediction! In fact, there may be no differences. That is, participants may see no difference in insecurity between a female and male Twitter user.)
(Note #2: You will write your second literature review with this prediction in mind – find support to back it up! But again here, this prediction ONLY looks at the independent variable “Twitter User Gender”. This main effect ignores the idea of a filter. Again, if your research does not support this prediction, feel free to alter it, but you do need to justify why you think you might get your predicted outcome using prior studies in your second literature review).
3). Interaction, Filter Condition (Filtered versus Unfiltered) X Twitter User Gender (Male versus Female). DV = “Insecure”
Participants will rate both the female and male Twitter users in the unfiltered condition as more insecure than participants in the filtered conditions. However, although participants will not differ in their insecurity perceptions of male and female Twitter users in the filtered conditions, they will find the female Twitter user in the filtered condition more insecure than participants in the male Twitter condition.
Although we do not expect differences in the insecurity ratings of participants in the male and female filtered conditions, we predict that participants will find the male and female Twitter users in the unfiltered conditions more insecure than their filtered counterparts, with participants in the unfiltered female condition rating the Twitter user’s insecurity higher than participants in the unfiltered male condition.
(Note again that you need to justify this interaction prediction as well through your literature review. If you disagree with the prediction above, that is fine. You can alter it, but you do need to justify the predictions that you create given the new independent variable).
Keep in mind that each dependent variable you plan to look at in your Study Two will need similar main effect and interaction hypotheses. You also want some overlap between Study One and Study Two, so you might want to focus your predictions for Study Two on the same dependent variables you analyzed in Study One.