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Try out PMC Labs and tell us what you think. Learn More. Respondents were most attracted to and perceived the least risk from attractive descriptions and were least attracted to and perceived the most risk from the risky descriptions. Similar were found by Montano, Kasprzyk, vonHaeften, and Fishbein in their study of different high risk groups. An important question not addressed in these epidemiological studies is how people make these risk assessments.
Knowing how people process limited information about a potential partner takes on additional importance as communication technology changes the way people find romantic and sexual partners. Public health researchers predict that relationships arranged through the internet will contribute to the spread of sexually transmitted diseases STD McFarlane et al.
Several studies have addressed, at a general level, the relationship between judgments of risk and attractiveness and intentions to engage in dating and mating behaviors. Similarly, Dijstra, Buunk and Blanton studied heterosexual males in an experiment that manipulated physical attractiveness assumed to be positively associated with attractiveness and social dominance assumed to be negatively associated with attractiveness of potential female romantic partners.
Participants viewed a picture that was either attractive or unattractive and Woman want casual sex Donner a personality description that was either high or low in social dominance. Although these studies give some insights into the relationships between physical attractiveness, perceived risk, and partner selection, they did not explicitly include features of the potential partner in addition to physical attractiveness such as being a social drinker or a smoker, being self-confident, or expressing specific long term romantic expectations.
Identifying which characteristics of a potential partner are correlated with assessments of risk and attraction and estimating the correlation between these risk and attractiveness judgments were not part of the research problem.
A recent study addressed these concerns. Fishbein et al. For example, they rated characteristics such as self-confident, happy with myself, dependent, is a good listener, has tattoos, or wants to spend quiet evenings together. A strength of the study was the use of an extensive list of possible indicators of risk and attractiveness. This article addresses three general research questions. First, how do young adults balance negative and positive information when making global assessments about romantic partners? Much research shows that decision-making about partner selection uses abstract or likely irrelevant information as inputs, what Misovich et al.
Gold et al. A second question is: what are the gender differences in risk and attractiveness judgments? Given that males respond differently to risk information than females Hoyle et al.
Finally, how does the order of the information presented in the romantic partner descriptions affect overall judgments? Under a model of primacy, initial information embedded in a persuasive message has greater impact than later information; if recency effects operate, later information has the greater impact Hovland, As Ohanian and Cunningham : summarize:.
In a typical primacy-recency study, one side of an issue is presented and then followed by a presentation of the opposite side, or a person is first described negatively and then positively or vice versa. In the case of romantic partner assessment, recency or primacy effects would be evident if descriptions that begin with attractive features Woman want casual sex Donner end with risk features are differentially assessed compared with those that lead with risky aspects and conclude with attractive ones.
The partner descriptions used in this study were deed to differ exactly in this way. The purpose of the present research was to investigate how, in a context analogous to an internet dating site Woman want casual sex Donner participants search for romantic partners not necessarily sex partnersyoung people use the information available to make judgments about the romantic attractiveness as well as risk status. research e. Finally, we assume that the more one believes that unprotected sex with a potential partner will lead to a STD or HIV infection, the less one should be willing to have unprotected sex with that partner.
We hypothesized that the consistent vignettes would produce the most disparate outcomes while the inconsistent partner profiles would produce intermediate values. That is, attractiveness ratings should be highest in descriptions that include no risky features and lowest in those that include no attractive characteristics while risk ratings should show the opposite pattern. The mixed descriptions i. We also expect primacy or recency effects for the mixed pattern but have no prediction as to which will prevail.
Absent an order effect, mixed descriptions should yield statistically equivalent average judgments because the random construction of the descriptions makes them essentially equivalent. We expect no gender differences in the consistently attractive descriptions but do expect gender differences Woman want casual sex Donner the descriptions of the potential romantic partner become dominated by risk information because males are known to be less risk averse than females.
This would suggest an interaction between gender and description type such that males rate risky vignettes as less risky than females but attractive ones approximately the same as females. Respondents were actively recruited to participate in the study on the campuses of two Philadelphia universities using pairs of students handing out flyers describing the study.
The original N was and the age and sexual preference conditions reduced the respondent sample size to Forty-nine percent were male. The survey was computer administered using the software MediaLab Jarvis, and consisted of three sections. The first section asked for age, gender, and sexual orientation so that the potential romantic partner was the appropriate gender for the respondent. The second part presented the respondent with twenty different descriptions of a romantic partner using the factorial survey method. After each description, respondents were asked to rate each potential partner on each of the five assessments.
A factorial survey is a self-administered survey that presents hypothetical scenarios i. Respondents then make an evaluative judgment or a decision based on the data presented in the vignette; this evaluation is the response variable given the vignette stimulus. Factorial surveys are often used to model individual decision-making processes and consumer preferences: Hennessy, Manteuffel, DiIorio, and Adame modeled adolescent decisions to have sex on the basis of randomly constructed social contexts. Some factorial survey examples eliciting preferences include contact-tracing programs to control STD infection Hennessy et al.
The construction and analysis of factorial surveys has been described in detail elsewhere Hennessy et al. The descriptions themselves were randomly constructed by MediaLab from the attributes listed in Appendix 1 based on in Fishbein et al. MediaLab then displayed twenty romantic partner descriptions to the respondent in a random order.
Each description of a potential romantic partner was constructed using an initial and then final section which were one of two types. A sections included partner features that were high attraction-low risk while R sections were constructed from partner features that were high risk-low attraction. The name of the potential partner plays no role in the analysis or study.
It was merely included to make the descriptions more realistic and less redundant. Similarly, the moderator variables play no role in the analysis here. As can be seen in Appendix 1the first attractiveness section A consisted of a choice from high attractiveness attributes 1 and 2, a choice from low risk attribute 1, and a choice from the moderating attribute. The first risky section R consisted of a choice from high risk attributes 1 and 2, a choice from the low attractiveness attribute 1, and a choice from the moderating attribute.
Because our focus here in on the attractiveness and riskiness ratings, we do not analyze the moderating factors here. The last half of the descriptions were constructed the same way: the second attractiveness section A consisted of a choice from high attractiveness attributes 3 and 4 and a choice from low risk attribute 2 while the second risky section R was comprised of a choice from high risk attributes 3 and 4 and a choice from the low attractiveness attribute 2.
Each vignette was a combination of two sections and each respondent evaluated five each of the randomly constructed AAARRAand RR partner descriptions. All the vignettes were uniform in one respect: all romantic partners were described as single and between the ages of 18 and 25 because this was both the general age range of respondents and preliminary research had shown that these two demographic aspects were highly valued in a romantic partner Fishbein et al.
When the selection rules, logical constraints, and gender specific pronouns were applied, a complete AA description displayed for a female heterosexual, bisexual female, or gay male respondent could be:. Paul is single between the ages of 18 and He is faithful to friends and acquaintances and is supportive of others. He believes that sex should be saved for someone really special.
One thing you should know about Paul is that Paul is open minded to new ideas. Brian is single between the ages of 18 and He is trustworthy in dealing with friends and acquaintances and does not use drugs. He strives to live responsibly. One thing you should know about Brian is that Brian carries a laptop most of the time. The respondents assessed the romantic partner descriptions as to romantic attractiveness i. The item wordings were chosen to be consistent with similar questions used in the original study Fishbein et al, All were coded on a 1 to 11 point scale.
For descriptive analysis, we use summary statistics, t and F tests, and bar plots of averages. For all regression analyses, we use random effects regression combined with Huber-White adjustments to the standard errors Kennedy,to correct for non-independence of the observations due to the nested nature of the data i. This is not a concern here, for our clustering variable — the respondent ID - has over values.
Our analysis strategy is the following. First, the three research questions are examined using descriptive graphs, plots, and correlations. Then the hypotheses are revisited using regression analysis to look more specifically at the details of the multivariate relationships, to formally test the equivalency of the AR and MA mixed descriptions for primacy or recency effects, and to investigate the appropriate functional form e.
Table 1 displays the means and standard deviations Woman want casual sex Donner the five outcome variables and Table 2 shows the correlations between the five outcomes. The scale for all outcome items is 1— See text for exact item wording. The have unprotected sex outcome is only moderately correlated with the other variables, due to its limited variance see Table 1. Regression analysis using three dummy variables to capture the types of romantic partner vignette were estimated to test the null hypothesis of no ificant differences on the assessment outcomes between the four types of vignettes.
Note that although statistically ificant, vignette type made little practical difference for the unprotected sex outcome; the averages ranged from 2. As expected, there were large differences between the RR and AA vignettes and smaller differences between RA and AR vignettes because these mixed descriptions present essentially balancing information.
Differences between the mixed vignette types were consistent with the primacy hypothesis: compared with RA vignettes, AR vignettes were rated higher for positive outcomes like attractiveness and going on a date and lower for negative outcomes like risk and getting infected. See Figure 1 for the graphical display of the means. RA is Risky-Attractive vignette type. AR is Attractive-Risky vignette type. AA is Attractive-Attractive vignette type.
All outcome assessments are 1—11 scale. See text for more information. Table 3 presents the mean judgments on each outcome variable by gender. Asterisks indicate statistically ificant differences between genders. This table shows that males are more attracted to and more likely to go on dates with their potential romantic partners than are females.
Males also see their potential romantic partners as less risky overall and as representing a lower chance of infection. Thus it is perhaps not surprising that they are also more likely to indicate that they would have unprotected sex with their potential partners. All outcomes are on a 1—11 scale.
of rated romantic partner descriptions is in parentheses. While the above analyses focused primarily on univariate displays, correlations, and mean differences, more detailed analyses of the research questions relating to the issues of primacy and the potential equivalence of the RA and AR vignettes and possible interactions between gender, partner description, and assessment require ificance tests of equality of regression coefficients and of interaction terms. Table 4 shows the .
The body of the table has the regression coefficients for each predictor with respect to each of the four assessments. Because these are unstandardized coefficients, they are the change in the assessment due to a single unit change in the predictor variables.
These unstandardized coefficients are comparable across dependent variables Greenland et al. All are in reference to the case where the intercept is the average assessment of an AA vignette by female respondent. Tests of RA and AR regression coefficient equivalency:. All outcomes are on a 1—11 scale, all predictors are dummy variables.
Robust Z statistics in parentheses. The regression show that adding risk information to the description decreased attractiveness and the chance of dating and increased perceived risk and chance of infection. But the mixed types of descriptions AR and RA tend not to have the same regression coefficient as they should if these two types were essentially identical from the viewpoint of the respondents.
In fact, there is a strong primacy effect when the vignettes are contradictory i. Specifically, in comparison to the AA vignettes, mixed partner descriptions with risk information first i. The for risk are just the reverse: risk information increases the risk assessment both of general risk and risk of infection more when it precedes than when it follows attractiveness information. For general risk the differences are 2.
All these patterns are unlikely to be due to chance: the change in R-square test that compares the null hypothesis of equal coefficients to the actual data show that the hypothesis of equivalency is not supported: all the F ratios testing this null hypothesis are large and statistically discernable from unity see the bottom section of Table 4 for the details. But is there a gender and vignette interaction? The for the interaction term tests are as follows.
Thus, it appears that male and female respondents do differ in their responses depending on the type of romantic partner description. While these regression tell the statistical story, a better way to display the is through plots. Note that for all assessments there are virtually no differences between male and female respondents for AA vignettes: males and females agree that attractiveness and going on a date is highest and riskiness and chance of infection is lowest compared with the other descriptions. As expected, the more one was attracted to a potential partner, the less they perceived that person as a health risk.
Moreover, although both perceived risk and attraction were ificantly correlated with the intention to go Woman want casual sex Donner a date with the potential partner, this judgment was based primarily on attraction.Woman want casual sex Donner
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