Are men overrated by women?
Abstract
The research is a part of an observational study that records the ratings of several personality attributes by people who volunteered in a Speed Dating Experiment. The focus of this paper is to find if, for the same level of various personality traits, do men enjoy more affection compared to what they impart on women. The result of this research shows that a male is rated about 0.23 points (on a scale of 0–10) higher than a female of a similar persona.
Introduction
Some researches have shown that men overestimate their attractiveness while another research says evolution has made women more attractive. The objective of this paper is to see if gender has a difference in how people perceive their partners.
Problem Statement
The focus is to find if gender influences how one rates one’s partner. Also, to find the most significant traits of a personality that contribute to the overall likeness towards one’s partner.
Research Methodology
The primary research was done by Professor Ray Fisman and Professor Sheena Iyengar from Columbia Business School. Around 552 heterosexuals from various professions have taken part in this experiment where they each have a four-minute date with various other participants of the opposite sex. They are rated before, during, and after the short date on the parameters of Attractiveness, Sincerity, Intelligence, being Fun, and Ambitious. Then a likeness rating was awarded which denotes the overall affection towards the partner. The experiment also includes interests and preferences of date, how the persons rate themselves on these parameters, etc. The results are available on Kaggle and can be used to answer a wide variety of questions regarding the mating behavior. This paper is focused on the five personality attributes and the overall likeness rating awarded to the participants. All the ratings are recorded on a scale of 0 to 10.
Hypotheses
The null hypothesis is that a male and a female with similar personalities, i.e., who received the same rating on the five attributes, also receive the same overall likeness rating from their respective partners. The alternate hypothesis is that an equally rated male and female, receive different likeness ratings from the partners.
Ho: Males and females with similar personalities standards receive similar likeness rating.
Ha: Males and females with similar personalities standards receive different likeness rating.
Solution
The analysis is broken down into four steps. The first step is to analyze the data set and perform data pre-processing. It includes inspecting the observations to ensure a random spread from the true population so that the sample is not biased. The second step is to visualize, and, if required, perform the transformations, on the independent and dependent variables. The third step is to shortlist the most significant variables that help in explaining the overall likeness rating. The model obtained with the most significant variables is termed as Reduced model. Then the indicator variable of gender, which denotes 1 for males and 0 for females is added to create a Full model. Finally, ANOVA is conducted to compare the reduced and full models and find the significance of gender in determining the overall likeness rating.
Analysis
Data Selection
The raw data set has about 2000 observations. After removing the rows that were missing at random, the data set is found to be disproportionately based on profession as almost half of the data set contains participants who either belonged to Law or to Business fields. An approx. same ratio and sample size of men and women are randomly chosen from the various professions like healthcare, politics, etc. to create a balanced data set of 706 observations.
Analysis, Visualization, and Transformation
There are approx. 45% of females and 55% of males. The proportion of couples belonging to the same race is 51% and to different races is 49% (Table 1).
Participants from 6 professions each contribute within the range of 14–19% to the total sample size (Table 2).
On visualization, dependent (Display1), as well as the independent (Display2) variables, look approximately normal with skewness lying within the range of -0.4 to -0.6. The boxplot shows a few outliers towards the lower tail but no skewness. Hence, no transformation is required for the data set.
The Interquartile range and median of ambitious-sincerity; and attractiveness-fun are approx. same. Distribution of Intelligence shows, in general, it is rated higher than other attributes.
A gender-wise F-test to compare the variances on each independent variable signifies unequal variances of Sincerity, being Fun and ambitious(Table3).
Interestingly, the two-sample t-test, at a 5% significance level, suggests that the mean rating of females for Attractiveness and being fun is higher than that of males, whereas, on average, males are rated higher for Intelligence and being ambitious(Table4).
Variable Selection
The initial model is fed with nine Independent variables, namely, Attractiveness, Intelligence, Ambitious, Sincerity, Fun, age, race, same race and an interaction term of race and the same race is also included, as people might prefer a partner from the same race and hence, it could be a significant variable for the model.
Stepwise model selection criteria have been used with both, AIC and BIC values to determine the most significant variables for the model. The minimum Cp statistic value for each of the AIC and BIC models created from Stepwise is selected. All the techniques, AIC, BIC, and Cp statistic suggest 4 explanatory variables(Table5).
Hence, following Occum’s razor principle and the results from the model selection criteria, a model with four independent variables is chosen. The final variables selected are Fun, Attractiveness, Intelligence, and Sincerity. The R squared or the variation in overall likeness rating explained from the model is approx. 64%.
Model Creation
The correlation chart (Display 6) which shows the correlation of the overall ‘like’ rating with the independent variables also suggests the same four explanatory variables for the model. This is called the Reduced Model (Reduced Model).
The variable of interest ‘gender’, which indicates 1 for males and 0 for females, is then added to the Reduced model to create the Full model, resulting in 5 independent variables(Full Model). The significance of Intelligence is reduced after adding the gender variable.
Reduced Model : likeness = b0 + b1.fun + b2. attr + b3. sinc + b4. intel
Full Model : likeness = b0 + b1.fun + b2. attr + b3. sinc + b4. intel + b5. gender
Model Comparison
The lack of fit test, ANOVA, is conducted on the Full and Reduced model to find the significance of gender (Display 5).
The null hypotheses is that the coefficient on gender is insignificant, Ho : b5 =0
The alternate Hypotheses is that the coefficient on gender is significant, Ha : b5 !=0
At a 5% significance level, the F statistic of 6.83 and the two-tailed p value<0.05, (being 0.0092), suggests that the gender variable is significant for the model.
Conclusion
The research provides enough evidence to reject the Null hypotheses that there is no difference in how males and females are rated by their partners, for the same personality standards. The estimated coefficient of slope on gender is 0.232 (on a scale of 0–10), which means, males are rates about 0.23 points higher as compared to females in the overall rating of likeness. Further, out of the five traits that were judged in this research, Fun, Attractiveness, Intelligence and Sincerity are the most significant in determining the overall likeness towards a person.
Scope of Inference
Since this is an observational study, that is conducted as an experiment with the people who volunteered, an inference to the true population can not be made. It consists of heterosexual males and females, which means the allocation is by nature and not random, hence, causal inference cannot be established with the results.
References
1. Fisman, Raymond; Iyengar, Sheena, 2006. ‘Gender Differences in Mate Selection: Evidence From a Speed Dating Experiment’. The Quarterly Journal of Economics
2. Regan, Pamela C.,1998, ‘Minimum Mate Selection Standards as a Function of Perceived Mate Value, Relationship Context, and Gender’, Journal of Psychology & Human Sexuality.
3. Speed Dating Experiment, ‘What attributes influence the selection of a romantic partner?’, https://www.kaggle.com/annavictoria/speed-dating-experiment
