Neo Personality Inventory Case Study
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We will take care of all your assignment needs We are a leading online assignment help service provider. Place an Order. Calculate your essay price. Type of paper. Academic level. Pages words. Read more. Plagiarism-free papers To ensure that all the papers we send to our clients are plagiarism free, they are all passed through a plagiarism detecting software. With the addition of gender and age variables, this resulted in seven total dependent variables studied in this work, which are depicted in Table 1 along with summary statistics.
Personality distributions are quite typical with means near zero and standard deviations near 1. The statuses ranged over 34 months, from January through October Previously, profile information i. Results of our analyses over gender, age, and personality are presented below. As a baseline, we first replicate the commonly used LIWC analysis on our data set. We then present our main results, the output of our method, DLA. Lastly, we explore empirical evidence that open-vocabulary features provide more information than those from an a priori lexicon through use in a predictive model.
Figure 2 shows the results of applying the LIWC lexicon to our dataset, along side-by-side with the most comprehensive previous studies we could find for gender , age. In our case, correlation results are values from an ordinary least squares linear regression where we can adjust for gender and age to give the unique effect of the target variable. One should keep in mind that it is often found that effect sizes tend to be relatively smaller as sample sizes increase and become more stable [84]. Even though the previous studies listed did not look at Facebook, a majority of the correlations we find agree in direction.
Articles are highly predictive of males, being older, and openness. As a content-related language variable, the anger category also proved highly predictive for males as well as younger individuals, those low in agreeableness and conscientiousness , and high in neuroticism. Openness had the least agreement with the comparison study; roughly half of our results were in the opposite direction from the prior work. This is not too surprising since openness exhibits the most variation across conditions of other studies for examples, see [25] , [27] , [65] , and its component traits are most loosely related [85]. Our DLA method identifies the most distinguishing language features words, phrases : a sequence of 1 to 3 words, or topics : a cluster of semantically related words for any given attribute.
Results progress from a one variable proof of concept gender , to the multiple variables representing age groups, and finally to all 5 dimensions of personality. Gender provides a familiar and easy to understand proof of concept for open-vocabulary analysis. Figure 3 presents word clouds from age-adjusted gender correlations. We scale word size according to the strength of the relation and we use color to represent overall frequency; that is, larger words indicate stronger correlations, and darker colors indicate frequently used words. For the topics , groups of semantically-related words, the size indicate the relative prevalence of the word within the cluster as defined in the methods section.
All results are significant at Bonferroni-corrected [76]. Many strong results emerging from our analysis align with our LIWC results and past studies of gender. For example, females used more emotion words [86] , [87] e. Males used more swear words, object references e. Other results of ours contradicted past studies, which were based upon significantly smaller sample sizes than ours. For example, in bloggers Huffaker et al. We calculated power analyses to determine the sample size needed to confidently find such significant results. Since the Bonferonni-correction we use elsewhere in this work is overly stringent i. Rerunning our language of gender analysis on reduced random samples of our subjects resulted in the following number of significant correlations Benjamini-Hochberg tested : 50 subjects: 0 significant correlations, subjects: 7 correlations; 5, subjects: 1, correlations; 50, subjects: 13, correlations more detailed results of power analyses across gender, age, and personality can be found in Figure S1.
Thus, traditional study sample sizes, which are closer to 50 or , are not powerful enough to do data-driven DLA over individual words. One might also draw insights based on the gender results. Figure 4 shows the word cloud center and most discriminating topics surrounding for four age buckets chosen with regard to the distribution of ages in our sample Facebook has many more young people. We see clear distinctions, such as use of slang, emoticons, and Internet speak in the youngest group e. We also find subtle changes of topics progressing from one age group to the next. For example, we see a school related topic for 13 to 18 year olds e. Additionally, consider the drunk topic e. Ordered from top to bottom: 13 to 18 19 to 22 23 to 29 , and 30 to In general, we find a progression of school, college, work, and family when looking at the predominant topics across all age groups.
DLA may be valuable for the generation of hypotheses about life span developmental age differences. Figure 5A shows the relative frequency of the most discriminating topic for each age group as a function of age. Typical concerns peak at different ages, with the topic concerning relationships e. We take this as a proxy for social integration [19] , suggesting the increasing importance of friendships and relationships as people age. Figure 5B reinforces this hypothesis by presenting a similar pattern based on other social topics.
One limitation of our dataset is the rarity of older individuals using social media; we look forward to a time in which we can track fine-grained language differences across the entire lifespan. Standardized frequency for the best topic for each of the 4 age groups. Standardized frequency of social topic use across age. We created age and gender-adjusted word clouds for each personality factor based on around 72 thousand participants with at least 1, words across their Facebook status updates, who took a Big Five questionnaire [91].
Figure 6 shows word clouds for extraversion and neuroticism. See Figure S2 for openness, conscientiousness, and agreeableness. The dominant words in each cluster were consistent with prior lexical and questionnaire work [14]. Language of extraversion left, e. Language distinguishing neuroticism left, e. Figure S8 contains results for openness , conscientiousness , and agreeableness. Topics were also found reflecting similar concepts as the words, some of which would not have been captured with LIWC. For example, although LIWC has socially related categories, it does not contain a party topic, which emerges as a key distinguishing feature for extraverts.
Topics related to other types of social events are listed elsewhere, such as a sports topic for low neuroticism emotional stability. Additionally, Figure 6 shows the advantage of having phrases in the analysis to get clearer signal: e. While many of our results confirm previous research, demonstrating the instrument's face validity, our word clouds also suggest new hypotheses.
For example, Figure 6 bottom-right shows language related to emotional stability low neuroticism. Additionally, results suggest that introverts are interested in Japanese media e. Although these are only language correlations, they show how open-vocabulary analyses can illuminate areas to explore further. Here we present a quantitative evaluation of open-vocabulary and closed vocabulary language features. Although we have thus far presented subjective evidence that open-vocabulary features contribute more information, we hypothesize empirically that the inclusion of open-vocabulary features leads to prediction accuracies above and beyond that of closed-vocabulary.
We use a linear support vector machine SVM [92] for classifying the binary variable of gender, and ridge regression [93] for predicting age and each factor of personality. Features were first run through principal component analysis to reduce the feature dimension to half of the number of users. Thus, the predictive model is created without any outcome information outside of the training data, making the test data an out-of-sample evaluation. As open-vocabulary features, we use the same units of language as DLA : words and phrases n-grams of size 1 to 3, passing a collocation filter and topics. As explained in that section, we use Anscombe transformed relative frequencies of words and phrases and the conditional probability of a topic given a subject.
For closed vocabulary features, we use the LIWC categories of language calculated as the relative frequency of a user mentioning a word in the category given their total word usage. We do not provide our models with anything other than these language usage features independent variables for prediction, and we use usage of all features not just those passing significance tests from DLA. As shown in Table 2 , we see that models created with open vocabulary features significantly outperformed those created based on LIWC features. The topics results are of particular interest, because these automatically clustered word-category lexica were not created with any human or psychological data — only knowing what words occurred in messages together.
Furthermore, we see that a model which includes LIWC features on top of the open-vocabulary words , phrases , and topics does not result in any improvement suggesting that the open-vocabulary features are able to capture predictive information which fully supersedes LIWC. For personality we saw the largest relative improvement between open-vocabulary approaches and LIWC. Some researchers have discretized the personality scores for prediction, and classified people as being high or low one standard deviation above or below the mean or top and bottom quartiles, throwing out the middle in each trait [61] , [64] , [67].
When we do such an approach, our scores are in similar ranges to such literature: to classification accuracy. Regression is a more appropriate predictive task for continuous outcomes like age and personality, even though scores are naturally smaller than binary classification accuracies. We ran an additional tests to evaluate only those words and phrases, topics, or LIWC categories that are selected via differential language analysis rather than all features. Thus, we used only those language features that significantly correlated Bonferonni-corrected with the outcome being predicting. To keep consistent with the main evaluation, we used no controls, and so one could view this as a univariate feature selection over each type of feature independently.
We again found significant improvement from using the open-vocabulary features over LIWC and no significant changes in accuracy overall. These results are presented in Table S2. In addition to demonstrating the greater informative value of open-vocabulary features, we found our results to be state-of-the-art. The highest previous out-of-sample accuracies for gender prediction based entirely on language were Our increased performance could be attributed to our set of language features, a strong predictive algorithm the support vector machine , and the large sample of Facebook data. Language use is objective and quantifiable behavioral data [96] , and unlike surveys and questionnaires, Facebook language allows researchers to observe individuals as they freely present themselves in their own words.
Differential language analysis DLA in social media is an unobtrusive and non-reactive window into the social and psychological characteristics of people's everyday concerns. Most studies linking language with psychological variables rely on a priori fixed sets of words, such as the LIWC categories carefully constructed over 20 years of human research [11]. Here, we show the benefits of an open-vocabulary approach in which the words analyzed are based on the data itself. We extracted words , phrases , and topics automatically clustered sets of words from millions of Facebook messages and found the language that correlates most with gender, age, and five factors of personality.
We discovered insights not found previously and achieved higher accuracies than LIWC when using our open-vocabulary features in a predictive model, achieving state-of-the-art accuracy in the case of gender prediction. Exploratory analyses like DLA change the process from that of testing theories with observations to that of data-driven identification of new connections [97] , [98]. Our intention here is not a complete replacement for closed-vocabulary analyses like LIWC.
When one has a specific theory in mind or a small sample size, an a priori list of words can be ideal; in an open-vocabulary approach, the concept one cares about can be drowned out by more predictive concepts. Further, it may be easier to compare static a priori categories of words across studies. However, automatically clustering words into coherent topics allows one to potentially discover categories that might not have been anticipated e. Open-vocabulary approaches also save labor in creating categories.
They consider all words encountered and thus are able to adapt well to the evolving language in social media or other genres. They are also transparent in that the exact words driving correlations are not hidden behind a level of abstraction. Given lots of text and dependent variables, an open-vocabulary approach like DLA can be immediately useful for many areas of study; for example, an economist contrasting sport utility with hybrid vehicle drivers, a political scientist comparing democrats and republicans, or a cardiologist differentiating people with positive versus negative outcomes of heart disease.
Like most studies in the social sciences, this work is still subject to sampling and social desirability biases. Language connections with psychosocial variables are often dependent on context [40]. Here, we examined language in a large sample of the broad context of Facebook. Under different contexts, it is likely some results would differ. Still, the sample sizes and availability of demographic information afforded by social media bring us closer to a more ideal representative sample [99]. Over the past one-hundred years, surveys and questionnaires have illuminated our understanding of people.
We suggest that new multipurpose instruments such as DLA emerging from the field of computational social science shed new light on psychosocial phenomena. Power analyses for all outcomes examined in this work. Words, phrases, and topics most distinguishing agreeableness , conscientiousness , and openness. Language of high agreeableness left and low agreeableness right ;. Language of high conscientiousness left and low conscientiousness right ;. Language of openness left and closed to experience right ; adjusted for gender and age, Bonferroni-corrected.
The 15 most prevalent words for the automatically generated topics used in our study. All topics available here: wwbp. Prediction results when selecting features via differential language analysis. Topics : Automatically created LDA topic clusters. WordPhrases : words and phrases n-grams of size 1 to 3 passing a collocation filter. No controls were used so as to be consistent with the evaluation in the main paper, and so one could consider this a univariate feature selection.
On average results are just below those of not using differential language analysis to select features but there is no significant difference. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract We analyzed million words, phrases, and topic instances collected from the Facebook messages of 75, volunteers, who also took standard personality tests, and found striking variations in language with personality, gender, and age.
Introduction The social sciences have entered the age of data science, leveraging the unprecedented sources of written language that social media afford [1] — [3]. Background This section outlines recent work linking language with personality, gender, and age. Automatic Lexical Analysis of Personality, Gender, and Age By examining what words people use, researchers have long sought a better understanding of human psychology [17] — [19]. Predictive Models based on Language In contrast with the works seeking to gain insights about psychological variables, research focused on predicting outcomes have embraced data-driven approaches. Download: PPT. Figure 1. The infrastructure of our differential language analysis.
Figure 2. Correlation values of LIWC categories with gender, age, and the five factor model of personality. Contributions The contributions of this paper are as follows: First, we present the largest study of personality and language use to date. With just under 75, authors, our study covers an order-of-magnitude more people and instances of language features than the next largest study [27]. The size of our data enables qualitatively different analyses, including open vocabulary analysis, based on more comprehensive sets of language features such as phrases and automatically derived topics.
Most prior studies used a priori language categories, presumably due in part to the sparse nature of words and their relatively small samples of people. With smaller data sets, it is difficult to find statistically significant differences in language use for anything but the most common words. Our open-vocabulary analysis yields further insights into the behavioral residue of personality types beyond those from a priori word-category based approaches, giving unanticipated results correlations between language and personality, gender, or age. Our inclusion of phrases in addition to words provided further insights e.
Such correlations provide quantitative evidence for strong links between behavior, as revealed in language use, and psychosocial variables. In turn, these results suggest undertaking studies, such as directly measuring participation in activities in order to verify the link with emotional stability. We demonstrate open-vocabulary features contain more information than a priori word-categories via their use in predictive models.
We take model accuracy in out-of-sample prediction as a measure of information of the features provided to the model. Models built from words and phrases as well as those from automatically generated topics achieve significantly higher out-of-sample prediction accuracies than a standard lexica for each variable of interest gender , age , and personality. Additionally, our prediction model for gender yielded state-of-the-art results for predictive models based entirely on language, yielding an out-of-sample accuracy of We present a word cloud visualization which scales words by correlation i.
Since we find thousands of significantly correlated words, visualization is key, and our differential word clouds provide a comprehensive view of our results e. Lastly, we offer our comprehensive word , phrase , and topic correlation data for future research experiments see: wwbp. Figure 3. Words, phrases, and topics most highly distinguishing females and males. Closed Vocabulary: Word-Category Lexica A common method for linking language with psychological variables involves counting words belonging to manually-created categories of language. That is to say that, when in excess, even positive qualities like ambition, reservation, courage, and mischief, will prove destructive.
The Hogan pre-employment assessment, then, can provide extremely invaluable information to employers about promising applicants. This psychometric evaluation, by contrast, will identify personality flaws without you having to specifically share them. Unfortunately, many of the qualities associated with dark side, are also linked to power dynamics. Whether dealing with animals, children, or, in this case employees, supervisors tend to mistreat their subordinates.
The more preparation you can do the better. Review some sample questions and answers before the test and practice answering as if it were the real HDS personality test.
