We checked-out potential variations of the website, geographic region, and you may ethnicity using t-examination and you will analysis out-of difference (ANOVA) toward LIWC classification percentages. On the a couple of websites, six of one’s several t-examination was extreme regarding the after the kinds: first-person just one [t(3998) = ?5.61, p Additional Desk 2 to possess function, simple deviations, and you can contrasts between cultural teams). Contrasts revealed extreme differences between Light as well as most other ethnic organizations in four of your own half a dozen extreme ANOVAs. Hence, we incorporated ethnicity because a dummy-coded covariate for the analyses (0 = Light, 1 = Almost every other cultural communities).
Of the several ANOVA examination linked to geographical region, only a couple of was basically high (family relations and you can self-confident emotion). Due to the fact variations were not technically meaningful, i failed to consider geographical part in after that analyses.
Volume out of word use is obvious within the descriptive analytics (look for Table step one) and you may through keyword-clouds. The word-affect techniques depicts many widely used terms along side entire test plus in each one of the a long time. The term-cloud system instantly excludes certain terms and conditions, and articles (a, and you may, the) and you can prepositions (so you’re able to, that have, on). The remaining posts terminology try scaled sizes prior to the frequency, carrying out an user-friendly portrait quite common posts terms around the new attempt ( Wordle, 2014).
Figure 1 shows the fresh new 20 most common posts terminology found in the whole decide to try. As can be seen, the essential frequently used conditions were love (looking when you look at the 67% from users), like (appearing in 62% away from users), looking (looking when you look at the 55% regarding pages), and you can anybody (searching when you look at the fifty% away from pages). Thus, the preferred terms and conditions was indeed similar round the age groups.
Figure 2 reveals the following 29 typical stuff terms during the new youngest and you can earliest a long time. By removing the initial 20 popular content terminology over the try, i teach heterogeneity regarding the dating profiles. Next 31 terminology for the youngest age bracket, significant number terms included score (36% off pages in the youngest generation), go (33% out of profiles in the youngest age bracket), and you will performs (28% out-of users regarding youngest age bracket). Having said that, the fresh new oldest age group had high rates from terms and conditions such take a trip (31% out-of profiles about oldest age group), high (24% away from users regarding the earliest generation), and you may dating (19% off pages on the eldest generation).
Second 29 common terms on youngest and you will eldest years organizations (once deducting the 20 typical conditions off Contour step 1).
Theory Research old Differences in Words for the Relationships Profiles
To check on hypotheses, brand new percentage of terms on the relationship reputation that suit for every LIWC class supported because the mainly based variables for the regressions. I checked decades and gender due to the fact separate variables as well as adjusting to possess site and ethnicity.
Hypothesis 1: More mature age could well be in the a high part of terms about following categories: first-people plural pronouns, family members, relatives, fitness, and self-confident emotion.
Results mostly offered Theory step 1 (get a hold of Table dos). Four of the four regressions found a critical main effect having many years, such that once the chronilogical age of the reputation author increased, the newest portion of terminology throughout the classification increased regarding the following the categories: first-person plural, members of the family, wellness, and you can positive emotion. I found zero tall ages impact to your ratio out-of terminology regarding the members of the family category.
a sex: 0 (female) and step one (male). b Web site: Both other sites had been dictomously coded due to the fact step 1 and 0. c Ethnicity: 0 (White) and you will 1 (Ethnic or racial fraction).
an intercourse: 0 (female) and you will 1 (male). b Web site: The two other sites was dictomously coded given that 1 and you will 0. c Ethnicity: 0 (White) and you will step one (Cultural or racial minority).