I checked possible distinctions from the site, geographical part, and you may ethnicity playing with t-testing and research out of variance (ANOVA) on LIWC classification rates. For the a couple websites, half a dozen of one’s a dozen t-examination was extreme regarding adopting the kinds: first-people one [t(3998) = ?5.61, p Additional Table 2 having function, basic deviations, and you may contrasts ranging from ethnic organizations). Contrasts shown extreme differences between Light as well as almost every other cultural teams inside five of your six significant ANOVAs. Hence, i incorporated ethnicity given that a great dummy-coded covariate in the analyses (0 = White, step one = Various other ethnic teams).
Of your a dozen ANOVA tests linked to geographical part, just two was basically high (family relations and you may self-confident feeling). Due to the fact differences just weren’t technically significant, we did not imagine geographical part in further analyses.
Overall performance
Regularity regarding word use is obvious during the descriptive statistics (look for Table step 1) and free chinese dating sites you can thru word-clouds. The expression-affect technique depicts many popular terms over the entire shot plus each of the a long time. The definition of-affect system immediately excludes certain terminology, also blogs (a, and you may, the) and you will prepositions (in order to, that have, on). The remaining posts terms and conditions was scaled in dimensions in line with their regularity, carrying out an user-friendly portrait of the most extremely commonplace posts conditions across the new take to ( Wordle, 2014).
Profile step one suggests this new 20 most commonly known blogs conditions utilized in the whole sample. As can be seen, by far the most frequently used terms and conditions have been love (lookin in the 67% regarding pages), such (appearing into the 62% of profiles), looking (appearing in 55% off profiles), and you may some body (appearing inside the 50% off users). Thus, the most used words was basically comparable across the age groups.
Contour dos shows next 31 popular content terms in the brand new youngest and you can eldest age range. By removing the first 20 preferred blogs terminology over the test, i show heterogeneity on matchmaking profiles. Next 30 conditions toward youngest age group, significant number terms included score (36% of pages regarding youngest age bracket), go (33% out-of pages regarding youngest generation), and you may works (28% out of users in the youngest age group). In contrast, the latest eldest age bracket had higher rates of terminology instance travel (31% regarding profiles from the earliest age group), high (24% regarding profiles on oldest age bracket), and matchmaking (19% regarding users on earliest age bracket).
Next 30 most frequent terms and conditions on youngest and you can eldest decades communities (after subtracting the 20 common terminology of Contour step 1).
Theory Research of age Differences in Code inside the Relationship Users
To test hypotheses, the fresh new percentage of terms regarding the relationship character that fit for each and every LIWC category offered as mainly based parameters within the regressions. We checked-out years and you may gender because the separate variables in addition to adjusting to have website and ethnicity.
Hypothesis step 1: More mature ages could well be in the a higher portion of terminology on the following groups: first-individual plural pronouns, relatives, household members, health, and you may self-confident emotion.
Findings mostly served Hypothesis step 1 (look for Desk dos). Four of your four regressions revealed a serious fundamental effect to have age, in a fashion that while the ages of this new character author increased, this new part of terminology on the classification improved regarding pursuing the categories: first-individual plural, family members, wellness, and you may positive feelings. We located no tall ages impression toward proportion away from conditions on the loved ones group.
an excellent Gender: 0 (female) and you will 1 (male). b Web site: The 2 other sites was in fact dictomously coded given that step one and you can 0. c Ethnicity: 0 (White) and you may step one (Cultural otherwise racial minority).
a beneficial Gender: 0 (female) and you can step one (male). b Site: Both other sites had been dictomously coded since the 1 and 0. c Ethnicity: 0 (White) and step 1 (Cultural or racial minority).