I checked prospective variations from the site, geographical part, and you will ethnicity using t-assessment and studies off variance (ANOVA) toward LIWC category percentages. For the one or two other sites, half dozen of your own a dozen t-assessment had been extreme about pursuing the categories: first-person just one [t(3998) = ?5.61, p Supplementary Desk dos having setting, standard deviations, and you can contrasts between cultural teams). Contrasts shown tall differences when considering Light as well as most other cultural organizations when you look at the five of one’s half dozen high ANOVAs. Therefore, we provided ethnicity given that a great dummy-coded covariate within the analyses (0 = Light, 1 = Some other cultural groups).
Of the a dozen ANOVA screening regarding geographic part, just one or two was in fact tall (family unit members and positive emotion). Because the variations weren’t technically meaningful, we didn’t envision geographical area within the further analyses.
Volume from phrase use is obvious in the descriptive analytics (look for Dining table step one) and you will through phrase-clouds. The definition of-affect method illustrates one particular widely used terminology along the entire sample and also in all the a long time. The expression-affect program instantly excludes specific words, including articles (good, and you will, the) and prepositions (to help you, having, on). The remainder stuff words are scaled in proportions in line with the regularity, creating an user-friendly portrait of the very common content terms around the this new shot ( Wordle, 2014).
Contour step 1 suggests the new 20 common content words found in the whole try. As well as rise above the crowd, the absolute most commonly used conditions were like (appearing from inside the 67% regarding pages), such as for instance (lookin when you look at the 62% of profiles), lookin (searching in the 55% of profiles), and you may anybody (lookin for the 50% out of pages). For this reason, the most used terms were similar all over age range .
Profile 2 shows next 30 most commonly known articles words from inside the the youngest and you can earliest age groups. By detatching the original 20 preferred blogs conditions along side decide to try, i show heterogeneity about matchmaking pages. In the next 31 terms and conditions towards youngest generation, raised percentage words integrated get (36% out-of users from the youngest generation), go (33% off pages in the youngest generation), and you can work (28% away from pages from the youngest age group). In contrast, the fresh eldest age group had highest percentages regarding terminology instance travel (31% from profiles throughout the oldest age bracket), great (24% away from users on eldest age group), and relationships (19% from pages about earliest generation).
2nd 29 most frequent words regarding youngest and earliest age communities (shortly after subtracting the 20 typical terminology from Contour step one).
Theory Assessment old Variations in Words from inside the Dating Users
To test hypotheses, the brand new part of terms regarding the relationship profile that suit each LIWC category served due to the fact mainly based details when you look at the regressions. We examined years and you may intercourse just like the independent details including changing getting site and you may ethnicity.
Theory 1: Earlier ages might possibly be on the a high percentage of terms on following classes: first-people plural pronouns, members of the family, household members, wellness, and you can confident feeling.
Conclusions mostly supported Theory step one (come across Desk 2). Five of one’s four regressions revealed a critical chief feeling to own years, in a way that since the chronilogical age of the brand new profile author enhanced, this new part of terms about classification increased from the pursuing the categories: first-individual plural, loved ones, health, and you can confident feeling. I receive zero extreme decades feeling into the ratio out-of conditions regarding the family unit members classification.
a sex: 0 (female) and 1 (male). b Web site: The two other sites were dictomously coded just like the 1 and you can 0. c Ethnicity: 0 (White) and you will step 1 (Ethnic otherwise racial fraction).
an intercourse: 0 (female) and you can 1 (male). b Web site: Both websites was in fact dictomously coded just like the step one and you can 0. c Ethnicity: 0 (White) and you can 1 (Ethnic or racial fraction).