We checked out potential distinctions of the webpages, geographic part, and you may ethnicity playing with t-evaluation and you will analysis off variance (ANOVA) into LIWC class percent. Into a couple other sites, six of twelve t-assessment was indeed tall on adopting the kinds: first-person one [t(3998) = ?5.61, p Supplementary Desk dos to possess mode, basic deviations, and you will contrasts between ethnic organizations). Contrasts shown significant differences when considering White as well as most other cultural communities within the five of one’s half dozen significant ANOVAs. Thus, i incorporated ethnicity because the a beneficial dummy-coded covariate inside analyses (0 = White, step one = Virtually any ethnic teams).
Of your a dozen ANOVA examination about geographical region, just a couple was indeed extreme (family and you will self-confident feeling). Once the variations just weren’t technically important, we didn’t imagine geographic area within the after that analyses.
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Regularity from term explore goes without saying within the descriptive analytics (select Dining table 1) and you can thru keyword-clouds. The term-cloud strategy depicts the quintessential widely used terms 
Shape step 1 shows new 20 most commonly known blogs terms found in the entire attempt. As can get noticed, by far the most frequently employed words was basically like (searching from inside the 67% out-of users), instance (appearing from inside the 62% of users), looking (lookin from inside the 55% of profiles), and you will people (appearing inside the 50% from users). Thus, typically the most popular terms and conditions was in fact equivalent round the a long time.
Figure 2 reveals another 31 typical content terms during the this new youngest and you will earliest age groups. By removing the original 20 preferred posts terminology over the attempt, we instruct heterogeneity throughout the relationship users. Within the next 30 terms and conditions into youngest generation, high level percentage terms and conditions included score (36% regarding pages regarding the youngest age group), go (33% regarding users in the youngest age bracket), and really works (28% away from users in the youngest age bracket). In contrast, the oldest age bracket had large percentages regarding words such as take a trip (31% of users regarding earliest generation), higher (24% away from users regarding the earliest age bracket), and you may matchmaking (19% out-of pages on eldest generation).
2nd 30 popular terms and conditions throughout the youngest and you can oldest ages communities (shortly after deducting new 20 most common words away from Profile step 1).
Theory Investigations of age Variations in Vocabulary when you look at the Dating Pages
To test hypotheses, the fresh portion of terms in the relationships reputation that fit each LIWC classification supported as built details during the regressions. I checked ages and you may intercourse as independent details along with modifying having website and you can ethnicity.
Hypothesis 1: Old years could well be of this a high percentage of terms and conditions regarding pursuing the categories: first-individual plural pronouns, relatives, family, fitness, and you will positive feeling.
Findings largely served Theory step one (come across Table dos). Five of the four regressions revealed a significant fundamental impression to have age, in a way that given that ages of the fresh new profile publisher increased, this new portion of conditions regarding class improved in the adopting the categories: first-people plural, family members, wellness, and confident emotion. We located zero high ages feeling for the proportion from terms regarding the loved ones category.
a gender: 0 (female) and you may step one (male). b Site: Both other sites was basically dictomously coded because the step one and 0. c Ethnicity: 0 (White) and you may 1 (Ethnic or racial fraction).
a gender: 0 (female) and you will step one (male). b Web site: The 2 websites was dictomously coded once the step one and you will 0. c Ethnicity: 0 (White) and you can step one (Ethnic otherwise racial fraction).