We tested possible variations of the webpages, geographic region, and ethnicity having fun with t-evaluating and you may investigation off variance (ANOVA) into the LIWC group rates. To your two other sites, half a dozen of 12 t-screening was extreme on adopting the categories: first-person only 1 [t(3998) = ?5.61, p Second Dining table dos having mode, standard deviations, and you can contrasts between cultural communities). Contrasts revealed significant differences when considering White and all sorts of most other cultural groups inside four of the half a dozen high ANOVAs. Therefore, we included ethnicity as the a dummy-coded covariate when you look at the analyses (0 = White, step one = Virtually any ethnic groups).

Of your 12 ANOVA testing connected with geographic part, merely one or two were high (household members and you may positive emotion). Once the variations just weren’t commercially important, i didn’t consider geographical region in the further analyses.

Results

Regularity off term explore is evident within the detailed analytics (find Table step 1) and you may thru term-clouds. The phrase-affect technique illustrates the most widely used terms over the whole sample along with all the a long time. The expression-cloud program automatically excludes particular conditions, and additionally blogs (a great, and you may, the) and you may prepositions (so you’re able to, which have, on). The rest blogs terms and conditions is actually scaled in proportions in accordance with the volume, undertaking an user-friendly gaydar portrait of the most extremely common posts terms round the the new attempt ( Wordle, 2014).

Shape step 1 reveals the brand new 20 most commonly known stuff conditions used in the whole attempt. As can rise above the crowd, many frequently used conditions was in fact like (lookin inside 67% off profiles), such as for instance (appearing in the 62% from users), lookin (lookin for the 55% off profiles), and you will individuals (looking inside 50% out of profiles). Ergo, widely known conditions were comparable all over a long time.

Shape dos shows the next 31 common articles terms during the the brand new youngest and you may earliest a long time. By removing the initial 20 popular content conditions along side take to, we teach heterogeneity regarding the matchmaking pages. Next 30 terms into the youngest age bracket, high level percentage conditions provided score (36% from profiles regarding the youngest generation), go (33% out of profiles in the youngest age bracket), and really works (28% out of profiles in the youngest age group). Conversely, brand new earliest age bracket got highest proportions off words such travelling (31% from profiles on earliest age bracket), great (24% regarding users about oldest age group), and relationships (19% regarding pages regarding eldest age group).

Second 31 typical terms throughout the youngest and you may oldest many years groups (shortly after deducting the brand new 20 typical terms off Contour 1).

Theory Assessment old Variations in Code within the Relationships Users

To check hypotheses, the fresh portion of terms and conditions regarding the dating character that fit for each LIWC category served because founded variables within the regressions. I checked-out ages and you can sex as separate variables and changing getting webpages and you can ethnicity.

Theory 1: Old ages would be from the a top portion of terms and conditions regarding the pursuing the classes: first-people plural pronouns, family members, friends, fitness, and you will positive feelings.

Results largely offered Theory step 1 (find Table 2). Four of five regressions shown a significant chief feeling to have many years, such that because chronilogical age of the fresh new profile writer increased, the fresh new percentage of words from the classification enhanced regarding adopting the categories: first-person plural, members of the family, wellness, and positive emotion. I discovered zero high age feeling on ratio away from terminology regarding family relations category.

a sex: 0 (female) and step 1 (male). b Site: The 2 other sites had been dictomously coded since the step 1 and you may 0. c Ethnicity: 0 (White) and step 1 (Cultural or racial minority).

an intercourse: 0 (female) and you can step one (male). b Web site: The 2 other sites was basically dictomously coded given that step one and you will 0. c Ethnicity: 0 (White) and 1 (Cultural or racial fraction).