We examined possible differences by the webpages, geographical part, and you will ethnicity using t-assessment and you may analysis out of variance (ANOVA) to the LIWC group proportions. Into several websites, half a dozen of the twelve t-assessment was extreme throughout the pursuing the categories: first-person only 1 [t(3998) = ?5.61, p Additional Table dos having means, simple deviations, and you will contrasts ranging from cultural organizations). Contrasts found extreme differences when considering White and all other ethnic organizations during the five of the half dozen extreme ANOVAs. For this reason, we integrated ethnicity due to the fact an effective dummy-coded covariate in the analyses (0 = White, 1 = All other cultural groups).
Of your own 12 ANOVA testing connected with geographic part, merely one or two was significant (family and you may confident emotion). Because the differences were not officially significant, we didn’t thought geographic region into the further analyses.
Abilities
Volume regarding phrase fool around with goes without saying inside the detailed statistics (get a hold of Desk 1) and you can thru keyword-clouds. The phrase-affect strategy illustrates more widely used words over the entire decide to try plus all the a long time. The expression-affect system immediately excludes certain words, including blogs (a beneficial, and, the) and prepositions (to help you, which have, on). The remaining stuff conditions was scaled sizes prior to the volume, performing an intuitive portrait really prevalent articles terms round the the newest try ( Wordle, 2014).
Figure 1 reveals the fresh 20 typical blogs conditions found in the complete take to. As can get noticed, the quintessential commonly used terminology had been love (searching during the 67% out of pages), eg (searching when you look at the 62% out of users), searching (searching within the 55% of profiles), and you can people (lookin within the 50% off pages). Ergo, the most popular terms and conditions was equivalent across the age groups.
Figure dos reveals the next 29 most typical content terms within the the fresh youngest and eldest a long time. By removing the first 20 well-known posts conditions along the shot, we illustrate heterogeneity about relationship pages. Within the next 30 conditions into the youngest age bracket, significant number terms and conditions integrated score (36% away from profiles regarding the youngest age group), wade (33% away from pages in the youngest age group), and you may functions (28% out of users on youngest generation). Having said that, new eldest generation got highest proportions of conditions such travel (31% out of pages on eldest generation), great (24% regarding pages about eldest generation), and dating (19% regarding pages on earliest age group).
Next 31 popular conditions on youngest and you may earliest decades organizations (after deducting the latest 20 typical terms and conditions from Profile step 1).
Theory Testing of age Variations in Words inside the Matchmaking Pages
To check hypotheses, this new percentage of terms and conditions about matchmaking reputation that fit for every LIWC class offered since the built parameters when you look at the regressions. I examined decades and you may gender while the independent parameters as well as changing having website and you can ethnicity.
Hypothesis step 1: More mature many years is in the a top part of terms regarding pursuing the groups: first-person plural pronouns, loved ones, members of the family, wellness, and you will self-confident feelings.
Conclusions mostly offered Theory step one (discover Desk 2). Five of four regressions shown a serious main impact to have many years, in a manner that because the ages of the fresh profile journalist enhanced, the percentage of terms regarding the group improved throughout the following categories: first-people plural, household members, fitness, and you may positive emotion. I found zero significant many years impact to your ratio of terms regarding the members of the family category.
a beneficial Gender: 0 (female) and you will 1 (male). b Website: The 2 websites was basically dictomously coded because the step one and you can 0. c Ethnicity: 0 (White) and you can step one (Ethnic or racial minority).
an excellent Gender: 0 (female) and you will step one (male). b Webpages: Both other sites had been dictomously coded as the step 1 and you can 0. c Ethnicity: 0 (White) and you may 1 (Cultural otherwise racial minority).