I checked prospective distinctions of the webpages, geographic part, and you can ethnicity using t-assessment and you can studies away from difference (ANOVA) on the LIWC class percent. Towards one or two websites, half a dozen of your twelve t-evaluation were significant throughout the adopting the groups: first-people only one [t(3998) = ?5.61, p Additional Desk 2 getting function, important deviations, and you will contrasts ranging from ethnic teams). Contrasts found significant differences between White and all other ethnic teams in four of the six tall ANOVAs. Ergo, i included ethnicity as the an effective dummy-coded covariate during the analyses (0 = Light, step one = Virtually any cultural organizations).
Of one’s a dozen ANOVA assessment pertaining to geographical part, only a couple of have been significant (members of the family and you may positive feeling). Because the distinctions weren’t officially important, i failed to consider geographical area into the next analyses.
Results
Regularity away from term use is obvious during the detailed analytics (select Table 1) and you may via term-clouds. The expression-affect method illustrates many popular conditions over the entire test as well as in each of the age groups. The expression-affect system immediately excludes certain terms and conditions, also articles (a great, and, the) and you will prepositions (so you can, that have, on). The rest content words are scaled in proportions in line with the regularity, undertaking an user-friendly portrait really commonplace articles conditions across the the new shot ( Wordle, 2014).
Profile step one suggests the 20 popular articles terminology utilized in the complete decide to try. As can be seen, the quintessential frequently employed terminology have been love (lookin from inside the 67% away from profiles), such additional reading as for instance (searching inside 62% out of users), lookin (appearing during the 55% off profiles), and you may somebody (appearing for the 50% out of users). Ergo, typically the most popular terminology was equivalent around the age groups.
Shape 2 reveals the second 31 most common content words for the the fresh new youngest and you may oldest a long time. By eliminating the initial 20 well-known posts terms and conditions along the sample, i show heterogeneity from the relationship pages. Within the next 31 terms into youngest generation, high level percentage terms and conditions incorporated get (36% out of users on the youngest age group), go (33% off pages regarding the youngest age group), and you can really works (28% off profiles on youngest age group). Having said that, the latest earliest generation got higher rates of conditions such as traveling (31% out of users regarding eldest generation), higher (24% regarding profiles regarding eldest age group), and you will dating (19% away from pages about eldest age bracket).
2nd 30 most typical terms regarding the youngest and oldest many years organizations (shortly after deducting the fresh new 20 most typical conditions of Shape step one).
Hypothesis Comparison old Differences in Vocabulary from inside the Dating Profiles
To evaluate hypotheses, the fresh portion of words from the matchmaking character that suit for every LIWC category supported while the created parameters in regressions. I examined many years and you can gender as independent variables and additionally changing to have webpages and you will ethnicity.
Hypothesis 1: Earlier age was in the increased part of conditions in the adopting the kinds: first-people plural pronouns, family members, relatives, health, and you will self-confident feelings.
Results largely served Hypothesis 1 (find Dining table 2). Five of one’s four regressions shown a significant main effect for years, in a manner that because the chronilogical age of the newest reputation blogger improved, the newest part of words in the classification enhanced regarding the following categories: first-person plural, family members, health, and you can self-confident feeling. I located zero significant age feeling to the ratio off conditions regarding the nearest and dearest class.
an effective Gender: 0 (female) and you can step one (male). b Website: Both websites were dictomously coded because the step one and you may 0. c Ethnicity: 0 (White) and you may 1 (Ethnic otherwise racial minority).
a good Gender: 0 (female) and you can step 1 (male). b Site: The two websites were dictomously coded since the 1 and 0. c Ethnicity: 0 (White) and you can step 1 (Ethnic or racial fraction).