I examined potential differences because of the web site, geographic region, and you will ethnicity using t-evaluating and you can analysis regarding variance (ANOVA) for the LIWC class rates. Towards the several websites, six of your own several t-tests was in fact high about after the categories: first-person only one [t(3998) = ?5.61, p Second Dining table 2 to possess means, fundamental deviations, and you can contrasts anywhere between ethnic organizations). Contrasts shown extreme differences when considering White as well as almost every other cultural organizations inside five of your half dozen extreme ANOVAs. Ergo, we included ethnicity since an excellent dummy-coded covariate inside analyses (0 = White, 1 = Every other ethnic teams).
Of one’s twelve ANOVA screening pertaining to geographic region, merely one or two was in fact high (relatives and confident emotion). Once the differences were not commercially important, we don’t thought geographic part in further analyses.
Abilities
Volume out-of keyword explore goes without saying during the descriptive statistics (discover Desk 1) and you will via keyword-clouds. The word-affect strategy illustrates the quintessential commonly used words across the whole decide to try plus in all the age groups. The phrase-cloud system immediately excludes specific words, in addition to blogs (a beneficial, and, the) and you may prepositions (in order to, which have, on). The rest articles terminology was scaled sizes relative to their frequency, undertaking an user-friendly portrait of the most extremely commonplace blogs terms and conditions round the the fresh take to ( Wordle, 2014).
Shape 1 reveals the 20 common content terms and conditions found in the whole take to. As well as get noticed, many frequently used terms and conditions was in fact love (searching from inside the 67% off profiles), eg (searching inside 62% of profiles), searching (appearing for the 55% away from users), and anyone (looking in 50% regarding users). Therefore, widely known terms and conditions had been comparable across age groups.
Contour dos reveals next 29 most frequent blogs terminology in new youngest and you can oldest age groups. By removing the original 20 popular posts terms along the sample, we illustrate heterogeneity regarding the relationships profiles. Next 31 terms and conditions towards the youngest generation, high percentage terminology integrated get (36% regarding profiles on youngest age bracket), wade (33% out of pages from the youngest generation), and you will functions (28% off profiles in the youngest age bracket). Conversely, new eldest age bracket had large proportions off terms particularly traveling (31% away from users on eldest age group), higher (24% of pages regarding oldest age group), and you can matchmaking (19% out of pages regarding oldest age bracket).
Second 29 typical terminology about youngest and you can oldest years teams (once deducting the 20 typical Salinas, PR brides match conditions of Figure 1).
Hypothesis Investigations old Variations in Code in the Relationship Users
To evaluate hypotheses, the fresh new percentage of terms and conditions about relationship profile that suit for each and every LIWC category supported given that built details during the regressions. We tested age and gender since separate variables and additionally modifying for web site and you may ethnicity.
Theory step one: Elderly years is associated with the a high percentage of terminology from the following the classes: first-person plural pronouns, members of the family, family, health, and you will positive feelings.
Findings mostly served Theory step 1 (discover Table 2). Four of the four regressions shown a critical main perception to have many years, such that just like the period of the fresh new character publisher enhanced, the latest part of terms on class increased from the after the categories: first-people plural, loved ones, wellness, and you can positive feeling. I found no significant years effect to the ratio of terms in the family group.
a great Gender: 0 (female) and you may step 1 (male). b Web site: Both websites was dictomously coded because 1 and 0. c Ethnicity: 0 (White) and you will step one (Ethnic or racial minority).
a great Gender: 0 (female) and you will step 1 (male). b Webpages: Both websites was basically dictomously coded given that step 1 and you can 0. c Ethnicity: 0 (White) and you will step 1 (Cultural or racial fraction).