We checked potential variations from the website, geographical region, and ethnicity using t-testing and you will data away from difference (ANOVA) to your LIWC class percent. To the two websites, six of your own 12 t-evaluation were extreme on the after the kinds: first-people only one [t(3998) = ?5.61, p Supplementary Table dos having form, fundamental deviations, and you can contrasts ranging from ethnic groups). Contrasts shown high differences when considering White and all most other ethnic teams inside the four of one’s six tall ANOVAs. Thus, i provided ethnicity given that an excellent dummy-coded covariate from inside the analyses (0 = Light, 1 = Any kind of cultural communities).
Of your several ANOVA examination related to geographical region, merely two were tall (family and you may self-confident feelings). While the variations just weren’t theoretically important, i don’t envision geographical part inside next analyses.
Overall performance
Volume out-of phrase have fun with is obvious inside the detailed analytics (come across Table step 1) and thru term-clouds. The term-cloud techniques illustrates the essential popular terms and conditions along the whole take to plus in each of the a long time. The term-cloud system immediately excludes particular conditions, also content (a great, and, the) and you will prepositions (so you’re able to, which have, on). The remaining blogs terms and conditions is actually scaled in proportions according to the regularity, creating an intuitive portrait really commonplace blogs conditions round the this new sample ( Wordle, 2014).
Profile step one suggests the fresh new 20 most frequent stuff terms utilized in the whole sample. As well as get noticed, the quintessential commonly used conditions have been love (looking inside 67% from profiles), including (looking inside 62% away from profiles), lookin (searching in 55% out-of pages), and you may some one (appearing for the 50% out-of profiles). Hence, widely known terms and conditions had been equivalent across the a long time.
Profile 2 reveals the next 31 common content terms during the the fresh new youngest and you can oldest age groups. By removing the original 20 prominent blogs terminology over the sample, i teach heterogeneity on the matchmaking users. Within the next 30 terms into the youngest age bracket, high percentage words integrated rating (36% out-of profiles about youngest age bracket), go (33% away from users regarding the youngest age group), and you can functions (28% regarding profiles on the youngest generation). Conversely, the newest oldest age bracket got higher percent out-of terms instance travel (31% away from users on oldest age group), higher (24% from profiles in the eldest generation), and you will matchmaking (19% away from profiles on the eldest generation).
2nd 31 most typical terms in the youngest and you will oldest ages groups (immediately after deducting this new 20 common terms and conditions off Contour step 1).
Hypothesis Investigations of age Variations in Code in Dating Pages
To check hypotheses, the new percentage of conditions throughout the relationships reputation that fit for each LIWC classification supported once the centered parameters inside the regressions. I looked at decades and gender once the independent variables as well as changing to have web site and you can ethnicity.
Hypothesis step one: Older decades would be of the a high percentage of conditions in the after the groups: first-individual plural pronouns, family relations, members of the family, wellness, and you will self-confident feelings.
Conclusions mainly offered Hypothesis step 1 (see Dining table dos). Five of four regressions shown a significant head impact getting age, such that because the ages of the reputation blogger enhanced, the new part of terms regarding the category improved about pursuing the categories: first-people plural, family, wellness, and you will self-confident feeling. We found no tall ages impression towards proportion regarding terminology about family members class.
a Gender: 0 (female) and you can 1 (male). b Webpages: The 2 other sites was in fact dictomously coded once the 1 and you will 0. c Ethnicity: 0 (White) and you will step 1 (Ethnic otherwise Macau in China brides racial minority).
a great Gender: 0 (female) and you will 1 (male). b Web site: The two other sites had been dictomously coded because the step one and you can 0. c Ethnicity: 0 (White) and you will step 1 (Ethnic or racial fraction).
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