We tested possible variations because of the webpages, geographic part, and you can ethnicity playing with t-tests and you can studies regarding difference (ANOVA) for the LIWC group proportions. To your several other sites, half a dozen of twelve t-screening was basically high regarding the after the kinds: first-person just one [t(3998) = ?5.61, p Secondary Dining table dos having form, basic deviations, and you may contrasts between cultural organizations). Contrasts found high differences between Light and all other ethnic communities when you look at the four of the six tall ANOVAs. Thus, we integrated ethnicity since the good dummy-coded covariate when you look at the analyses (0 = Light, step 1 = Every other ethnic communities).
Of several ANOVA screening about geographic area, just several was indeed extreme (members of the family and you may confident feeling). Since variations weren’t officially important, we don’t envision geographic area when you look at the further analyses.
Abilities
Volume of word play with goes without saying in the descriptive statistics (discover Table step 1) and you will via phrase-clouds. hot Aran women The phrase-cloud approach illustrates the most popular terminology along the whole sample and also in each one of the age groups. The term-cloud program instantly excludes certain conditions, also articles (an excellent, and, the) and prepositions (in order to, with, on). The remainder stuff words try scaled in size in line with the frequency, performing an intuitive portrait of the most extremely common stuff words around the new attempt ( Wordle, 2014).
Figure step one suggests the fresh new 20 most frequent blogs conditions utilized in the entire take to. As can get noticed, by far the most commonly used terms and conditions was basically love (searching from inside the 67% out-of pages), like (lookin into the 62% of pages), lookin (looking in the 55% out of profiles), and anybody (looking inside the fifty% regarding profiles). For this reason, typically the most popular terminology had been equivalent around the age groups.
Contour 2 reveals next 31 common stuff words from inside the the fresh youngest and you can earliest age range. By eliminating the initial 20 preferred articles conditions along the shot, i instruct heterogeneity on the relationship users. Next 30 terminology into youngest age bracket, raised percentage terminology integrated score (36% regarding pages in the youngest age group), wade (33% of profiles on youngest age group), and works (28% out-of users from the youngest age bracket). Alternatively, the fresh earliest age group had higher percentages out-of words such as travel (31% of profiles regarding eldest generation), higher (24% off profiles in the eldest age group), and dating (19% off users regarding oldest age bracket).
Second 29 common terminology regarding youngest and you can eldest years groups (just after subtracting this new 20 popular terms and conditions away from Shape step 1).
Hypothesis Analysis old Differences in Words inside the Dating Profiles
To check on hypotheses, brand new part of conditions about matchmaking profile that fit for every single LIWC category offered due to the fact situated variables inside the regressions. I looked at decades and you can gender because separate details plus modifying to have website and you will ethnicity.
Hypothesis step 1: Older ages was of this a top portion of words in the following categories: first-individual plural pronouns, family members, friends, health, and self-confident feeling.
Results mostly supported Theory step one (find Table 2). Four of one’s five regressions shown a significant main impact for age, in a fashion that because age of the fresh reputation creator enhanced, brand new portion of words on class improved throughout the adopting the categories: first-individual plural, relatives, health, and self-confident feeling. We discovered no significant age impact towards the ratio from terms and conditions about nearest and dearest classification.
a Gender: 0 (female) and you will 1 (male). b Web site: The two websites was in fact dictomously coded just like the step one and you may 0. c Ethnicity: 0 (White) and you will step one (Cultural otherwise racial minority).
a Gender: 0 (female) and step 1 (male). b Webpages: Both other sites had been dictomously coded just like the 1 and 0. c Ethnicity: 0 (White) and you can 1 (Ethnic otherwise racial fraction).