We looked at potential differences because of the webpages, geographical part, and you will ethnicity playing with t-evaluating and analysis out-of difference (ANOVA) towards the LIWC classification rates. Towards the two other sites, half a dozen of the a dozen t-evaluation had been extreme on the after the kinds: first-person one [t(3998) = ?5.61, p Secondary Desk 2 for setting, important deviations, and contrasts ranging from ethnic communities). Contrasts revealed significant differences kissbridesdate.com/fi/etiopian-naiset/ between Light and all sorts of other cultural groups within the five of the half a dozen high ANOVAs. Thus, i incorporated ethnicity because the a great dummy-coded covariate in the analyses (0 = Light, step 1 = Every other cultural organizations).
Of several ANOVA examination pertaining to geographic area, simply a couple was indeed tall (relatives and you can self-confident emotion). Given that variations were not technically important, i failed to think geographical region when you look at the subsequent analyses.
Results
Volume out-of phrase explore is obvious for the detailed statistics (see Table 1) and through phrase-clouds. The definition of-cloud strategy depicts the most popular terms and conditions over the whole test and in all the age groups. The phrase-affect system automatically excludes certain terms, and stuff (a good, and you may, the) and prepositions (to, with, on). The remainder articles words try scaled in proportions in accordance with its volume, performing an user-friendly portrait quite commonplace stuff terms and conditions across the brand new sample ( Wordle, 2014).
Shape step one shows the new 20 most commonly known posts terms used in the entire shot. As can be seen, many commonly used terminology was basically love (looking in 67% from profiles), instance (lookin for the 62% off users), lookin (searching from inside the 55% out-of profiles), and you will some one (appearing for the fifty% out of users). Therefore, typically the most popular words was equivalent round the a long time.
Profile 2 shows the following 29 common blogs terms when you look at the the fresh new youngest and you will earliest a long time. By detatching the initial 20 common blogs words along the sample, we instruct heterogeneity throughout the dating users. Within the next 31 terminology towards youngest generation, raised percentage terms and conditions incorporated rating (36% from pages regarding youngest age bracket), wade (33% away from users regarding the youngest generation), and you can works (28% off profiles on the youngest age bracket). Conversely, the latest oldest age group got high rates out-of words eg travelling (31% off profiles regarding oldest generation), higher (24% from users in the earliest generation), and you may matchmaking (19% regarding profiles from the eldest generation).
Next 29 most frequent terminology on youngest and you may earliest decades organizations (just after deducting the latest 20 most commonly known terms out of Profile step one).
Hypothesis Review old Differences in Words for the Matchmaking Pages
To test hypotheses, the brand new percentage of words on matchmaking profile that suit for each LIWC class offered due to the fact created variables when you look at the regressions. We examined years and you may gender as the separate variables together with modifying having site and ethnicity.
Hypothesis step 1: Elderly many years would be regarding the increased portion of words regarding the following the classes: first-individual plural pronouns, friends, family relations, fitness, and self-confident feeling.
Conclusions mainly supported Hypothesis 1 (pick Dining table 2). Five of four regressions revealed a significant head impression to possess decades, in a fashion that because the chronilogical age of the new reputation author increased, the brand new portion of words on the class enhanced throughout the pursuing the categories: first-people plural, family members, wellness, and positive emotion. We discovered no tall decades feeling into ratio off terms regarding family class.
a great Gender: 0 (female) and you may step one (male). b Website: Both websites have been dictomously coded once the step one and you can 0. c Ethnicity: 0 (White) and step one (Cultural or racial fraction).
good Gender: 0 (female) and you will step 1 (male). b Site: The 2 other sites was indeed dictomously coded because 1 and you will 0. c Ethnicity: 0 (White) and you may 1 (Cultural or racial fraction).
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