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I tested prospective differences by the web site, geographic region, and ethnicity playing with t-assessment and you may investigation off variance (ANOVA) with the LIWC group rates. With the a few other sites, six of twelve t-screening was in fact high on the following groups: first-individual only 1 [t(3998) = ?5.61, p Second Desk dos for means, basic deviations, and you may contrasts ranging from cultural groups). Contrasts revealed tall differences between Light and all of most other cultural communities inside five of half a dozen tall ANOVAs. Hence, we included ethnicity due to the fact an excellent dummy-coded covariate inside the analyses (0 = White, 1 = All other cultural communities).
Of your several ANOVA examination associated with geographical area, just several have been high (family relations and you will confident feeling). Because the distinctions were not theoretically meaningful, i don’t consider geographical part into the after that analyses.
Frequency of keyword have fun with is obvious when you look at the descriptive statistics (look for Dining table step one) and you can through keyword-clouds. The term-affect approach depicts by far the most commonly used words along the whole sample and in each of the age range. The term-cloud system immediately excludes particular terms, and blogs (a, and you may, the) and you may prepositions (so you’re able to, which have, on). The remaining content words was scaled in size relative to their regularity, creating an intuitive portrait of the most commonplace content terminology across the the new attempt ( Wordle, 2014).
Profile step 1 reveals new 20 most frequent content terminology used in the complete try. As can get noticed, 
one particular frequently employed terms were like (searching into the 67% out-of users), including (searching for the 62% off users), searching (looking during the 55% out of users), and individuals (searching inside the fifty% regarding users). Therefore, the preferred terminology was in fact comparable round the age range.
Profile dos suggests the second 30 common articles conditions inside the the youngest and you can eldest age groups. By removing the first 20 popular content terminology along side decide to try, we train heterogeneity from the relationships users. Within the next 29 terms towards the youngest age bracket, raised percentage terms and conditions integrated rating (36% away from profiles from the youngest age bracket), wade (33% of profiles from the youngest age group), and you will works (28% of users regarding youngest age group). Having said that, the brand new oldest age group got large proportions out-of words including take a trip (31% from profiles regarding oldest age bracket), high (24% out-of pages on eldest generation), and you will relationship (19% of users from the eldest generation).
2nd 30 most commonly known terminology throughout the youngest and you can oldest decades teams (immediately after subtracting the fresh new 20 most typical terms regarding Figure step 1).
To test hypotheses, the percentage of terms and conditions on the dating reputation that suit for each LIWC classification served given that established parameters inside regressions. We looked at decades and you may sex while the separate details plus modifying for website and you may ethnicity.
Hypothesis step one: Elderly many years is in the a higher part of words regarding following categories: first-people plural pronouns, friends, members of the family, health, and you will self-confident feelings.
Conclusions largely offered Hypothesis step one (select Desk 2). Five of your five regressions shown a life threatening head impact to possess decades, in a manner that once the chronilogical age of the fresh new reputation writer increased, the brand new percentage of terminology from the classification improved regarding the following the categories: first-individual plural, relatives, fitness, and you will self-confident emotion. We receive no significant many years feeling for the ratio off conditions on the family group.
an intercourse: 0 (female) and you will 1 (male). b Web site: The 2 other sites was indeed dictomously coded while the step one and you will 0. c Ethnicity: 0 (White) and you can step 1 (Cultural otherwise racial minority).
a gender: 0 (female) and you will step one (male). b Web site: Both other sites have been dictomously coded since step 1 and you can 0. c Ethnicity: 0 (White) and 1 (Ethnic otherwise racial minority).
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