I checked-out potential distinctions because of the website, geographical part, and you can ethnicity using t-evaluating and you will data off variance (ANOVA) into LIWC group rates. To the two websites, six of the several t-evaluating was basically high on the after the groups: first-people just one [t(3998) = ?5.61, p Second Dining table dos for means, important deviations, and you may contrasts anywhere between cultural communities). Contrasts revealed significant differences when considering White and all other cultural groups into the four of your own six significant ANOVAs. For this reason, i incorporated ethnicity once the an effective dummy-coded covariate inside analyses (0 = Light, step one = Any kind of ethnic organizations).
Of your own a dozen ANOVA tests about geographical region, only one or two was basically extreme (nearest and dearest and you can confident feeling). Since distinctions weren’t technically significant, we failed to think geographic part into the then analyses.
Abilities
Volume regarding term have fun with is evident inside descriptive statistics (find Dining table 1) and you will through word-clouds. The expression-affect approach portrays more commonly used conditions along the entire sample plus each one of the age range. The phrase-affect program automatically excludes specific words, in addition to posts (a good, and, the) and you will prepositions (so you can, having, on). The rest blogs conditions was scaled in dimensions according to the volume, starting an intuitive portrait really common articles words around the this new try ( Wordle, 2014).
Shape step 1 suggests the new 20 common stuff words included in the complete attempt. As can rise above the crowd, the quintessential frequently employed words was basically like (searching from inside the 67% regarding pages), such (appearing for the 62% of pages), searching (looking during the 55% regarding pages), and you can people (lookin into the fifty% out-of pages). Therefore, the preferred terms had been equivalent round the age groups.
Contour dos shows another 31 popular stuff words when you look at the the fresh youngest and you can earliest a long time. By detatching the first 20 well-known articles words along side attempt, i train heterogeneity about dating pages. Next 31 terms towards the youngest age group, high level percentage words included score (36% regarding pages about youngest age group), wade (33% out of profiles about youngest age group), and you may performs (28% out of profiles on the youngest generation). Alternatively, the eldest generation had higher percent out of words particularly traveling (31% regarding pages from the earliest age group), great (24% from pages throughout the eldest age group), and matchmaking (19% off profiles in the oldest age group).
2nd 31 typical terms and conditions from the youngest and you may eldest years organizations (immediately following subtracting the fresh new 20 popular conditions out-of Shape step one).
Hypothesis Testing of age Differences in Code when you look at the Relationship Profiles
To evaluate hypotheses, the portion of terms and conditions in the dating character that fit for every single LIWC group supported as created variables into the regressions. We tested many years and you will gender because the independent parameters as well as changing to own web site and you can ethnicity.
Theory 1: Old decades might be for the a top part of terms and conditions regarding following the groups: first-people plural pronouns, family, members of the family, health, and you may confident feeling.
Results mostly supported Hypothesis step one (get a hold of Desk dos). Four of four regressions revealed a significant fundamental impression having many years, in a manner that because ages of the latest character writer enhanced, the brand new part of terms and conditions regarding the group improved about following categories: first-individual plural, family members, fitness, and you will self-confident feeling. We located no extreme ages impact to the ratio regarding conditions on relatives group.
an effective Gender: 0 (female) and you can step 1 (male). b Webpages: The two websites was indeed dictomously coded since the step 1 and you may 0. c Ethnicity: 0 (White) and you can step 1 (Cultural otherwise racial minority).
an effective Gender: 0 (female) and you can their website 1 (male). b Website: The 2 websites have been dictomously coded since 1 and you may 0. c Ethnicity: 0 (White) and you may step one (Cultural otherwise racial fraction).
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