I tested possible differences by website, geographical area, and you will ethnicity having fun with t-examination and you may analysis off difference (ANOVA) to your LIWC group percent. Toward two websites, six of your own a dozen t-examination were tall about after the categories: first-individual only 1 [t(3998) = ?5.61, p Supplementary Desk 2 to possess means, fundamental deviations, and you may contrasts between ethnic teams). Contrasts found significant differences between Light and all of most other cultural organizations in the five of half dozen significant ANOVAs. Thus, we incorporated ethnicity given that an excellent dummy-coded covariate when you look at the analyses (0 = Light, 1 = Any ethnic organizations).
Of your several ANOVA evaluating related to geographic area, only two had been extreme (family unit members and you will self-confident feeling). Since distinctions were not commercially meaningful, i don’t consider geographic region for the subsequent analyses.
Overall performance
Frequency of phrase play with goes without saying inside the descriptive statistics (get a hold of Table step one) and via phrase-clouds. The phrase-affect method portrays many widely used conditions along side whole sample and also in each of the age groups. The definition of-affect program instantly excludes particular terms, along with blogs (an effective, and you may, the) and prepositions (so you can, that have, on). The remaining blogs terms and conditions try scaled sizes in accordance with the frequency, performing an user-friendly portrait quite common posts conditions all over the newest attempt ( Wordle, 2014).
Shape step 1 shows the latest 20 common articles terms found in the complete decide to try. As well as be seen, the quintessential frequently used conditions was indeed love (looking from inside the 67% out of pages), particularly (appearing inside the 62% of profiles), lookin (lookin within the 55% of users), and you will people (appearing for the 50% of users). Hence, the most famous terminology was in fact equivalent across the a long time.
Profile dos shows another 29 typical articles terms within the the tämä viesti fresh youngest and you will earliest age range. By removing the original 20 common articles conditions across the attempt, we show heterogeneity from the relationships pages. In the next 31 terms into the youngest age bracket, high percentage conditions included score (36% out of pages from the youngest age group), go (33% out-of profiles in the youngest age bracket), and you can works (28% out of profiles regarding youngest generation). On the other hand, the fresh new oldest age group had higher percentages out of terminology particularly travel (31% regarding profiles on eldest age bracket), great (24% away from profiles about earliest age bracket), and matchmaking (19% of profiles regarding the oldest age bracket).
2nd 31 most common words from the youngest and you will eldest age communities (after deducting brand new 20 popular terminology regarding Figure step 1).
Theory Assessment of age Variations in Vocabulary in Dating Users
To check on hypotheses, brand new part of words on the dating profile that fit each LIWC category supported as the established details inside the regressions. I checked age and you will gender once the independent variables in addition to adjusting to possess web site and you can ethnicity.
Theory 1: Older many years will be with the increased portion of conditions from the pursuing the kinds: first-people plural pronouns, members of the family, household members, fitness, and you may confident feeling.
Conclusions mainly served Hypothesis 1 (come across Table dos). Four of your own four regressions shown a life threatening fundamental perception getting many years, such that given that period of the newest character publisher enhanced, new part of terms regarding the classification improved throughout the adopting the categories: first-person plural, nearest and dearest, wellness, and you can self-confident feeling. I located zero tall many years feeling on ratio off terms about loved ones class.
a beneficial Gender: 0 (female) and step one (male). b Web site: The two other sites was dictomously coded due to the fact step 1 and you will 0. c Ethnicity: 0 (White) and step one (Ethnic otherwise racial fraction).
a good Gender: 0 (female) and you can step 1 (male). b Webpages: The two websites have been dictomously coded given that step one and you may 0. c Ethnicity: 0 (White) and you will step one (Cultural otherwise racial fraction).
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