Outcomes
Descriptive Statistics and Illustrations of Widely Used Terms
Frequency of word usage is evident in descriptive data (see dining dining Table 1) and via word-clouds. The word-cloud strategy illustrates the absolute most widely used terms over the whole sample and in each one of the age brackets. The word-cloud system automatically excludes words that are certain including articles (a, and, the) and prepositions (to, with, on). The rest of the content terms are scaled in proportions in accordance with their regularity, producing an intuitive portrait of the most extremely commonplace content words throughout the sample ( Wordle).
Figure 1 shows the 20 most frequent content words utilized in the sample that is entire. As can be viewed, the absolute most frequently employed terms had been love (showing up in 67per cent of pages), like (appearing in 62per cent of pages), looking (showing up in 55per cent of pages), and somebody (showing up in 50per cent of profiles). Therefore, probably the most words that are common comparable across age brackets.
Twenty most frequent content terms throughout the whole test.
Figure 2 shows the second 30 most frequent content terms when you look at the youngest and earliest age ranges. By detatching the initial 20 content that is common over the test, we illustrate heterogeneity within the dating pages. Within the next 30 words for the age group that is youngest, raised percentage words included get (36% of pages when you look at the youngest age bracket), get (33% of pages within the youngest age bracket), and work (28% of pages within the youngest age bracket). On the other hand, the earliest generation had greater percentages of words such as for instance travel (31% of pages within the earliest age bracket), great (24% of pages within the earliest https://besthookupwebsites.org/be2-review/ age bracket), and relationship (19% of pages into the earliest age bracket).
Next 30 most typical terms within the youngest and age groups that are oldest (after subtracting the 20 most typical terms from Figure 1).
Next 30 most typical terms when you look at the youngest and age groups that are oldest (after subtracting the 20 most frequent terms from Figure 1).
Hypothesis Testing of Age variations in Language in Dating pages
The percentage of words from the dating profile that fit each LIWC category served as the dependent variables in regressions to test hypotheses. We examined age and sex as separate factors along with adjusting for site and ethnicity.
Hypothesis 1: Older age should be connected with a greater portion of terms when you look at the following categories: first-person plural pronouns, household, buddies, wellness, and good feeling.
Findings mostly supported Hypothesis 1 (see Table 2). Four regarding the five regressions unveiled a substantial primary impact for age, so that whilst the chronilogical age of the profile writer increased, the portion of terms when you look at the category increased when you look at the following categories: first-person plural, buddies, wellness, and emotion that is positive. We discovered no significant age impact for the percentage of words when you look at the household category.
Regression research Predicting Percentage of Words in Linguistic Inquiry and Word Count (LIWC) groups (theory 1)
a Gender: 0 (female) and 1 (male). b internet site: the 2 sites had been dictomously coded as 1 and 0. c Ethnicity: 0 (White) and 1 (cultural or racial minority).
Regression research Predicting Percentage of Words in Linguistic Inquiry and Word Count (LIWC) groups (Hypothesis 1)
a Gender: 0 (feminine) and 1 (male). b web site: the 2 sites had been dictomously coded as 1 and 0. c Ethnicity: 0 (White) and 1 (cultural or racial minority).
Hypothesis 2: young age will likely to be connected with a greater portion of terms within the following categories: first-person singular pronouns, work, accomplishment, cash, attractiveness, sex, and emotion that is negative.
We discovered mixed help for Hypothesis 2 (see dining dining Table 3). Four of this seven regressions unveiled a pattern in line with hypotheses, so that while the chronilogical age of the profile writer increased, the portion of terms into the category reduced. Young grownups revealed greater percentages of terms within the first-person singular, work, success, and emotion that is negative. The model for the group of cash revealed a substantial effect that is main of in the contrary way of predictions, so that as age increased, so did the portion of terms into the money category. The models for sexuality and attractiveness groups would not show significant aftereffects of age.
Regression research Predicting Percentage of Words in Linguistic Inquiry and Word Count (LIWC) groups (theory 2)
a Gender: 0 (feminine) and 1 (male). b site: the 2 sites had been dictomously coded as 1 and 0. c Ethnicity: 0 (White) and 1 (cultural or racial minority).
Regression research Predicting Percentage of Words in Linguistic Inquiry and Word Count (LIWC) groups (theory 2)
a Gender: 0 (female) and 1 (male). b internet site: the 2 sites had been dictomously coded as 1 and 0. c Ethnicity: 0 (White) and 1 (cultural or racial minority).
Regressions additionally unveiled significant sex distinctions when you look at the percentage of terms in appropriate LIWC categories. For instance, ladies had an increased portion of terms into the first-person single category, whereas guys had a greater portion of terms when you look at the first-person plural category. Guys had greater proportions of terms within the ongoing work category. Ladies had greater proportions of terms within the types of buddies, family members, wellness, sex, and good feeling. No significant sex distinctions had been based in the kinds of accomplishment, cash, attractiveness, or emotion that is negative.