I transcribed the brand new parents’ verbatim solutions towards a keyword processor chip (in cases like this, WordPerfect six.0). For each and every description was followed closely by a single hard go back. Shape step one suggests the initial three definitions in our grasp file (Master.WP).

To helps investigation, we split for each and every unique terminology/descriptor of the a period and you may a space. That time/space combination has actually several advantages. Earliest, a period implies the end of a sentence, therefore can then utilize the keyword processor chip or concept checker in order to number what number of phrases in a file (Harris 1996). Next, we could make use of the months once the an excellent delimiter having posting the fresh new text study into an effective spreadsheet or a databases (such as for instance Do well otherwise Quattro Professional).

Once the i continuously generated parent’s gender brand new next word-of the fresh part, we can do that with the help of our word processor chip. Select all text message, and you can tell the expression processor to use new fourth phrase to kinds new emphasized paragraphs. (1) (Before sorting, backup the document.)

For every single respond to, i keyed in the family identification number, the type of family members, the brand new gender of the son being described, the fresh gender of mother or father whom offered the fresh dysfunction, therefore the done dysfunction

I following duplicated mothers’ and you may fathers’ solutions to split up data free BBW sex dating (Mother.Wordpress Dad.WP). Thus far we were only looking the fresh new descriptors, therefore we stripped from the extraneous suggestions from inside the for every single file. The a couple removed documents contains just the verbatim definitions provided with mothers and fathers.

We put WordPerfect’s document advice function to help you estimate certain standard analytics. (2) Document information is located less than File over the top menu. On top of other things, they exercise how many letters, words and phrases, additionally the average word duration, the typical quantity of conditions per phrase, while the limitation conditions for each sentence. Desk 1 compares these types of statistics getting mothers’ and you will fathers’ responses.

This might be effortlessly partial-automatic with a macro you to goes toward the beginning of per part and you will deletes the initial five terms (ID, family unit members types of, kid’s and you may parent’s gender,)

1) Parents fool around with significantly more conditions to describe kids than just manage dads. Of all conditions regularly explain the brand new 82 students, 56% come from parents and 49% come from dads.

2) Typically, parents put twenty-eight% a whole lot more phrases than just performed boys. [Mothers utilized =6.4 sentences to describe their children, when you are males used =5.0 sentences. Parents use the exact same amount of terminology each sentences, however, parents said a whole lot more aspects of kids.]

Dads and you may moms and dads become more similar within decide to try than it vary. Mothers play with a whole lot more terminology, not definitely way more, as well as on other actions, fathers and you can moms and dads are about equivalent. Obviously, mothers made use of the exact same “simple societal science survey outline” to respond to all of our inquiries — writing some terse phrases and words to possess one minute approximately.

We next glance at whether or not moms and dads have fun with various other conditions in order to define kids. Terms 2.0 (Johnson 1995) is a useful system that counts just how many powering words for the a text, means the amount of unique terminology variations, and listing the amount of events each and every unique means. (3) (Select Bernard 1995 to possess a review of Terms 2.0.) Other companies, such as CATPAC, also matter the brand new volume off novel conditions. (Get a hold of Doerfel and Barnett 1996 getting a glance at CATPAC).

To find the data ready to have Conditions dos.0, i very first saved our very own WordPerfect data (Mom.Word press and Dad.WP) into the ASCII format (calling her or him Mother.ASC and Dad.ASC so as never to overwrite the first data). Once we examined for each and every document, i utilized Conditions 2.0’s “preferred term list” so you’re able to exclude 125 of the very most-put English words. Shape dos reveals the main several outputs. Each efficiency informs us how many terms and conditions for each and every document contains in the first place, (4) exactly how many novel words had been discovered (and additionally unique common terminology), and exactly how of a lot terminology were got rid of once we eliminated an average of them. Conditions 2.0 outputs the list of unique terms and conditions employing respective volume out of occurrence. I mean the new rank purchase of any phrase according to the # signal. (This can be done on your own word processor by-turning into the range numbering option.) (5)