Children’s Dreams: A Word Search Analysis (part 2)

Children's Dreams: A Word Search Analysis (part 2) by Kelly BulkeleyOnce you’re ready to perform a word search analysis—once you’ve formulated a question, chosen a dream series, and acknowledged the limits of your approach—you have to decide the length of the dream reports you’re going to study.

 If you search for reports of any length, your results will include lots of short reports saying “none,” “no dream,” etc.  You’ll also get answers like “dreamed of a whale,” or “plane crash,” reports so short that it’s hard to work with them. You might also get super-long reports with elaborately detailed descriptions and additional waking commentary, which are also hard to work with.

 Unless you specifically want to study the shortest or longest reports, my advice is to set minimum and maximum word lengths for your searches. 

 I started by setting the searches for dreams between 20 and 300 words.  That gave me 622 reports to study.  After I learn more about the series I’ll look at the shorter and longer dreams and find a way to integrate them into the analysis.

 One factor I’m always thinking about is how to make my findings commensurable with those of other researchers. For example, the Hall and Van de Castle content analysis system, which has been used as a base of comparison by many researchers, focuses on dream reports between 50 and 300 words in length. Eventually I’ll look at that narrower range of reports, but at the beginning of my analysis I want to cast a wider net and include more reports in my initial assessment, hence the lower minimum length.

 So, where to start the word searching? 

 My immediate, admittedly vain concern was to know whether I was right or wrong about a recent prediction I made about this dream series.

 A couple weeks ago I wrote a post about people’s dreams of Harry Potter, drawing on results from another survey on highly memorable dreams I commissioned from Harris Interactive.  In that survey 1003 American adults 18 years and older reported dreams between 20 and 300 words in length, and I found two reports using at least one of the following words:

 “harry potter” voldemort hogwarts hagrid dumbledore malfoy snape hermione draco

At the end of that post I predicted there would be more HP-related dreams in the Harris children’s survey, which I had not yet studied.  Now that I have the children’s survey uploaded into the SDDB, I can quickly put that prediction to the test. 

 Of the 622 dreams between 20 and 300 words in the children’s survey, 6 of the dreams used at least one of these HP-related words, 1% of the total vs. 0.2% in the adult survey.

 It’s not an epic difference, but it’s statistically significant (p=.036), and it makes sense in terms of the different roles the HP novels have played in the waking lives of children vs. adults.      

 OK, I wasn’t completely wrong.  That’s the first thing I wanted to settle.

Part Three:Using the pre-set template of 40 word categories

Children’s Dreams: A Word Search Analysis (part 1)

Children's Dreams: A Word Search Analysis (part 1) by Kelly BulkeleyI’ve just begun a new project using word search methods to study dream reports from children and adolescents.  I thought that showing in real time the steps of my analytic process might help other people learn how to apply these methods to their own dream studies.

Any research project starts with a question.  In this case my question was about “big dreams” in childhood (the subject of a book-in-progress).  I wanted to know more about recurrent patterns in the dreams that children and adolescents remember most vividly.  Other researchers like David Foulkes have studied normal, average dream patterns in children, but my question focused on the distinctive features of highly memorable dreams in the early stages of life

Earlier this spring I contacted Harris Interactive, an opinion research company, regarding their “YouthQuery” survey, which enables a researcher to ask a single question and receive online answers from @1000 American children ages 8-18, along with a few other demographic data points.  (The cost of this survey, while considerable, was no more than I’ve paid research assistants to help with other projects in the past.)

There are pros and cons to online surveys.  On the downside, it’s impossible to validate a person’s answers, and it favors participants who are educated and affluent enough to use computers.  On the upside, participants can give their answers in a private setting in their own words, which is extra valuable for a word search approach.

I try not to let excessive angst about methodology slow me down.  Every study has its limits.  Once you’ve identified them, you move on and do the work.  I’m more interested in discovering what a method can do rather than dwelling on what it can’t do.

The Harris people and I decided to word the survey question as follows:

“We are interested in hearing about a dream that you had and remember a lot about.  Please try to tell us everything you remember about the dream, including where you were, who else was there, what happened, how you felt, what you were thinking during the dream and how it ended.  Please also tell us about how old you were when you had the dream.”

The other questions asked in the standard YouthQuery survey regarded age, gender, race/ethnicity, current grade in school, school location (urban, suburban, rural), and school type (public, private, parochial).

Harris conducted the survey in early April, and then I uploaded the info (thanks to Kurt Bollacker) into the sleep and dream database (SDDB).  The dream reports and answers to the other questions can be seen at:

Making this information publicly available enables others to check my work and test my claims, always a good thing in empirical research.  More importantly, it allows other researchers to explore facets of the data beyond what I or any single analyst can pursue.

Dream researchers have operated for too long with isolated sources of data that never receive more than one investigator’s systematic attention.  Digital databases can help our field move forward into a more dynamic and collaborative future.

Next: testing my first predictions