Children’s Dreams: A Word Search Approach (part 3)

Children's Dreams: A Word Search Approach (part 3) by Kelly BulkeleyIn a couple of recent articles (here’s one) I’ve developed and tested a set of 40 word categories to help in studying dreams via word searches. These categories are still a work in progress, but already they’ve proven useful in accurately mapping out some basic content patterns in people’s dreams.

A template with these 40 word categories is built into the SDDb search function, to make it easy to get results that can be compared to other sources.  You can search for other words if you choose, but it’s quicker to use the template if you want to gain an overview of the whole dream series.

It took me 26 minutes to perform the searches and record the results in a separate excel spreadsheet. The searches for “animals,” “friendliness” and “physical aggression” took the longest time to process because these categories include the largest numbers of words.

With more practice the searches could be performed even faster, and eventually the whole process could be automated, but a lot of preliminary work needs to be done before that’s a technological step worth taking.

Now I have a page of statistics showing how often certain categories of words appear in the dreams of this group of 622 children and adolescents.  What can be done with this information?

I’ll start by matching it to the word frequencies of the Hall and Van de Castle “norm dreams,” a set of 490 dreams from 100 college females and 491 dreams from 100 college males, available for study on Bill Domhoff’s website http://www.dreamresearch.net/  Although not a perfect sample of human dreaming, the HVDC norm dreams are the most widely used measuring stick for analyzing patterns of dream content.  I’d like to know, at this early stage of the process, how the children’s dreams compare to the HVDC norm dreams on basic features of content.

Ultimately I’d like to develop a better baseline for comparing different dream series.  Until then, I’m using the HVDC norms.

I go into the comparison with a number of expectations:

1. The children’s dreams will have lower frequencies overall, due to the inclusion of shorter dreams (20 minimum vs. 50 minimum in HVDC) and their immature writing skills and cognitive development relative to the HVDC college students.

2. The children’s dreams will have the same “ur-patterns” I’ve found via word searches in many other series of dreams: sight is the most frequent perception, smell and taste the lowest; fear is the most frequent emotion and sadness the least; there’s more aggression than sexuality, more family than animal references, more water than other elements, more falling than flying, and a high overall frequency of speech.  These are over-arching patterns I’ve found in virtually all other series, and I expect they will be present in the children’s dreams, too.

3. Because the HVDC dreams are “most recent” whereas the children’s dreams are “highly memorable,” I anticipate the children’s dreams will have more “primal” qualities such as nightmarish emotions, nature references, physical aggression, family characters, imaginary/fantastic beings, and magical activities (consistent with findings from a study I’ve just done with Ernest Hartmann comparing most recent and most memorable dreams).

I’m going to hold off on any strong conclusions until I’ve divided the children’s dreams by gender and performed another round of word searches.  To start I want a big picture of the whole set, but later I’ll take gender differences into account.

That’s a lot of preamble!  Let’s get to it and look at the first class of word categories in the SDDb, “Perception.”

Harris YQ HVDC Males HVDC Females
(N=622) (N=491) (N=490)
Perception
Vision 20.3 37.9 47.1
Hearing 4.2 12.2 12.7
Touch 2.4 6.5 8.4
Smell 0.3 1 0.4
Taste 0.7 1 1.4
Intensity 14.8 34.6 46.3
Chromatic color 5 7.9 17.1
Achromatic color 4.3 7.5 11.4
Aesthetic evaluation 13.3 12.6 20.2

This is pretty much what I expected: lower frequencies overall, but still following the same patterns of relative distribution, with vision highest and smell and taste lowest.  I’m surprised there’s not more color or intensity.

Here’s the second SDDb class of word categories, “Emotion.”

  Harris YQ HVDC Males HVDC Females
(N=622) (N=491) (N=490)
Emotion
Fear 19.6 16.1 27.8
Anger 3.1 6.7 9.8
Sadness 4.7 2.2 4.9
Confusion 2.3 7.5 10.2
Happiness 9.7 6.1 10.8

Here it gets a little more interesting.  The overall frequencies are lower among the children, but not as much as with the perceptions.  Fear is highest as expected, but sadness is not the lowest.  That’s the most intriguing difference discovered so far—the children’s dreams seem to have relatively more references to sadness.

Next, the SDDb class for “Cognition”:

  Harris YQ HVDC Males HVDC Females
(N=622) (N=491) (N=490)
Cognition
Awareness 3.9 20 18.8
Speech 14.6 37.1 45.1
Imagination 0.5 2 3.5
Planning 0.8 4.1 5.1
Choice 3.4 5.5 11.4
Effort 1 1.8 1.6
Reading/writing 1.8 6.7 6.7

Hmm, very low use of awareness words among the children.  Speech has the highest frequency for this class, but it’s still much lower than the frequency of speech references in the HVDC norms.

Here are the frequencies for “Nature” words:

  Harris YQ HVDC Males HVDC Females
(N=622) (N=491) (N=490)
Nature
Weather 2.3 7.1 5.5
Fire 4.5 5.3 2.9
Air 4.2 3.5 2.4
Water 10 13.8 16.9
Earth 5.8 4.7 6.7
Flying 5 4.5 2.4
Falling 6.8 9.6 7.1

The children’s frequencies seem roughly the same as the HVDC norms, contrary to what I expected about higher nature words usage in especially memorable dreams.  Water is the most frequently mentioned element in children’s dreams, and they have somewhat more falling than flying, though the flying frequency is slightly higher than the HVDC norms.  It will be interesting to look in more detail at the children’s flying dreams.

The results for the SDDb class “Characters” were the most dramatic, as measured by the sudden rise of my eyebrows:

  Harris YQ HVDC Males HVDC Females
(N=622) (N=491) (N=490)
Characters
Family 43.1 26.7 39.2
Animals 19.8 11.2 11
Fantastic beings 5.8 0.8 0.6

The children’s dreams, though lower on most other frequencies, are much higher on all three categories of characters.  The relative distributions are the same (family>animals>fantastic beings), consistent with the HVDC norms and most other dream series.  But the children’s dreams have more references to all these character types, which probably reflects several factors: the higher proportion of family and fantastic beings in memorable dreams, the prominence of fantasy in children’s literature, and the many roles, both actual and symbolic, of animals in childhood.  There’s clearly a lot here to study further.

The sixth SDDb class covers three types of “Social Interactions.”

  Harris YQ HVDC Males HVDC Females
(N=622) (N=491) (N=490)
Social Interactions
Friendliness 34.1 37.1 50
Physical aggression 18.3 26.5 13.9
Sexuality 1.8 11 3.7

These results seem to make sense in light of our expectations so far.  The children’s dreams have the same “ur-pattern” or relative distribution (friendliness>physical aggression>sexuality) as the HVDC norms.  The low sexuality is consistent with their young age, and the somewhat high physical aggression may relate to the “primal” features of memorable dreams.  This is a place where I’ll be interested to see the gender frequencies in the children’s dreams, since the HVDC norms suggest that males have much more physical aggression in their dreams than do females.

The last of the SDDb classes is “Culture,” which covers several word categories relating to people’s activities and experiences in the world of culture.  It’s kind of a catch-all class for now.

  Harris YQ HVDC Males HVDC Females
(N=622) (N=491) (N=490)
Culture
School 16.2 14.5 24.1
Transportation 12.2 26.9 22.9
Technology 5.8 8.4 7.3
Money 3.4 8.6 7.1
Christianity 3.5 3.7 4.5
Death 6.6 4.9 6.7

All the frequencies trend lower for the children’s dreams, with the exception of death.  That might reflect a higher proportion of nightmares in the children’s dreams.

Part Four:Assessing the results

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