Research Into the Straight Talk® Communication Styles

Lisa Bohon, Ph.D.
Associate Professor of Psychology,
California State University, Sacramento

In order to deepen the understanding of interpersonal communication, I asked the faculty of the psychology department at California State University, Sacramento, to conduct research into the Douglas Communication Styles and the underlying theory of human communication contained in Straight Talk. The resulting research was led by Dr. Lisa Bohon, associate professor of psychology, whose efforts yielded much in the way of fruitful results.

Further research is under way as Straight Talk goes to press. That research will be published on the Straight Talk web site (Straight-Talk-Now.com) when it is complete.

—Eric F. Douglas

The goal of the first phase of our research¹ was to construct a survey that could reliably measure the Douglas Communication Styles. The research focused on developing a tool to measure the Director, Expresser, Harmonizer and Thinker communication styles. We considered both what Eric Douglas’s theory told us about the different communication styles, as well as the responses of participants in the study. It was important to consider both the theory and the data, because using one without the other could increase the probability that chance alone would affect the final choice of statements.

We knew that we wanted to end up with a final survey—or inventory—consisting of between 30 and 40 statements. With this goal in mind, we developed 98 statements that we thought could diagnose the four different communication styles. A quarter of these statements would measure the Director style, a quarter would measure the Expresser style, and so forth. After we had randomized these 98 statements and compiled them into a survey, we gave it to 237 individuals to complete on their own².

After we collected the data, we summed the individual response items for each style. Next, we correlated each of the 98 items with all four of the styles to see which statements were most strongly related to each communicaton style (Jackson, 1970). We then chose the 12 items for each of the four styles that had the highest correlation with each style and were at least .05 different from correlations with the other three styles. In addition, any item that had a high correlation with a communication style for which it was not intended was discarded. Thus, our second generation inventory was winnowed down to 48 statements.

The next step was to subject this refined inventory to a factor analysis. This would allow us to measure the underlying traits responsible for the ways that people respond to the statements on the inventory. (Tabchnik & Fidell, 1996). Factor analysis also ensures that each statement is measuring the same underlying trait, and that the statements are maximally related to a single subscale while being minimally related to the other subscales³.

Ascree plot of the results showed us that four factors (or communication styles) had eigenvalues greater than one (2.88 to 13.26). This confirmed that the data are best explained by four communication styles, rather than, say, three or five⁵.

Based on this preliminary factor analysis, we narrowed our survey to eight statements for each communication style. We conducted a final factor analysis, which showed that each of these eight statements “loaded” on its relevant style with a score of .3 or above, and loaded lower than 3 on the other three styles. We were very pleased with these results, because they confirmed that our survey instrument was meeting established professional standards. (See Chart 27).

1. Thanks to Linda Kelley, Lisa Garner, and Tiffiny Schmid for assistance in data collection and data entry.
2. Participants in the first phase were all college students. The majority were female (70.5 percent) and young (85.2 percent were between the ages of 18 and 27). The ethnic mix of the sample was 1.7 percent African American/Black; 5.1 percent Asian American; 7.4 percent European American/White; 10.2 percent Hispanic American/Latino; and 8.9 percent other.
3. We chose to use the Promax rotation, because the theory says that the communication styles are somewhat related. For example, an individual who is high in the Director style is often also high in the Expresser style.
4. Eigenvalue is a measure that shows how much variance in response scores is explained by common features, e.g., communication styles.
5. The chi-square test for number of factors suggested that more than four were needed (chi-‘square = 679.152 (374), p = .0001). We placed more weight on the scree plot, however, because the chi-square test tends to inflate the number of factors needed (Gorsuch, 1993).

CHART 27: FINAL ITEM-SUBSCALE LOADINGS OF THE DOUGLAS COMMUNICATION STYLES INVENTORY (ROTATED FACTOR PATTERN)

Note: Letters after items indicate the communication style for which they were created: E = Expresser, T = Thinker, H = Harmonizer, D = Director

CHART 28: CORRELATIONS AMONG THE DOUGLAS COMMUNICATION STYLES INVENTORY AND THE SOCIAL DESIRABILITY SCALE

Note: * denotes significance at the .001 level. N= 237.

CHART 29: ALPHA RELIABILITY COEFFICIENTS FOR THE DOUGLAS COMMUNICATION STYLES INVENTORY SUBSCALES

We then conducted correlation analyses of the four subscales and the Social Desirability Scale (SDS; Crowne & Marlowe, 1964). We focused on the Social Desirability Scale because we were interested to see how the four subscales related to a scale that measured the tendency to present oneself in a favorable or socially desirable light. We found that the SDS was meaningfully related to the Harmonizer (r = .60) and the Thinker (r = 37) styles (see Chart 28). These relationships were predicted by the theory. Furthermore, we found strong relationships between the Expresser and Director subscales (r = .44) and the Thinker and Harmonizer sub-scales (r = 34) These relationships are also predicted by Douglas's theory.

The purpose of the next set of analyses was to investigate the internal consistency of the Douglas Communication Styles Inventory (DCSD. This would confirm that each item within the same subscale measures the same underlying communication style. Alpha reliability coefficients of .70 and above are considered acceptable. Our results revealed that the DCSI subscales showed good internal consistency (see Chart 29).

6. These surveys were counterbalanced when they were given to the same pool of subjects.

CHART 30: TEST RETEST RELIABILITY CORRELATION COEFFICIENTS

CHART 31: COMPARISONS OF AVERAGE SUBSCALE SCORES FROM TIME 1 TO TIME 2

Note: Significance levels equal to or less than .05 are considered meaningful. d= standard deviation; df = degrees of freedom.

We next investigated the stability of the Douglas Communication Style Inventory subscales. We gave the DCSI to 64 college students to fill out. Two weeks later our volunteer participants completed the DCSI once again. We conducted correlations of each of the DCSI subscales from time one to time two (see Chart 30).

The Director, Expresser, Harmonizer, and Thinker subscales showed excellent test-retest reliability (acceptable coefficients are equal to or greater than .70). Overall, the DCSI had an average test-retest reliability of .83, which indicates that this scale is stable across time.

We also conducted t-tests for correlated groups to see if average sub-scales changes significantly from Time 1 to Time 2 (see Chart 31).

7. The demographic characteristics of this sample matched those of the sample in Phase 1.

CHART 32: CORRELATIONS AMONG THE DOUGLAS COMMUNICATION STYLES INVENTORY SUBSCALES

Note: * denotes significance at the O01 level. N = 23:

CHART 33: ALPHA RELIABILITY COEFFICIENTS FOR THE DOUGLAS COMMUNICATION STYLES INVENTORY SUBSCALES

These analyses showed that average scores did not change from Time 1 to Time 2. This was added confirmation that the subscales are stable across time.

Next we correlated each of the DCSI subscales to determine their inter-relationships (see Chart 32).

Again, we found the expected high relationship between scores on the Expresser and Director subscales (r = .62), but not the Thinker and Harmonizer subscales (r = .22). We also found significant relationships between the Thinker and Director subscales (.38), the Harmonizer and Expresser subscales (.42), and the Thinker and the Expresser subscales (46). We believe, however, that the correlations from the first phase more accurately reflect the relationships among variables because they are based on a larger number of people (237 vs. 64), and therefore are more reliable.

The purpose of the final set of analyses was to confirm the internal consistency of the DCSI subscales. Our results revealed that the DCSI subscales were internally consistent (see Chart 33) with the exception of the Harmonizer subscale. As before, the alpha reliability coefficients from Phase 1 were given more weight because they were based on a larger sample.

In summary, we were very pleased with the results of the first phase of the research. The Douglas Communication Styles Inventory showed an ideal factor analysis pattern and good internal consistency. Moreover, the expected relationships among the DCSI subscales and the Social Desirability Scale were found. We were also pleased with the reliability of the survey instrument. The DCSI showed a good average test retest reliably (.83) and good internal consistency. Together, these results confirmed that the DCSI was a reliable instrument for measuring the four discrete styles of communication.

References

Crowne, D. & Marlowe, D. (1964). The Appraisal Motive. New York: Wiley.

Gorsuch, R. L. (1983). Factor Analysis (2nd ed.). Hillsdale, N.J.: Lawrence Erlbaum Associates.

Jackson, D. N. (1970). A sequential system of personality scale development. In C. D. Spielberger (Ed.), Vol. 2, Current Topics in Clinical and Community Psychology. New York: Academic Press.

Tabachnik, B. G., & Fidell, L. S. (1996) Using Multivariate Statistics (3rd ed.). New York: Harper Collins.