Edited by: Simine Vazire, Washington University in St. Louis, USA
Reviewed by: Jennifer L. Tackett, University of Houston, USA; Ryne A. Sherman, Florida Atlantic University, USA
*Correspondence: Oliver C. Schultheiss, Department of Psychology, Friedrich-Alexander University, Nägelsbachstrasse 49b, 91052 Erlangen, Germany e-mail:
This article was submitted to Personality Science and Individual Differences, a section of the journal Frontiers in Psychology.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Traditionally, implicit motives (i.e., non-conscious preferences for specific classes of incentives) are assessed through semantic coding of imaginative stories. The present research tested the marker-word hypothesis, which states that implicit motives are reflected in the frequencies of specific words. Using Linguistic Inquiry and Word Count (LIWC; Pennebaker et al.,
Implicit motives, that is, non-consciously operating dispositions to experience certain classes of incentives as affectively charged, are assessed through the content coding of written text (Schultheiss and Pang,
Thus, although the assessment of implicit motives is objective, as reflected in high agreements among experienced coders and between coders and expert-coded calibration materials, it is also a labor-intensive process that requires thorough training of coders and about 2 (coders) × 25 min for each participant's set of stories. For PSEs from a sample of 100 participants, this means that more than 80 h have to be invested in the content-coding process itself, not counting the time needed for coder training. This is a considerable investment of resources into the assessment of implicit motives, and high interrater reliability is not necessarily guaranteed, especially for coders who are new to the job. These hurdles may appear prohibitive to researchers who want to use implicit motive measures in their research.
In the present paper, I therefore explore to what extent content-coding of PSEs can be approximated by computer-based coding of
Implicit motive measures have been derived by experimentally arousing a motivational state in one group of participants, leaving it unaroused in a control group and then having both groups write imaginative stories about the same picture cues (see Atkinson,
Several separate coding systems were derived in this manner to assess the needs for achievement (
Because the research reported here was conducted with Winter's (
This brief review of Winter's (
Jim Callahan is |
|
Can motive assessment be simplified by identifying and counting marker words, that is, words that capture roughly the same meaning that a human coder would see when coding motivational imagery? For instance, in the text above, “accomplished” might be such a marker word for
This brief analysis highlights the possibilities and ambiguities inherent in finding suitable marker words that could approximate content coding of text. It also suggests that in some instances there
Early attempts at measuring motives through marker words include Zatzkis's (
Smith (
Seidenstücker and Seidenstücker (
Pennebaker and King (
Finally, Hogenraad (
To summarize, there exists a veritable research tradition that has aimed at substituting, or at least supplementing, the laborious work of content-coding motivational themes by counting specific words. Some of this research has looked at word classes that are not a priori linked to a motive, such as the work by Pennebaker and King (
One reason is technology. The first such studies were conducted at a time when there were no computers around for processing data or doing statistics, and therefore little was gained in terms of speed or objectivity relative to content coding. Even when computers were finally available, they were slow, expensive, and cumbersome to work with. Thus, for a long time it was simply more efficient to content-code PSE stories by hand and run analyses with the scores derived in this manner. Clearly, this had changed when Pennebaker and King (
Another reason is generalizability. Smith (
A third reason is validation. Most previous studies focused on and reported evidence of convergent validity between computer-based and content-coding motive scores. In addition, Pennebaker and King (
The present research aims to overcome the limitations of earlier studies in several ways. First, I decided to use LIWC 2001 to analyze PSE stories, because the LIWC software and dictionaries are very accessible and are used by many researchers. The LIWC 2001 dictionary has been validated for both English (Pennebaker et al.,
To examine the robustness of the linear combinations of LIWC categories, I tested their
In Study 1 I also explored whether LIWC-based scores show similar
To test the marker-word hypothesis and to derive robust linear combinations of LIWC 2001 word categories that converge with content-coded motive scores, I reanalyzed PSEs collected in a research project dealing with the congruence between implicit and explicit motives (Studies 2 and 3 in Schultheiss et al.,
One-hundred-and-forty-six students at the University of Michigan, Ann Arbor, USA, participated in a cross-sectional study on “attention and performance” for course credit. Of these, 113 participants (average age: 19 years; 57 mens, 48 womens, 8 participants did not give demographic information) provided a complete PSE and were included in the analyses. Of the participants who had provided demographic information, 62% self-identified as Caucasian, 20% as Asian, 6% as African-American, 3% as Pacific Islander; the remainder belonged to other or mixed ethnic groups.
One-hundred-six students at Friedrich-Alexander University, Erlangen, Germany, participated in a cross-sectional study on “attention and performance” for payment of €15. Of the initial sample, 100 participants (average age: 23 years; 51 mens; all Caucasian) provided a complete PSE.
US participants worked on an 8-picture PSE described by Schultheiss et al. (
Stories were later coded for motivational imagery by trained scorers using Winter's (
All PSE stories were first spell-checked and corrected and then individually saved and run through the LIWC software (
0.104 |
0.36 | 4.55 | 2.36 | 0.058 |
0.61 | 3.79 | 2.89 | 0.052 |
0.59 | 5.43 | 3.41 | ||
0.534 |
0.51 | 5.14 | 2.52 | 0.498 |
0.55 | 4.56 | 2.55 | 0.577 |
0.58 | 6.61 | 3.00 | ||
0.610 |
0.48 | 6.61 | 2.74 | 0.551 |
0.50 | 4.91 | 2.65 | 0.565 |
0.52 | 7.04 | 3.14 | ||
Activity inhibition | 0.023 |
0.45 | 5.58 | 3.57 | 0.012 | 0.64 | 5.00 | 3.90 | 0.017 | 0.65 | 6.92 | 4.67 | |
Word count | 0.054 |
0.93 | 607.85 | 151.54 | 0.001 | 0.94 | 668.09 | 206.15 | 0.023 |
0.95 | 901.91 | 272.5 | |
Words/ sentence | 0.020 | 0.76 | 15.04 | 3.46 | 0.058 |
0.78 | 17.31 | 3.62 | 0.048 |
0.82 | 17.49 | 3.48 | |
Unique words | 0.027 |
0.80 | 75.39 | 4.27 | 0.092 |
0.85 | 64.32 | 6.04 | 0.079 |
0.89 | 64.31 | 5.80 | |
Dictionary words | 0.168 |
0.58 | 70.37 | 3.23 | 0.210 |
0.73 | 77.68 | 3.85 | 0.275 |
0.78 | 78.01 | 3.71 | |
Words > 6 letters | 0.204 |
0.61 | 22.35 | 2.85 | 0.297 |
0.64 | 15.63 | 2.72 | 0.272 |
0.69 | 15.12 | 2.54 | |
Pronouns | I, our, they, you're | 0.092 |
0.67 | 10.54 | 2.49 | 0.082 |
0.67 | 10.22 | 2.34 | 0.090 |
0.74 | 10.28 | 2.27 |
I | I, my, me | 0.018 | 0.60 | 0.52 | 0.96 | 0.014 | 0.75 | 0.29 | 0.89 | 0.017 | 0.80 | 0.28 | 0.84 |
We | We, our, us | 0.019 | 0.49 | 0.20 | 0.44 | 0.020 |
0.14 | 0.06 | 0.13 | 0.017 | 0.45 | 0.06 | 0.17 |
Self | I, we, me | 0.015 | 0.68 | 0.72 | 1.26 | 0.018 | 0.73 | 0.35 | 0.94 | 0.023 |
0.79 | 0.35 | 0.91 |
You | You, you'll | 0.318 |
0.28 | 1.52 | 0.66 | 0.015 | 0.81 | 0.16 | 0.56 | 0.019 |
0.83 | 0.14 | 0.48 |
Other | She, their, them | 0.097 |
0.63 | 9.42 | 2.29 | 0.127 |
0.63 | 8.65 | 2.11 | 0.121 |
0.71 | 8.74 | 2.06 |
Negate | No, never, not | 0.032 |
0.35 | 1.37 | 0.65 | 0.039 |
0.61 | 0.95 | 0.63 | 0.031 |
0.66 | 0.94 | 0.57 |
Assent | Yes, OK | 0.012 | 0.35 | 0.20 | 0.25 | 0.055 |
0.39 | 0.09 | 0.16 | 0.044 |
0.52 | 0.08 | 0.15 |
Article | A, an, the | 0.164 |
0.70 | 10.65 | 2.24 | 0.169 |
0.73 | 9.94 | 2.25 | 0.173 |
0.77 | 9.76 | 2.10 |
Prepositions | On, to, from | 0.096 |
0.58 | 9.55 | 1.65 | 0.018 | 0.48 | 13.63 | 1.57 | 0.017 | 0.56 | 13.73 | 1.51 |
Number | One, thirty, million | 0.017 | 0.08 | 0.42 | 0.31 | 0.047 |
0.32 | 1.08 | 0.55 | 0.113 |
0.39 | 1.28 | 0.56 |
Affect | Happy, ugly, bitter | 0.123 |
0.48 | 5.65 | 1.52 | 0.143 |
0.33 | 4.71 | 1.18 | 0.119 |
0.38 | 4.60 | 1.03 |
Positive emotions | Happy, pretty, good | 0.110 |
0.50 | 3.81 | 1.26 | 0.286 |
0.40 | 3.34 | 1.07 | 0.242 |
0.48 | 3.25 | 0.96 |
Positive feelings | Happy, joy, love | 0.117 |
0.21 | 0.59 | 0.40 | 0.321 |
0.12 | 0.91 | 0.50 | 0.292 |
0.24 | 0.86 | 0.44 |
Optimism | Certainty, pride, win | 0.101 |
0.35 | 0.92 | 0.49 | 0.205 |
0.22 | 0.81 | 0.45 | 0.276 |
0.32 | 0.90 | 0.43 |
Negative emotions | Hate, worthless, enemy | 0.200 |
0.29 | 1.84 | 0.83 | 0.196 |
0.15 | 1.35 | 0.63 | 0.161 |
0.23 | 1.33 | 0.56 |
Anxiety | Nervous, afraid, tense | 0.045 |
−0.13 | 0.30 | 0.24 | 0.072 |
0.44 | 0.41 | 0.38 | 0.070 |
0.42 | 0.37 | 0.31 |
Anger | Hate, kill, pissed | 0.288 |
0.11 | 0.45 | 0.37 | 0.242 |
0.01 | 0.46 | 0.34 | 0.207 |
0.14 | 0.42 | 0.30 |
Sadness | Grief, cry, sad | 0.030 |
0.13 | 0.41 | 0.34 | 0.020 |
0.16 | 0.24 | 0.24 | 0.015 | 0.02 | 0.24 | 0.19 |
Cognitive processes | Cause, know, ought | 0.119 |
0.46 | 8.67 | 1.83 | 0.092 |
0.28 | 5.55 | 1.23 | 0.074 |
0.40 | 5.61 | 1.14 |
Causation | Because, effect, hence | 0.017 | 0.06 | 1.21 | 0.49 | 0.060 |
0.18 | 0.75 | 0.41 | 0.051 |
0.35 | 0.73 | 0.38 |
Insight | Think, know, consider | 0.088 |
0.49 | 2.44 | 0.95 | 0.052 |
0.14 | 1.77 | 0.63 | 0.048 |
0.21 | 1.79 | 0.56 |
Discrepancy | Should, would, could | 0.024 |
0.43 | 1.71 | 0.71 | 0.022 |
0.25 | 1.64 | 0.65 | 0.023 |
0.38 | 1.66 | 0.61 |
Inhibition | Block, constrain | 0.037 |
0.08 | 0.22 | 0.22 | 0.016 | −0.02 | 0.30 | 0.24 | 0.015 | 0.04 | 0.30 | 0.21 |
Tentative | Maybe, perhaps, guess | 0.040 |
0.68 | 1.21 | 0.9 | 0.021 |
0.79 | 1.58 | 1.21 | 0.046 |
0.83 | 1.54 | 1.13 |
Certainty | Always, never | 0.124 |
0.42 | 2.06 | 0.83 | 0.039 |
0.53 | 1.03 | 0.62 | 0.040 |
0.62 | 1.02 | 0.59 |
Senses | See, touch, listen | 0.146 |
0.01 | 0.25 | 0.22 | 0.079 |
0.49 | 2.23 | 0.94 | 0.219 |
0.61 | 2.36 | 0.91 |
See | View, saw, look | 0.069 |
−0.06 | 0.12 | 0.14 | 0.022 |
0.33 | 0.95 | 0.55 | 0.040 |
0.47 | 0.94 | 0.52 |
Hear | Heard, listen, sound | NC | 0.20 | 0.03 | 0.08 | 0.256 |
0.35 | 0.78 | 0.5 | 0.342 |
0.45 | 0.89 | 0.50 |
Feel | Touch, hold, felt | NC | −0.02 | 0.01 | 0.03 | 0.046 |
0.48 | 0.40 | 0.40 | 0.041 |
0.50 | 0.40 | 0.34 |
Social | Talk, us, friend | 0.096 |
0.60 | 13.59 | 2.47 | 0.091 |
0.57 | 13.73 | 2.55 | 0.323 |
0.64 | 14.29 | 2.46 |
Communication | Talk, share, converse | 0.250 |
0.24 | 1.32 | 0.59 | 0.263 |
0.22 | 1.47 | 0.62 | 0.396 |
0.36 | 1.63 | 0.63 |
Other references | 1st pl, 2nd, 3rd per prns | 0.082 |
0.62 | 10.33 | 2.24 | 0.116 |
0.62 | 9.01 | 2.06 | 0.117 |
0.70 | 9.09 | 2.03 |
Friends | Pal, buddy, coworker | 0.026 |
−0.30 | 0.48 | 0.29 | 0.026 |
0.25 | 0.28 | 0.30 | 0.227 |
0.21 | 0.43 | 0.36 |
Family | Mom, brother, cousin | 0.044 |
0.61 | 1.20 | 1.01 | 0.003 | 0.33 | 0.39 | 0.51 | 0.013 | 0.22 | 0.39 | 0.45 |
Humans | Boy, woman, group | 0.071 |
0.67 | 1.51 | 1.18 | 0.099 |
0.71 | 2.14 | 1.41 | 0.142 |
0.79 | 2.25 | 1.43 |
Time | Hour, day, oclock | 0.052 |
0.56 | 5.36 | 1.65 | 0.152 |
0.46 | 4.13 | 1.22 | 0.116 |
0.50 | 4.22 | 1.10 |
Past | Walked, were, had | 0.018 | 0.90 | 4.23 | 3.12 | 0.047 |
0.91 | 3.55 | 3.13 | 0.040 |
0.93 | 3.60 | 3.17 |
Present | Walk, is, be | 0.068 |
0.87 | 6.20 | 2.83 | 0.045 |
0.86 | 10.54 | 3.52 | 0.039 |
0.90 | 10.65 | 3.44 |
Future | Will, might, shall | 0.015 | 0.63 | 0.78 | 0.65 | 0.020 |
0.70 | 1.47 | 1.06 | 0.025 |
0.74 | 1.40 | 0.95 |
Space | Around, over, up | 0.079 |
0.50 | 7.78 | 1.49 | 0.068 |
0.37 | 3.32 | 0.94 | 0.077 |
0.41 | 3.47 | 0.85 |
Up | Up, above, over | 0.080 |
0.45 | 1.57 | 0.71 | 0.044 |
0.15 | 1.54 | 0.56 | 0.041 |
0.29 | 1.61 | 0.55 |
Down | Down, below, under | 0.021 | 0.08 | 0.10 | 0.18 | 0.055 |
−0.10 | 0.25 | 0.23 | 0.045 |
0.04 | 0.25 | 0.20 |
Inclusive | With, and, include | 0.105 |
0.54 | 6.32 | 1.29 | 0.096 |
0.53 | 7.30 | 1.45 | 0.073 |
0.55 | 7.29 | 1.26 |
Exclusive | But, except, without | 0.024 |
0.43 | 2.15 | 0.74 | 0.003 | 0.55 | 2.76 | 1.00 | 0.002 | 0.59 | 2.76 | 0.89 |
Motion | Walk, move, go | 0.069 |
0.31 | 1.25 | 0.54 | 0.091 |
0.34 | 1.36 | 0.61 | 0.069 |
0.27 | 1.32 | 0.50 |
Occupation | Work, class, boss | 0.468 |
0.25 | 4.38 | 1.21 | 0.358 |
−0.09 | 2.64 | 0.84 | 0.335 |
0.01 | 2.68 | 0.75 |
School | Class, student, college | 0.379 |
0.09 | 1.42 | 0.69 | 0.274 |
−0.11 | 0.72 | 0.61 | 0.255 |
−0.07 | 0.63 | 0.49 |
Job | Employ, boss, career | 0.369 |
0.05 | 1.46 | 0.57 | 0.121 |
0.13 | 0.89 | 0.51 | 0.130 |
0.10 | 0.78 | 0.40 |
Achievement | Try, goal, win | 0.245 |
0.32 | 2.22 | 0.86 | 0.182 |
−0.03 | 1.33 | 0.59 | 0.320 |
0.14 | 1.54 | 0.57 |
Leisure | House, TV, music | 0.406 |
−0.12 | 1.36 | 0.51 | 0.511 |
−0.11 | 1.22 | 0.52 | 0.470 |
−0.09 | 1.08 | 0.42 |
Home | House, kitchen, lawn | 0.032 |
0.39 | 0.34 | 0.35 | 0.030 |
0.28 | 0.32 | 0.31 | 0.025 |
0.22 | 0.30 | 0.26 |
Sports | Football, game, play | 0.434 |
0.03 | 0.55 | 0.38 | 0.141 |
−0.07 | 0.24 | 0.22 | 0.137 |
0.06 | 0.25 | 0.21 |
TV | TV, sitcom, cinema | 0.086 |
0.15 | 0.13 | 0.18 | 0.097 |
0.10 | 0.13 | 0.18 | 0.099 |
0.10 | 0.11 | 0.14 |
Music | Tunes, song, cd | 0.506 |
−0.18 | 0.46 | 0.27 | 0.616 |
−0.06 | 0.43 | 0.29 | NC | −0.06 | 0.33 | 0.22 |
Money | cash, taxes, income | 0.074 |
−0.22 | 0.52 | 0.33 | 0.143 |
0.15 | 0.39 | 0.36 | 0.149 |
0.30 | 0.33 | 0.31 |
Metaphysical issues | God, heaven, coffin | 0.040 |
0.29 | 0.37 | 0.32 | 0.007 | 0.06 | 0.11 | 0.15 | 0.008 | 0.21 | 0.10 | 0.13 |
Religion | God, church, rabbi | 0.045 |
0.25 | 0.27 | 0.26 | 0.011 | 0.04 | 0.04 | 0.09 | 0.008 | 0.26 | 0.04 | 0.09 |
Death | Dead, burial, coffin | 0.012 | 0.19 | 0.14 | 0.22 | 0.018 | 0.05 | 0.07 | 0.12 | 0.019 |
0.23 | 0.06 | 0.10 |
Physical states | Ache, breast, sleep | 0.189 |
0.23 | 1.26 | 0.55 | 0.232 |
0.04 | 1.42 | 0.60 | 0.190 |
0.19 | 1.54 | 0.59 |
Body | Ache, heart, cough | 0.092 |
0.47 | 0.65 | 0.48 | 0.051 |
0.16 | 0.60 | 0.43 | 0.073 |
0.24 | 0.67 | 0.42 |
Sexuality | Lust, penis, fuck | 0.083 |
0.07 | 0.18 | 0.20 | 0.074 |
0.18 | 0.31 | 0.31 | 0.064 |
0.27 | 0.28 | 0.28 |
Eating | Eat, swallow, taste | 0.308 |
−0.06 | 0.38 | 0.29 | 0.411 |
0.16 | 0.45 | 0.35 | 0.377 |
0.26 | 0.52 | 0.34 |
Sleeping | Asleep, bed, dreams | 0.019 | 0.15 | 0.07 | 0.15 | 0.024 |
0.16 | 0.10 | 0.16 | 0.058 |
0.19 | 0.12 | 0.14 |
Grooming | Wash, bath, clean | 0.008 | −0.15 | 0.04 | 0.08 | 0.011 | 0.00 | 0.03 | 0.09 | 0.057 |
0.13 | 0.07 | 0.15 |
Swear words | Damn, fuck, piss | 0.012 | 0.57 | 0.04 | 0.12 | 0.014 | 0.40 | 0.02 | 0.09 | 0.013 | 0.71 | 0.02 | 0.10 |
Fillers | You know, I mean | NC | 0.11 | 0.01 | 0.05 | 0.003 | 0.19 | 0.15 | 0.19 | 0.003 | 0.25 | 0.15 | 0.16 |
Question mark | ? | 0.018 | 0.56 | 0.20 | 0.32 | 0.012 | 0.70 | 0.07 | 0.27 | 0.012 | 0.77 | 0.07 | 0.26 |
Exclamation mark | ! | 0.013 | 0.88 | 0.30 | 0.82 | 0.020 |
0.38 | 0.08 | 0.22 | 0.017 | 0.52 | 0.09 | 0.22 |
Quote | “” | 0.023 |
0.76 | 0.41 | 0.84 | 0.017 | 0.74 | 0.34 | 0.98 | 0.017 | 0.84 | 0.36 | 1.02 |
All punctuation | .;:!? | 0.019 | 0.88 | 14.14 | 3.84 | 0.019 | 0.88 | 11.07 | 3.81 | 0.015 | 0.92 | 11.11 | 3.79 |
I used scales from the PRF (English version: (Jackson,
In addition to their gender, participants in the German sample also reported their current progress on ideographically assessed personal goals related to achievement and power on Brunstein et al.'s (
Table
Although the LIWC scores reported in Table
A second point worth making is that LIWC analysis of the PSE allows to partition the variance in language that is due to the eliciting cues (i.e., the PSE pictures, represented by η2Picture in Table
A third feature is the scarcity with which many categories (particularly subcategories) show up in the stories. Many category scores therefore deviate substantially from a normal distribution, violating an important prerequisite for regression-based inferential statistics. For all further analyses, I resolved deviations from normality, as revealed through significant Kolmogorov-Smirnov tests and inspection of score distribution histograms, as follows: (1) if the score distribution was sufficiently differentiated at the low scale end in both samples, I subjected it to a log transformation [new score = ln (1 + old score)] for both samples; (2) if a large proportion of participants (i.e., >20%) had a score of 0 in one or both samples, I converted the variable to a dichotomous format, with 0 representing the absence of the use of words in a category and 1 the presence of such words. Table
Word count | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
Words/ sentence | 0.02 | −0.12 | −0.13 | −0.10 | 0.06 | 0.02 | −0.07 | −0.28 |
0.07 | −0.02 | −0.04 | −0.32 |
|
Unique words | 0.17 | −0.01 | −0.13 | −0.15 | 0.02 | 0.03 | −0.10 | 0.06 | 0.00 | −0.01 | −0.10 | 0.09 | |
Dictionary words | −0.27 |
0.09 | 0.19 | 0.02 | 0.01 | −0.11 | 0.17 | 0.15 | 0.03 | −0.08 | 0.18 | 0.09 | |
Words > 6 letters | 0.12 | 0.06 | −0.21 |
−0.33 |
−0.13 | 0.05 | −0.15 | −0.25 |
−0.17 | 0.01 | −0.15 | −0.23 |
|
Pronouns | 0.09 | 0.06 | 0.15 | 0.29 |
0.01 | −0.04 | 0.14 | 0.35 |
−0.05 | −0.02 | 0.14 | 0.40 |
|
I | Dichotomized | 0.08 | −0.02 | −0.05 | 0.19 | −0.02 | −0.22 |
−0.07 | 0.13 | 0.00 | −0.09 | −0.17 | 0.05 |
We | Dichotomized | −0.01 | −0.07 | −0.06 | 0.08 | 0.07 | −0.23 |
−0.12 | 0.13 | 0.14 | −0.23 |
−0.12 | 0.12 |
Self | Dichotomized | 0.00 | −0.01 | −0.02 | 0.10 | 0.01 | −0.36 |
−0.10 | 0.14 | 0.07 | −0.28 |
−0.15 | 0.05 |
You | Logarithm | 0.13 | 0.16 | −0.04 | −0.05 | 0.01 | −0.10 | 0.03 | 0.00 | −0.05 | −0.07 | −0.02 | 0.11 |
Other | 0.07 | 0.08 | 0.30 |
0.18 | 0.10 | 0.12 | 0.37 |
0.19 | 0.03 | 0.10 | 0.40 |
0.26 |
|
Negate | −0.01 | −0.34 |
−0.15 | 0.85 |
0.04 | −0.18 | −0.07 | 0.69 |
0.01 | −0.20 |
−0.12 | 0.66 |
|
Assent | Dichotomized | −0.03 | 0.14 | −0.07 | 0.30 |
0.02 | −0.06 | −0.05 | 0.01 | 0.09 | −0.06 | −0.04 | 0.08 |
Article | −0.24 |
−0.02 | −0.12 | −0.22 |
0.03 | −0.02 | −0.03 | −0.25 |
0.11 | −0.02 | −0.02 | −0.31 |
|
Prepositions | 0.14 | −0.01 | −0.12 | −0.34 |
0.05 | 0.00 | 0.01 | −0.10 | 0.02 | 0.05 | 0.02 | −0.21 |
|
Number | 0.09 | −0.03 | 0.06 | −0.14 | 0.09 | 0.06 | −0.03 | −0.07 | 0.07 | 0.10 | 0.05 | −0.15 | |
Affect | 0.00 | 0.19 | 0.18 | −0.06 | −0.05 | 0.12 | 0.05 | 0.11 | −0.04 | 0.10 | 0.05 | 0.11 | |
Positive emotions | −0.14 | 0.18 | 0.26 |
−0.06 | −0.15 | 0.21 |
0.15 | −0.03 | −0.13 | 0.21 |
0.16 | −0.07 | |
Positive feelings | Logarithm | −0.17 | −0.03 | 0.33 |
0.11 | −0.11 | −0.02 | 0.26 |
−0.05 | −0.09 | −0.05 | 0.23 |
−0.10 |
Optimism | 0.06 | 0.31 |
0.12 | −0.16 | −0.07 | 0.30 |
0.31 |
−0.03 | −0.04 | 0.33 |
0.28 |
−0.12 | |
Negative emotions | 0.21 |
0.07 | −0.06 | −0.01 | 0.16 | −0.14 | −0.16 | 0.23 | 0.14 | −0.17 | −0.18 |
0.32 |
|
Anxiety | Logarithm | 0.00 | 0.26 |
0.15 | −0.13 | 0.01 | −0.06 | 0.05 | 0.02 | 0.01 | −0.11 | 0.04 | 0.08 |
Anger | Logarithm | 0.21 |
0.10 | −0.04 | −0.13 | 0.24 |
−0.17 | −0.19 |
0.14 | 0.25 |
−0.13 | −0.19 |
0.24 |
Sadness | Logarithm | 0.12 | −0.04 | 0.06 | −0.01 | −0.09 | 0.17 | −0.06 | 0.18 | −0.01 | 0.11 | −0.03 | 0.18 |
Cognitive processes | −0.20 |
−0.13 | −0.05 | 0.37 |
0.02 | 0.01 | −0.03 | 0.19 |
0.03 | 0.00 | 0.01 | 0.18 |
|
Causation | 0.10 | −0.17 | −0.01 | 0.26 |
0.03 | −0.06 | −0.07 | −0.05 | −0.06 | −0.03 | −0.02 | −0.01 | |
Insight | −0.18 | −0.25 |
−0.05 | 0.11 | 0.09 | 0.10 | 0.03 | 0.00 | 0.08 | 0.09 | −0.02 | 0.02 | |
Discrepancy | −0.00 | 0.04 | −0.10 | 0.31 |
−0.07 | 0.02 | 0.09 | 0.26 |
−0.04 | 0.02 | 0.16 | 0.29 |
|
Inhibition | Dichotomized | 0.09 | −0.03 | −0.10 | 0.09 | 0.09 | −0.07 | 0.00 | 0.13 | 0.04 | −0.09 | 0.00 | 0.10 |
Tentative | Logarithm | −0.40 |
−0.24 |
−0.15 | 0.25 |
−0.28 |
−0.23 |
−0.21 |
0.05 | −0.22 |
−0.27 |
−0.27 |
−0.01 |
Certainty | −0.02 | 0.11 | 0.13 | 0.02 | 0.01 | 0.18 | 0.06 | −0.02 | −0.03 | 0.16 | 0.04 | 0.00 | |
Senses | Dichotomized | 0.02 | 0.11 | 0.09 | −0.29 |
−0.02 | −0.13 | 0.01 | 0.09 | 0.00 | −0.13 | 0.02 | 0.10 |
See | Dichotomized | 0.27 |
0.28 |
0.13 | −0.28 |
−0.09 | −0.06 | 0.00 | −0.06 | 0.00 | −0.07 | −0.03 | −0.05 |
Hear | Dichotomized | −0.20 |
−0.12 | 0.13 | 0.06 | 0.05 | −0.07 | 0.01 | 0.24 |
0.03 | −0.07 | 0.02 | 0.23 |
Feel | Dichotomized | 0.14 | 0.02 | −0.16 | 0.01 | 0.01 | −0.02 | 0.00 | 0.05 | −0.01 | −0.08 | 0.07 | 0.02 |
Social | −0.04 | −0.06 | 0.37 |
0.23 |
0.10 | −0.03 | 0.26 |
0.20 |
0.06 | −0.02 | 0.27 |
0.20 |
|
Communication | −0.15 | −0.05 | 0.28 |
0.15 | 0.08 | −0.09 | −0.08 | 0.03 | 0.05 | −0.02 | −0.05 | 0.04 | |
Other references | 0.10 | 0.12 | 0.25 |
0.22 |
0.08 | 0.07 | 0.31 |
0.25 |
0.01 | 0.07 | 0.33 |
0.31 |
|
Friends | Logarithm | 0.02 | −0.09 | 0.28 |
0.00 | 0.14 | −0.02 | 0.37 |
0.04 | 0.08 | −0.10 | 0.36 |
0.04 |
Family | Logarithm | −0.14 | −0.22 |
0.15 | 0.00 | 0.00 | −0.11 | −0.03 | 0.01 | −0.05 | −0.17 | −0.02 | −0.02 |
Humans | Logarithm | −0.23 |
−0.22 |
0.09 | 0.03 | 0.01 | −0.05 | −0.04 | 0.00 | 0.06 | −0.05 | −0.06 | −0.08 |
Time | 0.09 | 0.15 | 0.03 | −0.25 |
−0.10 | 0.00 | 0.10 | −0.07 | −0.13 | −0.01 | 0.14 | −0.02 | |
Past | Logarithm | 0.14 | 0.09 | −0.17 | 0.06 | 0.04 | 0.07 | −0.08 | 0.08 | −0.03 | 0.07 | −0.04 | 0.14 |
Present | −0.17 | −0.03 | 0.18 | 0.11 | 0.02 | −0.10 | 0.19 |
0.03 | 0.10 | −0.07 | 0.11 | 0.01 | |
Future | Logarithm | −0.09 | −0.02 | 0.22 |
−0.09 | −0.21 |
−0.03 | 0.05 | −0.02 | −0.18 | −0.07 | 0.01 | −0.06 |
Space | 0.09 | −0.06 | −0.05 | −0.36 |
0.27 |
−0.07 | 0.06 | 0.11 | 0.20 |
−0.02 | 0.02 | 0.13 | |
Up | −0.05 | 0.08 | 0.11 | −0.16 | 0.12 | −0.13 | 0.03 | 0.08 | 0.07 | −0.12 | −0.07 | 0.08 | |
Down | Dichotomized | 0.04 | −0.02 | −0.08 | 0.12 | 0.22 |
0.10 | −0.04 | 0.08 | 0.23 |
0.10 | −0.07 | 0.11 |
Inclusive | −0.03 | 0.00 | 0.02 | −0.20 |
0.03 | −0.04 | 0.09 | −0.19 |
0.03 | −0.03 | 0.09 | −0.20 |
|
Exclusive | 0.13 | −0.07 | −0.01 | 0.10 | 0.16 | 0.00 | −0.02 | 0.22 |
0.15 | 0.02 | −0.01 | 0.21 |
|
Motion | 0.00 | 0.14 | 0.07 | −0.24 |
0.01 | −0.15 | 0.09 | −0.06 | 0.01 | −0.12 | 0.05 | −0.01 | |
Occupation | 0.00 | 0.22 |
0.05 | −0.20 |
0.01 | 0.25 |
0.08 | −0.07 | −0.07 | 0.31 |
0.08 | −0.14 | |
School | Logarithm | 0.05 | 0.22 |
−0.10 | −0.17 | −0.07 | −0.07 | 0.02 | −0.19 |
−0.09 | −0.03 | 0.02 | −0.14 |
Job | 0.02 | 0.00 | 0.07 | −0.12 | 0.02 | 0.12 | 0.02 | 0.15 | 0.00 | 0.14 | 0.05 | 0.07 | |
Achievement | Logarithm | −0.05 | 0.21 |
0.08 | −0.10 | 0.00 | 0.35 |
0.11 | 0.06 | −0.08 | 0.36 |
0.06 | −0.08 |
Leisure | −0.13 | 0.08 | 0.07 | −0.17 | −0.02 | −0.04 | 0.09 | −0.06 | −0.01 | −0.08 | 0.02 | −0.04 | |
Home | Logarithm | 0.08 | −0.06 | −0.01 | −0.21 |
−0.10 | 0.07 | 0.18 | −0.28 |
−0.15 | 0.05 | 0.15 | −0.26 |
Sports | Logarithm | −0.15 | 0.18 | −0.02 | −0.12 | −0.02 | −0.12 | 0.00 | 0.17 | 0.00 | −0.09 | −0.13 | 0.12 |
TV | Dichotomized | 0.12 | 0.20 |
0.01 | −0.16 | 0.08 | 0.13 | 0.02 | −0.10 | 0.06 | 0.02 | −0.02 | −0.01 |
Music | −0.20 |
0.06 | 0.06 | −0.08 | −0.06 | −0.11 | −0.03 | 0.07 | −0.01 | −0.16 | −0.05 | 0.15 | |
Money | Logarithm | 0.05 | −0.09 | −0.09 | 0.05 | 0.18 | 0.06 | −0.14 | 0.02 | 0.23 |
0.11 | −0.09 | 0.10 |
Metaphysical issues | Dichotomized | 0.00 | 0.08 | −0.01 | −0.07 | 0.09 | 0.24 |
0.08 | 0.08 | 0.12 | 0.28 |
0.05 | 0.15 |
Religion | Dichotomized | 0.05 | 0.01 | −0.02 | −0.02 | −0.16 | 0.06 | 0.10 | 0.08 | −0.12 | 0.11 | 0.06 | 0.06 |
Death | Dichotomized | 0.13 | −0.03 | −0.03 | −0.10 | 0.14 | 0.22 |
0.03 | 0.01 | 0.14 | 0.25 |
0.03 | 0.08 |
Physical states | −0.13 | 0.04 | −0.01 | −0.16 | −0.09 | −0.14 | 0.17 | −0.10 | −0.01 | −0.09 | 0.14 | −0.03 | |
Body | Logarithm | 0.01 | −0.01 | −0.09 | −0.04 | −0.10 | −0.10 | −0.04 | −0.04 | −0.01 | −0.06 | −0.11 | 0.04 |
Sexuality | Dichotomized | −0.02 | 0.01 | 0.19 | 0.03 | −0.00 | 0.02 | 0.35 |
0.03 | 0.05 | 0.02 | 0.29 |
0.07 |
Eating | Logarithm | −0.22 |
0.11 | 0.00 | −0.16 | 0.04 | −0.12 | 0.07 | −0.12 | −0.02 | −0.10 | 0.15 | −0.14 |
Sleeping | Dichotomized | −0.05 | 0.04 | 0.05 | −0.10 | −0.15 | −0.06 | 0.03 | 0.00 | −0.09 | 0.02 | 0.16 | −0.05 |
Grooming | Dichotomized | 0.17 | −0.07 | 0.01 | −0.16 | 0.07 | 0.10 | −0.08 | −0.10 | 0.02 | −0.11 | −0.10 | −0.04 |
Swear words | Dichotomized | 0.05 | −0.09 | −0.07 | 0.12 | −0.02 | −0.06 | 0.08 | 0.23 |
−0.04 | −0.03 | −0.07 | 0.28 |
Fillers | Dichotomized | 0.00 | −0.06 | −0.11 | 0.01 | −0.09 | −0.14 | −0.01 | −0.01 | −0.09 | −0.16 | −0.03 | −0.02 |
Question mark | Dichotomized | 0.00 | −0.22 |
−0.06 | 0.34 |
−0.01 | −0.04 | −0.19 |
0.12 | 0.01 | 0.01 | −0.21 |
0.22 |
Exclamation mark | Dichotomized | 0.25 |
0.02 | −0.12 | 0.11 | 0.01 | −0.14 | −0.01 | 0.23 |
0.07 | −0.10 | −0.06 | 0.22 |
Quote | Dichotomized | −0.03 | −0.17 | −0.12 | 0.25 |
0.02 | −0.04 | −0.09 | 0.04 | −0.01 | −0.06 | −0.19 |
0.06 |
All punctuation | Logarithm | 0.13 | −0.07 | −0.05 | 0.32 |
−0.05 | −0.06 | −0.23 |
0.27 |
−0.08 | −0.05 | −0.28 |
0.26 |
Table
Some correlations between motive and LIWC scores were unique to each sample. In the German sample,
As expected, across both samples activity inhibition was highly correlated with
The correlations reported in Table
Constant | 0.540 | 0.264 | 0.04 | 0.276 | 0.284 | 0.33 | 0.082 | 0.301 | 0.78 | 0.408 |
Anger |
0.672 | 0.393 | 0.09 | 0.958 | 0.399 | 0.02 | 1.126 | 0.453 | 0.01 | 0.815 |
Tentat |
−1.060 | 0.262 | 0.0001 | −0.707 | 0.245 | 0.005 | −0.531 | 0.260 | 0.04 | −0.883 |
0.182 | 0.126 | 0.099 | ||||||||
10.79 | 7.91 | 6.03 | ||||||||
2, 97 | 2, 110 | 2, 110 | ||||||||
0.00006 | 0.0006 | 0.003 |
Constant | 0.090 | 0.481 | 0.85 | −0.456 | 0.450 | 0.31 | −0.361 | 0.532 | 0.50 | −0.183 |
Negate | −0.419 | 0.138 | 0.003 | −0.194 | 0.137 | 0.16 | −0.199 | 0.152 | 0.20 | −0.306 |
Optim | 0.495 | 0.185 | 0.009 | 0.364 | 0.201 | 0.07 | 0.400 | 0.218 | 0.07 | 0.429 |
Tentat |
−0.340 | 0.257 | 0.19 | −0.457 | 0.241 | 0.06 | −0.540 | 0.247 | 0.03 | −0.399 |
Family |
−0.591 | 0.213 | 0.007 | −0.244 | 0.194 | 0.21 | −0.347 | 0.204 | 0.09 | −0.418 |
Achieve |
0.607 | 0.330 | 0.07 | 1.130 | 0.363 | 0.002 | 1.016 | 0.427 | 0.02 | 0.868 |
0.287 | 0.221 | 0.234 | ||||||||
7.56 | 6.07 | 6.54 | ||||||||
5, 94 | 5, 107 | 5, 107 | ||||||||
0.000005 | 0.00006 | 0.00002 |
Constant | −2.134 | 0.526 | 0.0001 | −1.010 | 0.518 | 0.05 | −1.177 | 0.570 | 0.04 | −1.572 |
Posfeel |
1.076 | 0.369 | 0.004 | 0.933 | 0.326 | 0.005 | 0.881 | 0.368 | 0.02 | 1.004 |
Tentat |
−0.467 | 0.246 | 0.06 | −0.627 | 0.225 | 0.006 | −0.620 | 0.240 | 0.01 | −0.547 |
Social | 0.116 | 0.037 | 0.002 | 0.044 | 0.034 | 0.20 | 0.055 | 0.036 | 0.13 | 0.080 |
Friends |
1.178 | 0.455 | 0.01 | 1.721 | 0.403 | 0.00004 | 1.249 | 0.380 | 0.001 | 1.449 |
0.281 | 0.275 | 0.244 | ||||||||
9.28 | 10.23 | 8.70 | ||||||||
4, 95 | 4, 108 | 4, 108 | ||||||||
0.000002 | 0.0000005 | 0.000004 |
To estimate PSE motive scores from LIWC categories for further analyses, I averaged the
1. Gender | 0. 54 (0.50) | 0. 49 (0.50) | − | −0.04 | − | −0.12 | − | 0. 32 |
− | 0. 11 | − | 0. 13 | − | 0. 08 | − | 0. 47 |
− | −0.24 |
−0.13 | 0. 15 | 0. 09 |
2. Winter |
0. 00 (1.00) | 0. 00 (1.00) | −0.08 | − | − | 0. 14 | − | −0.18 | − | −0.04 | − | 0. 42 |
− | 0. 17 | − | 0. 07 | − | −0.08 | 0. 15 | 0. 03 | −0.27 |
3. Winter |
0. 00 (1.00) | − | −0.09 | 0. 91 |
− | − | − | − | − | − | − | − | − | − | − | − | − | − | − | − | − |
4. Winter |
0. 00 (1.00) | 0. 00 (1.00) | −0.13 | 0. 14 | 0. 12 | − | − | −0.02 | − | −0.36 |
− | 0. 24 |
− | 0. 52 |
− | 0. 01 | − | −0.03 | 0. 07 | −0.23 |
0. 01 |
5. Winter |
0. 00 (1.00) | − | −0.15 | 0. 18 | 0. 16 | 0. 93 |
− | − | − | − | − | − | − | − | − | − | − | − | − | − | − |
6. Winter |
0. 00 (1.00) | 0. 00 (1.00) | 0. 18 | 0. 08 | 0. 09 | 0. 15 | 0. 15 | − | − | −0.03 | − | 0. 10 | − | 0. 14 | − | 0. 52 |
− | −0.01 | −0.14 | 0. 13 | 0. 10 |
7. Winter |
0. 00 (1.00) | − | 0. 19 |
0. 12 | 0. 08 | 0. 18 | 0. 14 | 0. 92 |
− | − | − | − | − | − | − | − | − | − | − | − | − |
8. Activity inhibition (6) | 0. 00 (1.00) | 0. 00 (1.00) | 0. 13 | 0. 08 | 0. 06 | −0.15 | −0.20 |
0. 01 | 0. 02 | − | − | −0.26 |
− | −0.53 |
− | 0. 05 | − | 0. 03 | −0.15 | 0. 25 |
−0.06 |
9. Activity inhibition (8) | 0. 00 (1.00) | − | 0. 16 | 0. 09 | 0. 09 | −0.19 |
−0.23 |
−0.05 | −0.03 | 0. 92 |
− | − | − | − | − | − | − | − | − | − | − |
10. LIWC |
−0.07 (0.39) | 0. 04 (0.39) | −0.01 | 0. 35 |
0. 31 |
0. 11 | 0. 15 | 0. 09 | 0. 13 | 0. 02 | 0. 14 | − | − | 0. 49 |
− | 0. 29 |
− | −0.04 | −0.01 | 0. 07 | 0. 01 |
11. LIWC |
−0.08 (0.37) | − | 0. 02 | 0. 33 |
0. 30 |
0. 13 | 0. 17 | 0. 11 | 0. 14 | 0. 02 | 0. 12 | 0. 95 |
− | − | − | − | − | − | − | − | − |
12. LIWC |
−0.07 (0.49) | 0. 20 (0.49) | 0. 08 | 0. 04 | 0. 05 | 0. 45 |
0. 48 |
0. 27 |
0. 28 |
−0.28 |
−0.31 |
0. 30 |
0. 33 |
− | − | 0. 06 | − | −0.06 | −0.01 | −0.09 | 0. 12 |
13. LIWC |
0. 04 (0.48) | − | 0. 05 | 0. 04 | 0. 03 | 0. 40 |
0. 48 |
0. 25 |
0. 26 |
−0.26 |
−0.31 |
0. 30 |
0. 35 |
0. 93 |
− | − | − | − | − | − | − |
14. LIWC |
−0.02 (0.54) | 0. 08 (0.50) | 0. 25 |
0. 18 | 0. 17 | 0. 05 | 0. 07 | 0. 51 |
0. 51 |
0. 05 | −0.00 | 0. 23 |
0. 25 |
0. 32 |
0. 31 |
− | − | −0.05 | −0.01 | 0. 18 | 0. 04 |
15. LIWC |
0. 17 (0.56) | − | 0. 29 |
0. 13 | 0. 11 | 0. 03 | 0. 01 | 0. 41 |
0. 49 |
0. 06 | 0. 06 | 0. 31 |
0. 35 |
0. 29 |
0. 28 |
0. 87 |
− | − | − | − | − |
16. PRF Dominance | 9. 64 (3.50) | 9. 33 (3.86) | −0.23 |
0. 07 | 0. 06 | 0. 07 | −0.02 | −0.05 | 0. 02 | 0. 16 | 0. 10 | 0. 01 | −0.01 | −0.04 | −0.01 | 0. 01 | 0. 07 | 0. 76/.81 | 0. 33 |
0. 31 |
0. 29 |
17. PRF Aggression | 7. 99 (3.41) | 6. 21 (3.02) | −0.33 |
0. 18 | 0. 17 | 0. 01 | 0. 01 | −0.01 | 0. 00 | −0.08 | −0.08 | 0. 03 | 0. 04 | −0.10 | −0.02 | −0.06 | −0.11 | 0. 21 |
0. 72/0.69 | −0.17 | −0.16 |
18. PRF Achievement | 10. 71 (3.14) | 10. 84 (2.96) | 0. 13 | 0. 06 | −0.03 | 0. 19 |
0. 14 | 0. 14 | 0. 15 | 0. 10 | 0. 04 | 0. 03 | 0. 01 | 0. 02 | −0.03 | 0. 05 | 0. 03 | 0. 26 |
−0.10 | 0. 69/0.70 | 0. 26 |
19. PRF Affiliation | 11. 50 (3.13) | 11. 70 (3.43) | 0. 11 | −0.09 | −0.08 | 0. 12 | 0. 04 | 0. 07 | 0. 09 | 0. 00 | −0.02 | 0. 05 | 0. 09 | 0. 13 | 0. 12 | 0. 03 | 0. 03 | 0. 21 |
−0.08 | 0. 04 | 0. 74/0.80 |
While the strategy of using only LIWC categories whose association with PSE motive scores could be obtained in both samples increases the generalizability of prediction formulas, it also incurs a loss in explained variance by ignoring LIWC correlates of motives scores that are specific to each sample and linguistic differences between English and German. I therefore also followed a second strategy for deriving regression estimations of PSE motive scores by finding linear solutions within each sample, and separately for 6- and 8-picture PSEs in the US sample, that fulfilled the following criteria: (1) LIWC category scores and PSE motive scores had significant zero-order correlations (Table
Like the original PSE motive scores, which did not show any substantial, replicable patterns of association with the PRF self-report measures of motivational needs, average-
LIWC scores of one motive should also be sufficiently independent of LIWC scores of a different motive, just as content-coded PSE motive scores do not show significant overlap with each other in either sample. As Table
Constant | 16.613 | 2.712 | 0.000001 | 18.034 | 2.872 | 0.000001 | 16.782 | 2.808 | 0.000001 |
Agency | −10.538 | 3.745 | 0.006 | −14.545 | 7.479 | 0.05 | −13.697 | 6.481 | 0.04 |
Agentic goal progress | 0.471 | 0.193 | 0.02 | 0.375 | 0.199 | 0.06 | 0.460 | 0.200 | 0.02 |
Interaction | 0.695 | 0.256 | 0.008 | 1.007 | 0.514 | 0.05 | 0.847 | 0.431 | 0.05 |
0.119 | 0.083 | 0.092 | |||||||
4.34 | 2.89 | 3.26 | |||||||
3, 96 | 3, 96 | 3, 96 | |||||||
0.007 | 0.04 | 0.03 | |||||||
Agency | |||||||||
≤ Md (n = 50) | −0.02 | 0.89 | 0.11 | 0.45 | 0.13 |
0.39 | |||
> Md (n = 50) | 0.53 | 0.0001 | 0.36 | 0.01 | 0.27 |
0.05 |
Results from Study 1 show that motive scores derived through established content-coding procedures can be estimated through weighted linear combinations of LIWC 2001 categories, with overall convergent validity increasing from
LIWC-derived motive scores had similar discriminant validity as content-coded motive scores vis-à-vis self-attributed motivational needs assessed with the PRF. However, they had considerably less discriminant validity relative to each other when regression solutions representative of both samples were used. Inter-motive discriminant validity improved somewhat when sample-specific LIWC estimates were used; however, as discussed in the introduction, such solutions may in turn incur a loss in generalizability. Finally, LIWC motive scores and content-coded motive scores had similar validity for two criteria: the gender difference typically seen for
McClelland (
I reanalyzed data from those 30 participants (17 women; average age: 20 years) of the Schultheiss et al. (
The study had a Condition (affiliation arousal, power arousal, no arousal) × Time (PSE pre-arousal, PSE post-arousal) × Motive (
The dependent variables in the original study were
With PSE sequence controlled for in all analyses, there was a significant Motive × Time × Condition effect for
How much did LIWC-estimated motive score changes covary with content-coded
To summarize, LIWC-estimated motive scores were sensitive to experimental arousal of
This research aimed at testing the marker-word hypothesis, which states that there are types of words whose frequency is diagnostic of individual differences in an implicit motive. Using the LIWC 2001 dictionary and software to capture a diverse range of linguistic features, I analyzed PSEs from three samples to derive and validate linear combinations of word categories that sensitively and robustly converge with content-coded measures of
In Study 1, I subjected PSEs from German and US American samples to LIWC analysis and identified correlations between LIWC categories and motive scores derived with Winter's (
In Study 2, I tested the causal validity of LIWC-based motive scores by examining their sensitivity to experimentally induced arousal of the motivational needs for power and affiliation. The presentation of movies with affiliative or power-related content, relative to a control movie with motivationally neutral content, elicited corresponding changes in LIWC-derived estimates of
Taken together, these findings provide support for the marker-word hypothesis by demonstrating that motive scores based on content coding can be approximated with satisfactory convergent validity and replicability through weighted linear combinations of LIWC's word categories, that LIWC-based scores have acceptable discriminant validity, and that they show evidence of criterion and causal validity.
In general, convergent validity between LIWC-based scores and content-coding scores were higher in the present research than in Pennebaker and King's (
How robustly do the linear combinations of LIWC categories derived in the present research converge with content-coded motive scores? How much do they depend on the specific PSE used in Study 1 to derive them and could they also be used with other types of text material to assess a person's motives? The present research, although limited to three samples, suggests that the linear combinations are surprisingly robust. They yielded scores that not only correlated with content-coded motive scores in a similar way in two samples from different cultures and with different languages. Study 2 also provided evidence that the LIWC score formulas derived with the PSE used in Study 1 can be applied to a shorter PSE with slightly different pictures without losing their validity. Moreover, a recent study on
While more research is needed to test the validity of the marker-word approach to motive assessment in a sufficiently stringent manner, these observations provide initial evidence for robust generalizability and validity of the LIWC-based motive score estimates derived in the present research. They suggest that if content-coding is not feasible, LIWC analysis of texts using the regression formulas provided in Tables
How far can the marker-word approach be taken? Is it conceivable that it could replace more traditional content-coding methods or is too much information lost when mere word frequencies instead of complex semantic information are used for motive assessment? Clearly, the word-count approach misses out on subtle contexts and semantic contingencies that render a sentence codeable or uncodeable with traditional content-coding methods. The observation that convergence coefficients are in the medium effect size range suggests that the marker-word approach captures only some aspects but by no means all of what content-coding methods identify as motive imagery. However, it should also be noted that the size of these coefficients is not substantially lower than the convergence coefficients of 0.45–0.72 Winter (
Convergence between the LIWC categories and motive scores derived with Winter's (
Moreover, the approach used in the present research has also helped to uncover aspects of language that are associated with content-coded motive scores and contribute to LIWC motive estimates' validity, but that have never been explicitly identified as critical by researchers devising content-coding measures. Take the LIWC
One shortcoming of the present approach is its reliance on convergence of LIWC categories with content-coded motive scores. Although this approach has served as a good starting point for parsing linguistic dimensions that are reliably associated with motive scores across samples, languages, and PSEs, the causal relationship between aroused motivation and LIWC category variations remains an issue. The LIWC regression formulas derived in the present research may correlate to some unknown extent with variance portions of content-coded motive scores that do not directly reflect a causative influence of motivation on story writing. Study 2 suggests that if this problem exists, it is not extensive, at least not for the assessment of
I suggest that a particularly fruitful direction for future work on the marker-word approach is to study the effects of experimentally aroused motivation on changes in language on the PSE, as assessed with the LIWC dictionary and perhaps with additional, custom-tailored dictionaries and sophisticated automated analysis tools, such as latent semantic analysis (see Landauer et al.,
The present research suggests that the marker-word hypothesis has merit. Assessment of implicit motives with a word-count approach yields scores that converge with content-coded motive measures, that predict well-documented validation criteria of implicit motive measures, and that respond sensitively to experimental arousal of motivation. The marker-word approach corroborates the idea that certain word categories should be associated with specific motive measures for a priori reasons (e.g., words related to anger, achievement, and friendship). But it also allows the a-posteriori exploration and identification of additional dimensions of language that are diagnostic of motivational dispositions. The present research thus demonstrates that the obstacles that have hampered earlier tests of the marker-word hypothesis can be overcome through the ubiquitous availability of high-speed computers, well-validated and accessible software, and the development of word-count measures that have sufficient generalizability to be useful across different languages and story-eliciting cues. I believe that this approach holds great promise for the evolution of motive assessment and should be explored broadly and vigorously in future research.
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
I acknowledge support by Deutsche Forschungsgemeinschaft and Friedrich-Alexander-Universität Erlangen-Nürnberg through the funding program Open Access Publishing. I am grateful to Yitai Seih and Phillip Heimbeck for extracting the stories from the US and German samples' PSE files in Study 1 and Anna Rüppel for transcribing the PSE stories from Study 2. Tables of LIWC and content-coding scores for individual pictures are available upon request.
Sample-specific regression solutions for predicting PSE motive scores (Winter,
(1) 6 pictures: −0.654 + 0.653*Anger1 − 0.678*Tentative1 + 0.247*Space + 0.640*Money1
(2) 8 pictures: −0.293 + 0.812*Anger1 − 0.532*Tentative1 + 1.326*Downd + 0.763*Money1
(3) 6 pictures: −0.858 − 0.631*Selfd − 0.245*Negate + 0.322*Optimism + 1.071*Achievement1 + 0.479*Metaphysical Issuesd
(4) 8 pictures: −0.998 − 0.521*Selfd − 0.325*Negate + 0.357*Optimism + 1.059*Achievement1 + 0.569*Metaphysical Issuesd
(5) 6 pictures: −1.857 + 0.206*Other + 0.870*Positive Feelings1 − 0.106*Social + 1.557*Friends1 + 0.047*Present + 0.961*Sexualityd − 0.377*Question Markd
(6) 8 pictures: −1.407 + 0.140*Other − 1.075*Anger1 + 1.066*Friends1 + 1.384*Sexualityd − 0.507*Question Markd
(7) 4.309 + 0.212*Negative Emotions − 0.841*Tentative1 + 0.317*Seed + 0.335*Exclamation Markd − 0.062*Dictionary
(8) 0.167 − 0.326*Negate + 0.490*Optimism − 0.210*Insight − 0.582*Family1 + 0.808*Achievement1 − 0.425*Question Markd
(9) −1.403 − 0.061*Words > 6 letters + 0.066*Other + 0.127*Positive Emotions + 0.838*Positive Feelings1 + 0.346*Communication + 1.384*Friends1 + 0.625*Future1