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Background and Purpose
Creativity and creative problem solving (CPS) is an impor-
tant educational goal with a long and substantial research
history (Fasco, 2000-2001; Strom & Strom, 2002;
Treffinger, Schoonover, & Selby, 2013; Treffinger,
Solomon, & Woythal, 2012). Indeed, the goal preparing
learners to be able to effectively deal with ever increasing
and complex problems of the modern world is more and
more a concern of business and political leaders as well as
educators (Coy, 2000; National Governors Association,
Council of Chief State School Officers, and Achieve, Inc,
2008; Partnership for 21st Century Schools, 2007). Many
corporate, government, and nonprofit professional organi-
zations worldwide have called for expanded attention to
education for creativity. Creativity, knowledge, and access
to information are . . . “powerful drivers of development”
(p. 3) and “Creativity is . . . the key resource in the knowl-
edge economy, leading to innovation and technological
change and conferring competitive advantage on businesses
and national economies . . .” (United Nations Committee on
Trade and Development and the UNDP Special Unit for
South-South Cooperation, 2008, p. 202).
What is known about creativity and CPS is that it is a
complex phenomenon, involving skills of both idea genera-
tion and evaluation (Lubart, 2001; Mumford et al., 2012;
Treffinger, Isaksen, & Stead-Dorval, 2006). Generative
thinking involves developing many new possibilities.
Generation of ideas is an open exploration or search for ideas
in which a person generates many ideas (fluency in think-
ing), varied ideas and new perspectives (flexibility), and
unusual or novel ideas (originality). According to Treffinger
and Isaksen (2005), generating ideas is viewed by many peo-
ple as “creative,” and is sometimes (in error) equated with
“brainstorming.” Generating ideas is but one important com-
ponent and stage in CPS and brainstorming is one specific
tool (among many) for generating options.
On the contrary, evaluative thinking involves exploring
ways to make promising options into workable solutions and
preparing for successful implementation. Evaluation of ideas
is essential when solutions are presented and individuals
must develop the solutions and make them as appropriate,
useful, or workable as possible (Gibson & Mumford, 2013).
Applying planned strategies and tools to analyze, develop,
and refine possibilities, and to transform them into promising
592679SGOXXX10.1177/2158244015592679SAGE OpenPolitis and Houtz
1Ramsey, NJ, USA
2Fordham University, New York, NY, USA
John C. Houtz, Fordham University, 113 West 60th Street, New York,
NY 10023, USA.
Effects of Positive Mood on Generative
and Evaluative Thinking in Creative
Jennifer Politis1 and John C. Houtz2
The goal of this study was to examine the role of positive mood on generative and evaluative thinking in creative problem
solving. Participants included 89 middle school students who watched either a positive or neutral mood video program.
After students watched the video, they completed the Positive and Negative Affect Schedule (PANAS) scale to determine
their current mood. Participants were then divided into three groups and given a divergent thinking task to complete. Group
A was asked to generate potential solutions to a problem (generative thinking). Group B was given one solution to the
problem that had been offered by participants’ peers in a previous pilot study and then asked to generate possible advantages
to this particular solution (evaluative thinking). Group C was given the potential solution but asked to generate potential
disadvantages (also evaluative thinking). Students in the positive mood condition were significantly more fluent than those
who watched the neutral video. Students in the neutral mood condition generated more disadvantages than advantages, but
this difference was significant only at p < .10. Implications and limitations of these results were discussed.
mood, creativity, divergent thinking, evaluative thinking, creative problem solving
2 SAGE Open
solutions is evaluative thinking. In its latest formulation,
instruction in CPS (V. 6.1™; Treffinger et al., 2006;
Treffinger et al., 2013) involves both generative and evalua-
tive thinking tools. At each stage of the creative problem-
solving process, the individual applies both types of tools,
and with each successive step or task in the process, the indi-
vidual engages in appraisal or evaluative activity, providing
information and insight to guide further efforts.
Although the CPS model had many revisions over the last
50 years (Isaksen & Treffinger, 2004), CPS has been shown
to be a powerful tool and effective method for igniting cre-
ative potential and making productive change (Christie &
Kaminski, 2002; Freeman, Wolfe, Littlejohn, & Mayfield,
2001; Isaksen, 2008; Isaksen & De Schryver, 2000;
McCluskey, Baker, & McCluskey, 2005; Puccio, Firestein,
Coyle, & Masucci, 2006; Scott, Leritz, & Mumford, 2004).
However, numerous researchers have argued that skill build-
ing is of limited utility unless more personal, affective quali-
ties in the problem situation are considered (Davis, Kaufman,
& McClure, 2011; Fernandez-Abascal & Martin Diaz, 2013;
Stanko-Kaczmarek, 2012). No less a major figure than J. P.
Guilford (1962, 1977) long ago proposed that personal quali-
ties of the individual were the real determinants of a creative
With regard to responding to individuals’ affective quali-
ties, teachers and trainers have had a limited number of tools
(Beghetto, 2010). But, one area that has interested research-
ers for many years is that of the effect of mood on perfor-
mance (Martin, 1990; Westermann, Spies, Stahl, & Hesse,
1996). “Mood induction procedures” (MIPs) include a broad
diversity of methods “whose aim is to provoke in an indi-
vidual a transitory emotional state in a non natural situation
and in a controlled manner . . . an experimental analogue of
the mood that would happen in a certain natural situation”
(García-Palacios y Baños, 1999, p. 16). MIPs have been used
in both clinical and educational situations, and involved
induction of both positive and negative feelings.
A substantial amount of research exists on the relationship
of mood to creativity (Abele, 1992; Clapham, 2001; Greene
& Noice, 1988; Isen, 2000; Isen & Daubman, 1984; Isen,
Daubman, & Nowicki, 1987; Isen, Johnson, Mertz, &
Robinson, 1985; Isen, Rosenzweig, & Young, 1991;
Kaufmann, 2003; Vosburg, 1998a, 1998b). Isen’s studies
have consistently shown that positive mood inductions lead
to better, more efficient decision making, including decision
making requiring more careful, systematic, and thorough
processing. Several recent studies continue to show benefi-
cial effects of positive affect (Eubanks, Murphy, & Mumford,
2010; Xiao, Wang, Chen, Zheng, & Chen, 2015).
However, not all of the literature supports the position
that positive mood broadens one’s ability to think (Harmon-
Jones, Gable, & Price, 2013). Kaufmann and Vosburg (1997,
2002; Vosburg & Kaufmann, 1999) found that negative
affect facilitated divergent thinking late in the idea genera-
tion process. Kaufmann (2003) later concluded that the
positive mood–creativity link is not guaranteed. The results
from some mood induction studies indicate that participants
in a happy or positive mood perform many tasks more poorly
than participants with neutral or sad affect. Happy partici-
pants tended to be less critical and analytical in their thinking
and are more easily persuaded by weak arguments (Bless,
Hamilton, & Mackie, 1992; Mackie & Worth, 1989), and
more likely to make inaccurate judgments, even in situations
where there are objective criteria (Sinclair & Mark, 1995). In
the Sinclair and Mark study, participants with statistical
training were asked to estimate the magnitude and direction
of correlation coefficients associated with several scatter
plots. Participants who were in a positive or happy mood
processed the material less systematically, took less care, and
consequently made more errors than did participants in a
neutral or negative mood.
One reason for these different findings may be the fact
that researchers have more often examined only the genera-
tion of solution possibilities—fluency or divergent thinking.
When evaluative thinking is called for, results of positive
moods are less clear. Therefore, the purpose of the present
study was to investigate the effect of positive mood on both
generative and evaluative thinking in the creative problem-
Participants were 89 sixth-, seventh-, and eighth-grade mid-
dle school students from a middle-class suburban school dis-
trict, approximately 40 miles from New York City.
Participants ranged in age from 11 to 14. There were 75
Caucasian, 11 Asian, 2 Hispanic, and 1 African American
students. In terms of academic achievement, each grade
scored above the 90th percentile on recent statewide tests in
Instruments and Materials
Positive and Negative Affect Schedule (PANAS). The PANAS
(Watson & Clark, 1988) is a 20-item list of positive and neg-
ative adjectives scored on a 5-point scale that asks the par-
ticipant to respond to given words describing emotions and
feelings and indicate to what degree the participant feels this
way (1 = very slightly, not at all; 2 = a little; 3 = moderately;
4 = quite a bit; 5 = extremely) for a given time period (at the
moment). The positive affect items, which are interspersed
with the negative affect items, include interested, excited,
strong, enthusiastic, proud, alert, inspired, determined,
attentive, and active. The negative affect items are distressed,
upset, guilty, scared, hostile, irritable, ashamed, nervous, jit-
tery, and afraid.
Internal consistency coefficients range from .84 to .90.
Test-retest reliabilities range from .39 to .71, with the higher
Politis and Houtz 3
coefficients reported for the longer durations. The PANAS
scale is one of the most widely used measures of affectivity
and has been reported with excellent psychometric proper-
ties with U.S. samples. The scale, which has been validated
across diverse time frames, response formats, and cultures,
measures positive and negative affect (mood) separately,
treating them as two dimensions of a mood state rather than
as the opposite ends of a continuum.
Mood induction videos. Participants were randomly assigned
to either a positive induction experience group or the control
group. A 20-min nature video was used for the control group.
This video was used to not elicit any emotion at all. The
video segment was taken from the Planet Earth DVD series
from the Discovery Channel. A 20-min comedy video was
used to induce a positive mood experience. The video con-
sisted of excerpts from a Bill Cosby humor routine from the
video titled, Himself. The routine was specifically selected to
be essentially free from aggressive content, dealing with
such topics as Cosby’s experiences as an expectant and new
father, the birth of his child, the training of his son to be a
successful athlete, an encounter at the dentist office, and so
It should be noted that when examining the mood induc-
tion studies, the majority of research did not specify the type
of video used. However, in all studies with a neutral mood or
control group, video of nature was used. The most common
videos used for the positive mood induction was a comedy
video of famous comedians such as Robin Williams, Eddie
Murphy, and Bill Cosby. The majority of the mood research
focused on college participants and adults. Given the ages of
the participants, a comedy video of Bill Cosby was more
appropriate given the rating of Parental Guidance (PG).
Previous studies (Dienstbier, 1995; Lucas & Baird, 2004)
used the same comedy routine for the mood induction proce-
dure with successful results.
Before beginning the present study, both video segments
were shown to colleagues and participants in the pilot study
to assess whether their reactions matched the expected ones.
The PANAS scores reported a positive mood after the com-
edy video as the nature video elicited a neutral mood because
all participants did not score high on the positive or negative
Problem-solving task. During the pilot study, participants were
given sample questions that consisted of generative and eval-
uative types of thinking. From these questions, the solutions
given were then used for the current study. The hypothetical
problem of the cell phone company was used as participants
showed the most interest in that problem; it was relatable and
they were familiar with the content. When considering the
problem-solving task, a model used by Treffinger and Feld-
husen (1998) was considered for productive thinking applied
to CPS. This model stated that three areas that should be con-
sidered in the problem were the foundations, realistic tasks,
and real-life opportunities and challenges. Given the age of
the participants, it was important to use a task that was age
and level appropriate.
The institutional review board of Fordham University
reviewed and approved this study at the proposal stage for
adherence to ethical standards and protection of human par-
ticipants. School administrators were contacted about the
nature of this research and gave their permission. Parents of
prospective student participants were contacted and 89
returned signed consent forms for their sons and daughters to
The study was conducted in students’ guidance classes,
which are classes they have once a week with their guidance
counselor to discuss personal and social concerns. In each
class, participants were separated randomly into two groups.
Participants then watched either the comedy video or the
neutral video. Each movie was 20-min long.
Nonparticipant students were excused from guidance
class and attended supervised library and/or study hall.
After students watched the movie, they completed the
PANAS scale. Then, students were given a piece of paper
and a pencil. Within the positive mood induction group and
the neutral mood induction groups, participants were further
divided randomly into three conditions via distinct written
directions: (a) Group A participants were asked to generate
ideas to a problem situation (possible ideas to reverse a com-
pany’s declining cell phone sales), (b) Group B was asked for
the advantages of using one possible solution to the “declin-
ing sales” problem, and (c) Group C was asked for the disad-
vantages of using the one possible solution to the “declining
sales” problem. Participants were given 15 min to work on
their respective tasks. The possible solution to the “cell
phone sales” problem that was given to Groups B and C was
selected from those offered by different participants in an
earlier, separate pilot study.
It should be noted that the lead author was the experi-
menter for all the groups as she was the teacher for the guid-
ance class where the participants were examined. While the
participants were completing the problem-solving tasks, the
lead author scored the PANAS to determine whether any par-
ticipant scored extremely high in the negative mood cate-
gory. This was not the case for any participant. If any such
case or cases had occurred, a school counselor was available
to assure adequate and appropriate assistance to the
Table 1 presents the means, standard deviations, standard
error, and range of all variables in the study. These variables
4 SAGE Open
Table 3. Analysis of Variance of Grade Code by Mood Induction
and Directions Groups.
Source SS df MS F
Mood 2.636 1 2.636 4.917*
Directions 0.905 2 0.453 0.844
Mood × Directions 0.739 2 0.370 0.690
Error 44.502 83 0.536
Note. SS = Sums of Squares; MS = Mean Squares.
*p < .05.
Table 4. Analysis of Covariance of Fluency Scores by Mood by
Variable SS df MS FES Power
Grade code 16.649 1 16.649 3.166 .037 .420
Mood 72.742 1 72.742 13.833* .144 .957
Directions 79.295 2 39.647 7.540* .155 .937
Mood × Directions 2.658 2 1.329 0.253 .006 .089
Error 431.195 82 5.258
Note. SS = Sums of Squares; MS = Mean Squares; ES = Effect Size.
**p < .01.
include the math and language arts standardized test scores,
the positive and negative PANAS scores, and fluency scores,
that is, the number of responses generated by participants in
each directions group. Pearson intercorrelations among the
variables were also computed. Only the correlation between
the standardized state math and language arts scores
(r = .457, p < .01) and between the language arts scores and
fluency (r = .239, p < .05) were significant.
Test of treatment fidelity. Analyses of variance were computed
to compare the two mood induction groups (positive and neu-
tral) on the positive and negative PANAS scores. Table 2
presents these results. There was a significant F ratio compar-
ing the two mood groups on the positive PANAS scores. The
means of the positive and neutral groups were 35.15 (SD =
6.98) and 26.93 (SD = 8.66), respectively. The positive mood
induction group scored significantly higher on the positive
PANAS score. The F ratio for the comparison of negative
PANAS scores was, technically, not statistically significant
(p = .052). The means, however, for the positive and neutral
induction groups on the negative PANAS scores were 15.71
(SD = 4.00) and 18.07 (SD = 7.11), respectively. The neutral
mood induction group had a higher negative PANAS score.
Tests of possible covariates. Analyses of variance comparing
Mood Induction and Directions groups on standardized lan-
guage arts test scores were computed to determine if the lan-
guage arts scores would be an appropriate covariate in tests
of the study hypotheses. There were no significant F ratios.
A second ANOVA was computed using the dummy code for
grade level. In Table 3, there is a significant F ratio for the
main effect of Mood Induction group. Further tests of homo-
geneity of variance, normality, and correlation between the
dependent measure (fluency of response) and grade code
across each experimental group suggested that the use of
grade code as a covariate was appropriate.
Tests of the hypotheses
Tests of the first hypothesis. Hypothesis 1 was that the posi-
tive mood induction group would generate more responses
than the neutral mood induction group. Table 4 presents the
results of the 2 × 3 factorial analysis of covariance. As can be
seen, the main effects of Mood Induction Group and Direc-
tions were both significant (Fs = 24.572 and 3.88, dfs = 1,
87, ps < .01, respectively). Eta square for the main effects of
Mood and Directions ranged from .14 to .16. These effect sizes
are considered small to moderate (Cohen, 1988). However, the
statistical power of each significant F ratio was above .9.
Table 5 presents the means and standard deviations of the
various groups being compared. The positive mood induction
Table 1. Means, Standard Deviations, Standard Errors, Minimum, and Maximum Scores of Study Variables (N = 89).
Variable M SD SE Minimum Maximum
Math scores 226.00 27.02 2.86 136 300
Language arts scores 225.00 19.07 2.02 169 280
Positive PANAS 31.36 8.78 0.93 11 50
Negative PANAS 16.80 5.74 0.61 10 34
Fluency 6.29 2.75 0.29 2 11
Note. PANAS = Positive and Negative Affect Schedule.
Table 2. Analyses of Variance of PANAS Scores.
Source SS df MS F
Mood 1,493.735 1 1,493.735 24.572**
Error 5,288.760 87 60.790
Mood 123.662 1 123.662 3.880*
Error 2,772.697 87 31.870
Note. PANAS = Positive and Negative Affect Schedule; SS = Sums of
Squares; MS = Mean Square.
*p < .05. **p < .01.
Politis and Houtz 5
group generated more responses overall than the neutral
mood induction group. As for the directions groups, the over-
all means of the “solutions” and “disadvantages” groups were
greater than the “advantages” groups. These differences were
statistically significant according to both Scheffe and
Neuman–Keuls’ post hoc procedures beyond the .01 (Kirk,
Tests of the second and third hypotheses. Hypothesis 2 was
that participants in the positive mood induction group would
generate more advantages than the neutral group. Hypoth-
esis 3 was that participants in the neutral mood induction
group would generate more disadvantages than participants
in the positive mood group. Simple main effects (Winer,
1971) were computed comparing the fluency of positive and
neutral mood group students in the advantages directions
group and the disadvantages directions groups. The F ratios
for these analyses were 3.29 (df = 1, 12, p < .10) and 4.194
(df = 1, 18, p < .10), respectively. Although these tests are
significant only at .10, the means of the disadvantages direc-
tions groups are larger by more than two points compared
with the advantages groups. Hypothesis 2 was not supported,
and results in favor of the third hypothesis were significant
at only .10.
In this study, the question was what effect mood would have
on both generative and evaluative thinking. Would the effects
be the same? Or, would positive or neutral moods affect gen-
erative and evaluative thinking in different ways? The mood
literature suggested that a positive mood would result in
more ideas being generated (Fredrickson, 2001; Isen et al.,
1987). The creative problem-solving literature clearly argues
for the avoidance of negative attitudes and negative, judg-
mental, evaluative comments during idea generation stages
(Guilford, 1962; Isaksen, Dorval, & Treffinger, 2000;
Osborn, 1966; Sternberg & Lubart, 1996). Thus, it was
hypothesized that a positive mood condition would result in
a greater number of ideas being generated. This hypothesis
was supported. The main effect of mood on fluency was sta-
tistically significant with the positive mood group generating
more ideas than students in the neutral condition. This result
was also evident in the interaction effect means, with the
mean of the positive-mood-solutions group greater by more
than four and two ideas, respectively, compared with the
positive-mood-advantages- and positive-mood-disadvan-
However, a differential effect of mood on evaluative think-
ing was not demonstrated. There is some literature that sug-
gests that in the evaluative domain, it is easier for individuals
to be more negative than positive (Isaksen et al., 2000). That
is, it is more likely that people can think of what is wrong
with an idea rather than what is good about a new idea.
However, in this study, it was hypothesized that a positive
mood would reverse that relationship. Thus, it was expected
that the positive mood group given directions to think of
advantages about a potential solution would generate more
ideas than would participants given directions to think of dis-
advantages about the potential solution. This was not the find-
ing in this study. The overall main effect of directions clearly
showed that more disadvantages than advantages were gener-
ated. Furthermore, the positive-mood-disadvantages group
generated more ideas than the positive-mood-advantages
Are we a pessimistic population? Are we hypercritical?
Do people easily learn to be more negative rather than posi-
tive? This study did not address these questions, but one
explanation may be offered for the greater number of disad-
vantages. Although the solution presented to student partici-
pants was one that was generated by participants’ peers in the
pilot study, perhaps it is more difficult to think of advantages
without greater information about how the solution was
developed, how it might be implemented, or what, exactly,
might be the criteria for a good solution. Participants in the
disadvantages group (neutral or positive mood) could easily
“turn around” the three points just mentioned and make them
negatives. For example, the solution may not be a good one
Table 5. Means and Standard Deviations of Fluency by Mood Induction and Directions Groups.
Variable n M SD SE Minimum Maximum
Positive mood 48 7.479 2.361 .340 3 11
Neutral mood 41 4.902 2.538 .396 2 10
“Solutions” group 28 7.143 2.368 .448 3 11
“Advantages” group 27 4.556 2.026 .390 2 10
“Disadvantages” group 34 6.971 2.970 .509 2 11
Positive mood × “Solutions” 19 7.947 2.297 .527 3 11
Positive mood × “Advantages” 10 3.941 1.190 .600 3 9
Positive mood × “Disadvantages” 19 5.667 2.236 .513 4 11
Neutral mood × “Solutions” 9 5.444 1.509 .503 3 8
Neutral mood × “Advantages” 17 5.600 1.887 .458 2 10
Neutral mood × “Disadvantages” 15 8.000 3.331 .861 2 10
6 SAGE Open
because we don’t know how the solution was developed (or
by whom); it might not be able to be put into practice (that is,
it is impractical, it won’t work); or the solution won’t com-
pletely solve the problem (that is, we don’t know how to
judge if it works well enough). Put another way, it may take
more technical knowledge and background to argue in favor
of a solution.
As described above, the latest model of CPS (Version
6.1™; Treffinger et al., 2006) stresses a ubiquitous context of
evaluative thinking. As individuals work on problems, they
uncover, generate, or transform information that affects the
successive steps they may take. Treffinger et al. refer to this
as appraising tasks, and it is an essential process integral to
effective problem solving. We may conclude the present
study has again demonstrated that mood is a variable that can
affect generative thinking. Inducing a positive mood can be a
useful method in problem solving and instructional situa-
tions when fluency of ideas is an important outcome. But the
question remains whether and how mood can affect evalua-
tive thinking. Additional research is needed to answer the
question “Can a positive mood increase individuals’ positive
task appraisals or reduce negative appraisals?”
The research reported herein was completed in partial fulfillment of
the requirement for the doctor of philosophy in educational psy-
chology from Fordham University.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
The author(s) received no financial support for the research and/or
authorship of this article.
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Jennifer Politis is a licensed professional counselor and educa-
tional psychologist. She earned her doctorate in Educational
Psychology and a Master‘s degree in Counseling from Fordham
University. She has a private therapy practice in Ramsey, NJ and
8 SAGE Open
specializes in adolescents and adults with depression, anxiety, and
relationship issues. She also develops therapeutic products for chil-
dren and parents to assist family communication and enhance emo-
tional connectedness. Her website: www.drjenpolitis.com
John C. Houtz is Bene Merenti professor of Educational
Psychology in the Graduate School of Education of Fordham
University, New York City. He earned his doctorate at Purdue
University in 1973 under the direction of John Feldhusen. He also
is research fellow for the Center for Creative Learning, Inc., a
licensed psychologist, and during his 42-year career at Fordham, he
has served two terms each as department chair and associate dean.
He has authored several books and published numerous research
papers on creativity and problem solving style. He may be con-
tacted at email@example.com