Priming Students for Performance in the Classroom
Thad Jackson
Eastern Kentucky University
Introduction
Results
Conclusions
Hundreds of studies have shown that trait concepts and
stereotypes can be activated subconsciously via so-called
‘priming’ techniques.
Below you will find the images for the four groups and, based upon examination of the priming instrument (the ‘vocabulary exercise’), the words
students most commonly associated with the individual portrayed in their respective images.
Though the experiment was rigorously conducted, the notion
that priming can have such a powerful effect should be
discounted greatly—if these findings are true then the
implications are (seriously) incredible. First, there are many
potential sources of endogeneity. Second, if priming can
have such significant and long-lasting effects on student
performance, then faculty, administrators and students could
conceivably enjoy a (virtually) free-lunch in terms of
improving student outcomes. (Also, the priming literature is
controversial to some. Many people viscerally reject the
notion that subconscious cues significantly affect behavior.)
Developing and implementing primes would be very cheap
and scalable across very large populations—including
primary, secondary and post-secondary school students. The
results are promising, but much more needs to be done.
In a laboratory setting, Bargh, Chen, and Burrows (1996)
primed experiment participants with rudeness and observed
that test subjects interrupted the experimenter more quickly
and frequently than did participants primed with politerelated stimuli. In the same study, Bargh, Chen, and Burrows
found that participants primed with an elderly stereotype
walked more slowly down the hallway when leaving the
laboratory than did control participants.
Dijksterhuis (1998) showed that participants’ scores on
general knowledge tests were significantly higher for those
who were primed for intelligence via exposure to a
photograph of a professor than control group members
who were exposed to images of soccer hooligans.
“Champion, winner, coach, football, Crimson Tide”
“Apple, iPhone, cancer/dead, genius”
I Need Your Help:
I primed students in the classroom environment. Students
were primed with “achievement” or “underachievement,”
then they listened to a lecture and quizzed on its content.
Students primed with achievement statistically, significantly
outperformed those primed for underachievement.
“College, messy, movie, funny, angry”
Materials and methods
I blindly, randomly assigned 69 University of
Alabama/Shelton State CC undergraduate students of
introductory economics to one of four groups: Nick Saban;
Steve Jobs; Bluto; The Twins. (n=64; four foreign students
did not recognize Nick Saban and one student did not
complete the experiment.)
During regularly scheduled class time, students were asked
to complete a vocabulary exercise. I blindly gave each
student an image of the individual from their assigned group
and asked them to write as many words as possible
describing their assigned image within a five minute timeframe.
Next, the class watched a video of Daniel Kahneman’s
Nobel Prize Lecture (duration: 38 minutes).
“Ears, twins, angry/dirty, brothers”
On average, students primed for achievement (Nick Saban/Steve Jobs) scored 9.5% (𝑝 = .061) higher on the quiz than ‘underachievers.’
Students primed with Nick Saban scored 4.9% (𝑝 = .36) higher than all others and students primed with Steve Jobs scored 6.9% (𝑝 = .247) higher.
Students primed with Bluto scored 10.9% (𝑝 = .16) lower than all others and students primed with the Twins scored 5.4% (𝑝 = .33) lower.
Regressions incorporating age, class standing, course grade, course load, residency, and attendance yielded similar results.
Literature cited
When the results of students whose class started at 8AM were excluded, the Saban/Jobs group scored 13.6% (𝑝 = .02) higher.
. reg percor russian
. reg percor saban
. reg percor high
Source
SS
df
MS
Model
Residual
.144182966
2.46056252
1
62
.144182966
.039686492
Total
2.60474548
63
.041345166
percor
high
_cons
Coef.
.095679
.3888889
Std. Err.
.0501974
.037648
t
1.91
10.33
Number of obs
F( 1,
62)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.061
0.000
=
=
=
=
=
=
64
3.63
0.0613
0.0554
0.0401
.19921
[95% Conf. Interval]
-.0046641
.3136315
.1960222
.4641463
. reg percor high if class1 ==0
Source
Then, students were (pop) quizzed on the content of the
Kahneman lecture and then completed a survey verifying the
integrity of priming experiment methodology. Postexperiment survey results indicate complete adherence to the
standards offered by Chartrand & Bargh (2000).
1. If you have suggestions for improving experimental
design and establishing causality, I am eager to hear your
thoughts.
2. Become a co-author on my next paper. I am running
this experiment again at EKU, but more heterogeneity
and additional co-authors and independently generated
data would improve confidence in the results. I will
provide a packet of materials and instructions and you
will conduct a field experiment during a regularly
scheduled class. Since the experiment is categorized by
IRB as Category I exempt, you will need department
chair approval.
3. I need a job for the Summer and Fall. If you have any
leads, please let me know.
SS
df
MS
.2169573
1.76413025
1
45
.2169573
.039202894
Total
1.98108755
46
.043067121
Coef.
high
_cons
.1361616
.3838384
SS
df
MS
Model
Residual
.035039456
2.56970603
1
62
.035039456
.041446871
Total
2.60474548
63
.041345166
percor
Coef.
saban
_cons
.0498339
.4263566
Std. Err.
.0541991
.0310464
t
0.92
13.73
Number of obs
F( 1,
62)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.361
0.000
Std. Err.
.0578798
.0422131
t
2.35
9.09
Number of obs
F( 1,
45)
Prob > F
R-squared
Adj R-squared
Root MSE
=
=
=
=
=
=
SS
df
MS
Model
Residual
.083360891
2.52138459
1
62
.083360891
.040667493
Total
2.60474548
63
.041345166
[95% Conf. Interval]
percor
Coef.
russian
_cons
-.109127
.4563492
-.0585085
.3642957
.1581763
.4884175
Std. Err.
.076221
.0269482
t
-1.43
16.93
Number of obs
F( 1,
62)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.157
0.000
=
=
=
=
=
=
64
2.05
0.1572
0.0320
0.0164
.20166
[95% Conf. Interval]
-.2614906
.4024805
.0432366
.5102179
. reg percor bluto
47
5.53
0.0231
0.1095
0.0897
.198
Model
Residual
.056261287
2.5484842
1
62
.056261287
.041104584
Total
2.60474548
63
.041345166
P>|t|
[95% Conf. Interval]
percor
Coef.
0.023
0.000
.0195858
.2988168
jobs
_cons
.0699924
.4263039
.2527374
.46886
Source
Source
=
64
=
0.85
= 0.3614
= 0.0135
= -0.0025
= .20359
. reg percor jobs
Model
Residual
percor
Source
SS
df
MS
Std. Err.
.0598262
.0289632
t
1.17
14.72
Number of obs
F( 1,
62)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.247
0.000
64
1.37
0.2465
0.0216
0.0058
.20274
Model
Residual
.040155028
2.56459045
1
62
.040155028
.041364362
Total
2.60474548
63
.041345166
[95% Conf. Interval]
percor
Coef.
bluto
_cons
-.0540404
.459596
-.0495984
.3684072
=
=
=
=
=
=
.1895833
.4842005
Source
SS
df
MS
Std. Err.
.0548481
.030661
t
-0.99
14.99
Number of obs
F( 1,
62)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.328
0.000
=
64
=
0.97
= 0.3283
= 0.0154
= -0.0005
= .20338
[95% Conf. Interval]
-.1636803
.3983054
.0555994
.5208865
Bargh, J., Chen, M, & Burrows, L. (1996). Automaticity of
social behavior: Direct effects of trait construct and
stereotype activation on action. Journal of Personality
and Social Psychology, 71(2), 230-244.
Chartrand, T., & Bargh, J. (2000). A practical guide to
priming and automaticity research. In H. Reis, & C.
Judd, Handbook of research methods in social and
personality psychology (pp. 253-285). New York:
Cambridge University Press
Dijksterhuis, A., & Knippenberg, A.V. (1998). The relation
between perception and behavior, or how to win a game
of trivial pursuit. Journal of Personality and Social
Psychology, 74(4), 865-877.