1. Predicting Perceptions of Intelligence
Jenna Montague1
Antonio Brewer2
Thomas Ledbetter2
Supreme Santiago3
The University of Rochester, Rochester, NY

2. Authors’ Notes Jenna Montague Antonio Brewer Thomas Ledbetter
Buffalo, NY
Sacred Heart Academy
Antonio Brewer
Brooklyn, NY
Millennium Brooklyn High School
Thomas Ledbetter
Newark, NY
Newark High School
Supreme Santiago
Bridgeport, CT
Bridgeport Military Academy
This research was supported in part by the University of Rochester Pre-College Program
Contact:

3. Background What do you think intelligence is?
What do you think being smart is?
Is there a difference?
What factors predict intelligence?

4. Introduction Spearman (1904): general intelligence
Daniel (1997): different kinds of intelligence tests
Rosenthal and Jacobson (1966): How expectations of teachers affect pupils’ IQ gain
Herrnstein and Murray (1994)
We predict that those who identify as smart will also identify as intelligent
Rosenthal: turning point

5. Methods Mixed-Methods Approach (Cresswell, 2002)
Survey Analysis (multiple linear regression)
Interviews: thematic coding
Tools
SurveyMonkey.com
SPSS (statistical package for social science)
Google docs
WordPress.com

6. Survey Format Survey Questions:
Demographics (gender, race/ethnicity, socioeconomic class)
Home Setting
Learning Difference
School Type
Learning Style ( Visual/Spatial, Verbal/Linguistic, Logical/Mathematical, Auditory)
Standardized tests scores
Self-Perception of being smart
Self-Perception of intelligence

7. Interview Format Tell me about yourself
Tell me about your family background
Do you think that standardized tests are a measure of your intelligence? Why or why not?
Should standardized tests be necessary for college admissions?
What do you think about intelligence? How do you characterize it?
Do you think you are smart?
Do you think you are intelligent?

8. 21% Logical/Mathematical
Gender
61% female
36% male
3% gender-fluid
Race
77% white
10% black
3% Hispanic
3% Asian
3% Mixed Race
2% Pacific Islander
2% Other
Home Setting
49% Suburban
21% Village
18% Urban
7% Town
5% Rural
Learning Difference
82% No
13% Not sure
5% Yes
School Type
52% Private
46% Public
2% Charter
0% Boarding
Socioeconomic Class
66% Middle Class
18% Upper Class
16% Lower Class
Learning Style
41% Visual/Spatial
33% Verbal/Linguistic
21% Logical/Mathematical
5% Auditory
Standardized Test Scores
42% N/A
25% 1700 to 2000
19% 1500 to 1700
9% less than 1500
5% 2000 or greater
Self-perception of being smart
76.67% yes
11.67% no
11.67% not sure
Self-perception of being intelligent
78.33% yes
13.33% not sure
8.33% no
Findings:
Descriptive Statistics

9. Pearson Correlation (Findings Continued)
Variable 2 3 4 5 6 7 8 9 10
1 Self-perception of intelligence
.134
.104
-.179
.286
.114
.049
.080
.142
.414
2 Gender 1
-.19
.163
.266
.174
.057
-.003
-.001
-.178
3 Race
-.314
.003
.27
-.14
-.224
.031
-.106
4 Home Setting
-.285
-.405
.183
.166
.047
-.284
5 Learning Difference
.132
.079
-.010
.373
6 School Type
-.423
.096
.008
7 Socioeconomic Class
.181
-.387
.000
8 Learning Style
-.083
9 Standardized Test Scores
.001
10 Self-perception of being smart
The only significant variables that we found were highlighted (p<0.05 or p<0.001)

10. Structured Coefficient
Predictor Variables
Model B
Se-b
Beta
Pearson’s R
Sr2
Structured Coefficient
Constant
-.651
.794
Gender
.303
.179
.259
.134
.046
.241
Race
.073
.047
.23
.104
.039
.187
Home Setting
-.034
.086
-.064
-.179
.003
-.321
Learning Difference
.07
.251
.043
.286
.001
.513
School Type
-.027
.211
-.022
.114
.0003
.205
Socioeconomic Class .1
.049
.006
.088
Learning Style
.107
.109
.145
.08
.016
.144
Standardized Test Scores
.074
.067
.158
.142
.019
.255
Self-perception of being smart
.483
.156
.462
.414
.153
.743

11. Best Fit Line

12. Qualitative Data 2 Participants Different perspectives on intelligence
First Participant: Intelligence and being smart are synonymous
Second Participant: Although intelligence and being smart are related, they are different concepts
Main theme: no clear consensus on what people perceive as being smart and being intelligent

13. Conclusion/Discussion
Quantitative: being smart predicts whether one believes that they are intelligent
Qualitative: there is no clear consensus
Even though that we showed that those who believe that they are smart will, statistically, also believe that they are intelligent, the qualitative data shows that there is no clear consensus on public perceptions of intelligence.

14. References Brooks-Gunn, J. , & Duncan, G. J. (1997)
References Brooks-Gunn, J., & Duncan, G. J. (1997). The Effect of Poverty on Children. Children and Poverty, 7(2), Creswell, J. (2002). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research. Saddle River, NJ: Prentice Hall. Daniels, M. H. (1997). Intelligence Testing. American Psychologist, 52(10), Frey, M. C., & Detterman, D. K. (2004). Scholastic Assessment or g? The Relationship Between the Scholastic Assessment Test and General Cognitive Ability. Psychological Science, 15, Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. New York: Basic Books. Gould, S. J. The mismeasure of man. New York, NY: Norton. Herrnstein, R. J., & Murray, C. (1994). The bell curve: Intelligence and class structure in American life. New York: Free Press. Jacoby, R., & Glauberman, N. (Eds.). (1995). The Bell Curve debate: History, documents, opinions. New York: Times Books. Siegler, R. S. (1992). The other Alfred Binet. Developmental Psychology, 28, Spearman, C. (1904). “General Intelligence,” objectivity determined and measured. American Journal of Psychology, 13, Thorndike, E. L., et al. (1921). Intelligence and its measurement: A symposium. Journal of Educational Psychology, 12, , , Thurstone, L. L. (1947). Multiple factor analysis: A development and expansion of The Vectors of Mind. Chicago: University of Chicago Press. U.S. Census Bureau (2013) US Census, Summary File 1 (SF1). QT-P7. Socioeconomic class alone: Retrieved July 16, 2015 from
Thank you to the University of Rochester Pre-College Program for your time and support, we really appreciate having this learning opportunity in the Pre-College Program and the opportunity to do this research. We accept donations…

15. Questions?