Intelligence Jenna M ontague 1 Antonio Brewer 2 Thomas Ledbetter 2 Supreme Santiago 3 The University of Rochester Rochester NY Authors Notes Jenna Montague Buffalo NY Sacred Heart Academy ID: 733761
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Slide1
Predicting Perceptions of Intelligence
Jenna
M
ontague
1
Antonio
Brewer
2
Thomas Ledbetter
2
Supreme Santiago
3
The University of Rochester, Rochester, NYSlide2
Authors’ Notes
Jenna Montague
Buffalo, NY
Sacred Heart AcademyAntonio BrewerBrooklyn, NYMillennium Brooklyn High SchoolThomas LedbetterNewark, NYNewark High SchoolSupreme SantiagoBridgeport, CTBridgeport Military AcademyThis research was supported in part by the University of Rochester Pre-College ProgramContact: precollege@Rochester.eduSlide3
BackgroundWhat do you think intelligence
is?
What do you think being smart is?Is there a difference?What factors predict intelligence?Slide4
IntroductionSpearman (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 Slide5
Methods
Mixed-Methods Approach (
Cresswell
, 2002) Survey Analysis (multiple linear regression)Interviews: thematic codingToolsSurveyMonkey.comSPSS (statistical package for social science)Google docsWordPress.comSlide6
Survey FormatSurvey Questions:
Demographics (gender, race/ethnicity, socioeconomic class)
Home Setting
Learning DifferenceSchool TypeLearning Style ( Visual/Spatial, Verbal/Linguistic, Logical/Mathematical, Auditory)Standardized tests scoresSelf-Perception of being smartSelf-Perception of intelligenceSlide7
Interview FormatTell 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?Slide8
Findings:
Descriptive Statistics
Gender
61% female36% male3% 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% No13% Not sure5% Yes School Type52% Private46% Public2% Charter0% Boarding Socioeconomic Class66% Middle Class18% Upper Class16% Lower Class Learning Style41% Visual/Spatial33% Verbal/Linguistic21% Logical/Mathematical5% Auditory Standardized Test Scores42% N/A25% 1700 to 200019% 1500 to 17009% less than 15005% 2000 or greater Self-perception of being smart76.67% yes11.67% no11.67% not sure Self-perception of being intelligent78.33% yes13.33% not sure8.33% no Slide9
Pearson Correlation (Findings Continued)
Variable
2
34567
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-.1783 Race 1-.314.003.27-.14-.224.031-.1064 Home Setting 1-.285-.405.183.166.047-.2845 Learning Difference 1.132.079-.14-.010.3736 School Type 1-.423.096.142.0087 Socioeconomic Class 1.181-.387.0008 Learning Style
1.166-.0839 Standardized Test Scores 1.00110 Self-perception of being smart 1
The only significant variables that we found were highlighted (p<0.05 or p<0.001)Slide10
Predictor Variables
Model
B
Se-bBetaPearson’s RSr2Structured Coefficient
Constant
-.651
.794
Gender
.303
.179
.259
.134
.046
.241
Race
.073
.047
.23
.104
.039.187Home Setting -.034.086-.064-.179.003-.321Learning Difference.07.251.043.286.001.513School Type-.027.211-.022.114.0003.205Socioeconomic Class.114.187.1.049.006.088Learning Style.107.109.145.08.016.144Standardized Test Scores.074.067.158.142.019.255Self-perception of being smart.483.156.462.414.153.743Slide11
Best Fit LineSlide12
Qualitative Data2 Participants
Different perspectives on intelligence
First Participant: Intelligence and being smart are synonymousSecond Participant: Although intelligence and being smart are related, they are different conceptsMain theme: no clear consensus on what people perceive as being smart and being intelligentSlide13
Conclusion/DiscussionQuantitative: 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.Slide14
References
Brooks-Gunn, J., & Duncan, G. J. (1997). The Effect of Poverty on Children.
Children and Poverty,
7(2), 55-71.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), 1038-1045.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, 373-378.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,
179-190.
Spearman, C. (1904). “General Intelligence,” objectivity determined and measured.
American Journal of Psychology, 13, 201-293.Thorndike, E. L., et al. (1921). Intelligence and its measurement: A symposium. Journal of Educational Psychology, 12, 123-147, 195-216, 271-275.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). 2013 US Census, Summary File 1 (SF1). QT-P7. Socioeconomic class alone: 2013. Retrieved July 16, 2015 from http://factfinder.census.govSlide15
Questions?