Mengxue Hu Reflection Paper #2 10/20/2015 Topic: “explain how your race and class has influenced your life experiences.” I was born and raised in China which makes Race and ethnicity has not influenced me that much. I have not considered any of these problems before I came to the states. From what I heard, racial discrimination is common especially in the United States. People make their decisions not on one’s achievement but on their racial group. For our Asian especially Chinese, the situation of model minority is around us in many ways. After I finished my freshman year, I was looking for a job on campus, I wasn’t sure what I wanted to do until my math professor found me. When I took her class, I basically knew all the stuff because those were what I have learned when I was in grade 9 in China. When I took a nap on her class, she ignored me because she knew I knew all of those without learning. I became the math tutor without a doubt. When I was trying to help students out, a lot of student were asking me some questions like “how did you know that without using the calculator?” or “I heard all Chinese are good at math, is that true?”. After I learned the lecture from class, I realized that belongs to model minority. Roadmap: Stroop Overview: Lab 2 introduces you to the nuts and bolts of another classic experimental psychology paradigm, the Stroop effect. Data collection will occur on the computers. Each student will complete a 20-30 minute Stroop experiment. The data will be analyzed and reported in a full APA style research report. The main goal of this experiment is provide a concrete example of a 2x2 Factorial Design. As well, we will learn to relate theory and data.You will be taught about the horse-race model of Stroop, and you will use this model to predict the data from the class experiment. The class experiment has two goals. First, to replicate the Stroop effect. Second, to test a manipulation that will reduce the size of the Stroop effect. In this case, the manipulation will be task. For half of the trials, you will identify the color, and for the other half of the trials you will identify the word. In your research paper you will be required to introduce the Stroop effect, and explain the horse race model. You will explain how the horse race model can be used to predict which task will lead to the largest Stroop effect. You will describe the methods and results. The results will be reported in a figure or table (your choice). NOTE: when you report the results you MUST report all main effects, the interaction, and any necessary post-hoc tests. Things you will learn: Using reaction time as a dependent measure 2x2 Factorial designs Reading and citing primary source material Predicting data based on a theory Control in experimental design Background on the Stroop paradigm: The Stroop paradigm involves the identification of a bi-valent stimulus. For example, you could be presented with a word, that is written i ...

1. Mengxue Hu
Reflection Paper #2
10/20/2015
Topic: “explain how your race and class has influenced your
life experiences.”
I was born and raised in China which makes Race and
ethnicity has not influenced me that much. I have not
considered any of these problems before I came to the states.
From what I heard, racial discrimination is common especially
in the United States. People make their decisions not on one’s
achievement but on their racial group. For our Asian especially
Chinese, the situation of model minority is around us in many
ways.
After I finished my freshman year, I was looking for a job
on campus, I wasn’t sure what I wanted to do until my math
professor found me. When I took her class, I basically knew all
the stuff because those were what I have learned when I was in
grade 9 in China. When I took a nap on her class, she ignored
me because she knew I knew all of those without learning. I
became the math tutor without a doubt. When I was trying to
help students out, a lot of student were asking me some
questions like “how did you know that without using the
calculator?” or “I heard all Chinese are good at math, is that
true?”. After I learned the lecture from class, I realized that
belongs to model minority.

2. Roadmap: Stroop
Overview:
Lab 2 introduces you to the nuts and bolts of another classic
experimental psychology paradigm, the Stroop effect. Data
collection will occur on the computers. Each student will
complete a 20-30 minute Stroop experiment. The data will be
analyzed and reported in a full APA style research report.
The main goal of this experiment is provide a concrete example
of a 2x2 Factorial Design. As well, we will learn to relate
theory and data.You will be taught about the horse-race model
of Stroop, and you will use this model to predict the data from
the class experiment.
The class experiment has two goals. First, to replicate the
Stroop effect. Second, to test a manipulation that will reduce
the size of the Stroop effect. In this case, the manipulation will
be task. For half of the trials, you will identify the color, and
for the other half of the trials you will identify the word.
In your research paper you will be required to introduce the
Stroop effect, and explain the horse race model. You will
explain how the horse race model can be used to predict which
task will lead to the largest Stroop effect. You will describe the
methods and results. The results will be reported in a figure or
table (your choice).
NOTE: when you report the results you MUST report all main
effects, the interaction, and any necessary post-hoc tests.
Things you will learn:
Using reaction time as a dependent measure 2x2 Factorial
designs
Reading and citing primary source material Predicting data
based on a theory
Control in experimental design Background on the Stroop
paradigm:
The Stroop paradigm involves the identification of a bi-valent
stimulus. For example, you could be presented with a word, that
is written in a particular color. Dimension 1 is the word (e.g.,
BLUE), and dimension 2 is the color (e.g., RED). The resulting

3. stimulus would look like this: BLUE. In this case the word and
color do not match, this is called an incongruent stimulus. A
congruent stimulus occurs when the word and color dimension
match (e.g., BLUE). In a standard Stroop experiment you would
be presented with these kinds of congruent and incongruent
items. The task usually involves identifying the color dimension
as quickly as possible, while ignoring the word. The Stroop
effect itself is the finding that reaction times to identify the
color dimension are faster for congruent trials (when the word
matches) than incongruent trials (when the word mismatches).
The effect is interesting because people are unable to ignore the
word dimension even though it is not part of their task. The
Stroop effect is usually used to measure your ability to
selectively attend to information in your environment. The
review paper by Macleod (1990) demonstrates the popularity of
Stroop research, and the many different ways that this task has
already been studied.
Background readings:
Stroop, J. R. (1935). Studies of interference in serial verbal
reactions. Journal of Experimental Psychology, 18, 643-662.
Macleod, C. M. (1991). Half a century of research on the Stroop
effect: An integrative review. Psychological Bulletin, 109, 163-
203.
-Pages 187-188 describe the horse-race model (it is termed the
relative speed of processing account).
Writing the paper
Refer to the lab manual for more resources on writing APA
style research-reports.
1. Use the same general APA formatting rules that you learned
in lab 1. 2. Create a suitable title for the paper
3. Write the abstract :
-no more than 150 words
-The aim is to very briefly describe the main experimental aims,
how they address the theory at hand, and the basic pattern of
results.
The introduction (around 2 double-spaced pages)

4. The goal of the introduction is to put the research into a broader
context, and then narrow the focus to describe the specific
research aims.
A. Opening section: (starting broad) - about 1 paragraph
-Define the general problem of selective attention.
-Give an anecdote that describes a real-world situation
involving the need to select task- relevant from irrelevant
information
-Link this real world situation to the Stroop paradigm (this
provides an argument that the Stroop paradigm can be used to
understand how selective attention works
B. Middle section: (discussing prior work)
-Define the Stroop paradigm (1-2 paragraphs) -cite the original
paper
-describe the basic features of a Stroop experiment
-Describe the assumptions of the Horse-Race model (1-2
paragraphs)
-e.g., the model assumes color and word information have
different processing times -Mention that the purpose of the
experiment is to test predictions of the model.
C. Final section: (narrowing down to the aims of the
experiment)
-Explain that the specific aim of the experiment is to further
test the horse-race model of the
Stroop effect.
-Briefly describe the independent variables that will be
manipulated
-Congruency (congruent vs. incongruent
-Task (name color vs. name word)
-Does the model predict a main effect of Congruency?
-Does the model predict a main effect of Task?
-Does the model predict an interaction between Congruency &
Task?
Methods (1-2 pages)
The methods section should be a complete recipe that anyone
could follow to replicate your experiment. At the same time,

5. you should be as brief as possible.
-Participants
-how many people? where did they come from?
- Materials
-How many words, how many colors
-How were they combined to make congruent and incongruent
items
-How many congruent items -how many incongruent items
-Procedure
-What was the design, IVS, DVs, within or between? -how many
trials
-how were the stimuli for each trial chosen -Describe the
complete trial-sequence
-first the fixation cross appeared (for how long) -then the
Stroop stimulus appeared (for how long) -reaction times were
recorded
6. Results
The result section is used to report the patterns in the data, and
the statistical support for those patterns. Refer to the lab manual
for help on reporting statistics from a factorial design.
- Describe the statistical analysis
e.g., mean RTs from each condition were submitted to a
2(Task:name word vs. color) x 2 (congruency: congruent vs.
incongruent) within-subjects ANOVA.
-Tell the reader where they can see the data.
-e.g., the results of experiment 1 are presented in table 1, or in
figure 1
-you will have to make a table or figure to display the data in
your paper
-Describe the pattern of each main effect -The main effect of
Task was ...
-The main effect of Congruency was ...
-Describe the Congruency X Task interaction
-You will report the interaction the same way as a main effect.
7. Discussion
The discussion can be used to briefly restate verbally the

6. pattern of the most important results, and then to relate the
results to theory and ideas developed in the introduction
-highlight the main findings from the experiment
-remind the reader that the point of the paper is to evaluate
predictions from the model -discuss whether or not the model
accurately predicted the patterns of data.
8. References
-include citations used in the paper
9. figures or tables
METHODS OF EXPERIMENT CONDUCTED … GOES INTO
THE METHODS SECTION
Stroop Fact Sheet
Computer details
- The experiment was conducted on PCs running in-house
METACARD software.
- The screens were 15" LCD
Stimulus details
Number of words used - 4 (red, green, blue, yellow) Number of
colors used - 4 (red, green, blue yellow)

7. Each Stroop stimulus involved the presentation of one color and
one word. The word and color were presented simultaneously
Independent variables
Congruency
Number of possible congruent items - 4 Number of possible
incongruent items - 12 proportion of congruent and incongruent
trials - 50/50
Task
1 block of 48 trials: Name the word 1 block of 48 trials: Name
the color 96 total trials
The order of each block was randomly determined by the
computer for each subject. Half of the subject will do word
naming then color naming. The other half will do color naming
then word naming
Design
The experiment involved the factorial combination of 2
independent variables, each with 2 separate levels in a within-
subjects design. In other words, the design was 2 (Congruency:
congruent vs. incongruent) x 2 (Task: name word vs. color)
This means that all subjects experienced an equal number of
trials representing each of the independent variables. This is
how each trial breaks down.
Responses
There are four possible responses: red, green, blue, & yellow.
Responses are given by having subjects type the word into the
computer keyboard.
Details of a single trial
-Each trial begins with the presentation of a fixation cross in
the center of the screen -the fixation cross is visible for 500
milliseconds
-the fixation cross is removed, and immediately followed by the
word and color stimulus -The stroop stimulus remained on the
screen until a response was typed and the participant pressed
the spacebar
-Immediately after the response the stimuli were removed from
the screen -the next trial was triggered 500 milliseconds after

8. the last keypress
EXCEL FILE AND SPPS FILE GOES INTO RESULTS
SECTION … INTERPRET THESE NUMBERS INTO WORDS
… THE MEANS AND STANDARD DEVIATIONS
Name Color
Name Word

9. Congruent
Incongruent
Congruent
Incongruent

10. 1,1
1,2
2,1
2,2
1083
1503
847
979

11. 1285
1412
980
1090
1539
1648
971
970

12. name color
name word
1415
1682
1234
1155
congruent
1129.26
834.16
854
1200
629

13. 618
incongruent
1327.00
879.68
1142
1397
728
813
ERROR
988

14. 1275
882
933
name color
name word
978
1091
772
791
congruent
202.15
137.75

15. 1441
1487
855
968
incongruent
203.31
151.48
1024
1071
879
1119

16. 1008
1196
808
722
1364
1646
907
945

17. 985
1167
682
800
1093
1322
764
731

18. 987
1161
812
841
1078
1348
799
786

19. 1312
1443
857
933
1020
1074
816
876

20. 860
1090
627
644

21. 1129.26
1327.00
834.16
879.68
MEANS
202.15
203.31
137.75
151.48

22. STDEV

29. SPSS FILE

30. Descriptive Statistics
Mean
Std. Deviation
N
Cong_Color
1129.2632
202.15309
19
Incog_Color
1327.0000
203.30738
19
Cong_Word
834.1579
137.74948
19
Incong_Word
879.6842
151.47792
19
Multivariate Testsa
Effect
Value
F
Hypothesis df
Error df
Sig.
Partial Eta Squared
Color
Pillai's Trace
.876
127.106b

31. 1.000
18.000
.000
.876
Wilks' Lambda
.124
127.106b
1.000
18.000
.000
.876
Hotelling's Trace
7.061
127.106b
1.000
18.000
.000
.876
Roy's Largest Root
7.061
127.106b
1.000
18.000
.000
.876
Word
Pillai's Trace
.839
93.700b
1.000
18.000
.000
.839

32. Wilks' Lambda
.161
93.700b
1.000
18.000
.000
.839
Hotelling's Trace
5.206
93.700b
1.000
18.000
.000
.839
Roy's Largest Root
5.206
93.700b
1.000
18.000
.000
.839
Color * Word
Pillai's Trace
.527
20.091b
1.000
18.000
.000
.527
Wilks' Lambda
.473
20.091b

33. 1.000
18.000
.000
.527
Hotelling's Trace
1.116
20.091b
1.000
18.000
.000
.527
Roy's Largest Root
1.116
20.091b
1.000
18.000
.000
.527
a. Design: Intercept
Within Subjects Design: Color + Word + Color * Word
b. Exact statistic
Mauchly's Test of Sphericitya
Measure: RT
Within Subjects Effect
Mauchly's W

34. Approx. Chi-Square
df
Sig.
Epsilonb
Greenhouse-Geisser
Huynh-Feldt
Lower-bound
Color
1.000
.000
0
.
1.000
1.000
1.000
Word
1.000
.000
0
.
1.000
1.000
1.000
Color * Word
1.000
.000
0
.
1.000
1.000
1.000

35. Tests the null hypothesis that the error covariance matrix of the
orthonormalized transformed dependent variables is
proportional to an identity matrix.
a. Design: Intercept
Within Subjects Design: Color + Word + Color * Word
b. May be used to adjust the degrees of freedom for the
averaged tests of significance. Corrected tests are displayed in
the Tests of Within-Subjects Effects table.
Tests of Within-Subjects Effects
Measure: RT
Source
Type III Sum of Squares
df
Mean Square
F
Sig.
Partial Eta Squared
Color
Sphericity Assumed
2618147.842
1
2618147.842
127.106
.000
.876
Greenhouse-Geisser
2618147.842
1.000
2618147.842
127.106
.000
.876

36. Huynh-Feldt
2618147.842
1.000
2618147.842
127.106
.000
.876
Lower-bound
2618147.842
1.000
2618147.842
127.106
.000
.876
Error(Color)
Sphericity Assumed
370765.158
18
20598.064
Greenhouse-Geisser
370765.158
18.000
20598.064
Huynh-Feldt
370765.158
18.000
20598.064

37. Lower-bound
370765.158
18.000
20598.064
Word
Sphericity Assumed
281090.579
1
281090.579
93.700
.000
.839
Greenhouse-Geisser
281090.579
1.000
281090.579
93.700
.000
.839
Huynh-Feldt
281090.579
1.000
281090.579
93.700
.000
.839

38. Lower-bound
281090.579
1.000
281090.579
93.700
.000
.839
Error(Word)
Sphericity Assumed
53998.421
18
2999.912
Greenhouse-Geisser
53998.421
18.000
2999.912
Huynh-Feldt
53998.421
18.000
2999.912
Lower-bound
53998.421
18.000
2999.912

39. Color * Word
Sphericity Assumed
110048.211
1
110048.211
20.091
.000
.527
Greenhouse-Geisser
110048.211
1.000
110048.211
20.091
.000
.527
Huynh-Feldt
110048.211
1.000
110048.211
20.091
.000
.527
Lower-bound
110048.211
1.000
110048.211
20.091
.000
.527
Error(Color*Word)

40. Sphericity Assumed
98592.789
18
5477.377
Greenhouse-Geisser
98592.789
18.000
5477.377
Huynh-Feldt
98592.789
18.000
5477.377
Lower-bound
98592.789
18.000
5477.377
Tests of Within-Subjects Contrasts
Measure: RT

41. Source
Color
Word
Type III Sum of Squares
df
Mean Square
F
Sig.
Partial Eta Squared
Color
Linear
2618147.842
1
2618147.842
127.106
.000
.876
Error(Color)
Linear
370765.158
18
20598.064
Word
Linear
281090.579
1
281090.579
93.700
.000
.839

42. Error(Word)
Linear
53998.421
18
2999.912
Color * Word
Linear
Linear
110048.211
1
110048.211
20.091
.000
.527
Error(Color*Word)
Linear
Linear
98592.789
18
5477.377
Estimates
Measure: RT
Color
Mean

43. Std. Error
95% Confidence Interval
Lower Bound
Upper Bound
1
1228.132
44.963
1133.669
1322.595
2
856.921
31.962
789.770
924.072
Pairwise Comparisons
Measure: RT
(I) Color
(J) Color
Mean Difference (I-J)
Std. Error
Sig.b
95% Confidence Interval for Differenceb
Lower Bound
Upper Bound
1
2

44. 371.211*
32.926
.000
302.036
440.385
2
1
-371.211*
32.926
.000
-440.385
-302.036
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Least Significant
Difference (equivalent to no adjustments).
Multivariate Tests
Value
F
Hypothesis df
Error df
Sig.
Partial Eta Squared
Pillai's trace
.876
127.106a
1.000
18.000
.000
.876
Wilks' lambda
.124
127.106a

45. 1.000
18.000
.000
.876
Hotelling's trace
7.061
127.106a
1.000
18.000
.000
.876
Roy's largest root
7.061
127.106a
1.000
18.000
.000
.876
Each F tests the multivariate effect of Color. These tests are
based on the linearly independent pairwise comparisons among
the estimated marginal means.
a. Exact statistic
WORD
Estimates
Measure: RT
Word
Mean
Std. Error
95% Confidence Interval
Lower Bound
Upper Bound

46. 1
981.711
36.318
905.409
1058.012
2
1103.342
35.512
1028.734
1177.950
Pairwise Comparisons
Measure: RT
(I) Word
(J) Word
Mean Difference (I-J)
Std. Error
Sig.b
95% Confidence Interval for Differenceb
Lower Bound
Upper Bound
1
2
-121.632*
12.565
.000
-148.031
-95.233
2
1

47. 121.632*
12.565
.000
95.233
148.031
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Least Significant
Difference (equivalent to no adjustments).
Multivariate Tests
Value
F
Hypothesis df
Error df
Sig.
Partial Eta Squared
Pillai's trace
.839
93.700a
1.000
18.000
.000
.839
Wilks' lambda
.161
93.700a
1.000
18.000
.000
.839
Hotelling's trace
5.206
93.700a

48. 1.000
18.000
.000
.839
Roy's largest root
5.206
93.700a
1.000
18.000
.000
.839
Each F tests the multivariate effect of Word. These tests are
based on the linearly independent pairwise comparisons among
the estimated marginal means.
a. Exact statistic
3. Color * Word
Measure: RT
Color
Word
Mean
Std. Error
95% Confidence Interval
Lower Bound
Upper Bound
1
1
1129.263
46.377
1031.828
1226.698

49. 2
1327.000
46.642
1229.009
1424.991
2
1
834.158
31.602
767.765
900.551
2
879.684
34.751
806.674
952.694