February 8th, 2011
When White Men Can’t Do Math: Necessary and Sufficient
Factors in Stereotype Threat (assignment 1)
1. Aronson, J., Lustina, M. J., Good, C., Keough, K., Steele, C. M., & Brown, J. (1999)
When White Men Can’t Do Math: Necessary and Sufficient Factors in Stereotype Threat. Journal of Experimental Social Psychology, 35, 29-40.
2. This research study was conducted to test the theory of stereotype threat, (which is a phenomenon that is characterized as situations that lead to impaired functioning when a person feels threatened by stereotypes within that situation), and the conditions assumed to be necessary for this phenomenon to impair the ability of the test subject to perform well on a standardized test.
3. The purpose of this current research study is to define the necessary variables required to evoke the phenomenon of stereotype threat in an individual that will impair their intellectual abilities. The results of this study will be used to further develop the theory of stereotype threat and further our understanding of this phenomenon’s components and workings which in turn will further contribute to the research literature on this topic and make available knowledge for future studies.
The problem that is under investigation is, if the prescribed assumption that for stereotype threat to be felt, one must be a cultural minority with a long standing notion of being inferior, Or if situational pressure is also a factor in creating the effects and outcomes of stereotype threat in an individual.
4. The main hypothesis in this research experiment was that participants would have impaired performance when exposed to the stereotype regarding Asian superiority in math. The null hypothesis would be that perceived Asian superiority in math will have no influence on the subjects’ performance.
Another hypothesis of this research study was that stereotype threat didn’t require a preexisting sense of inferiority but could be due to situational factors alone. The null hypothesis would be that situational pressures alone had no influence on the effects of stereotype threat.
A third hypothesis was, to be affected adversely by an implemented stereotype claiming low ability; the person must have a perceived confidence in the ability being tested. The null hypothesis would be that regardless of the person’s perception of their abilities, the implemented stereotype claiming low ability will have no influence on the person’s performance.
5. My alternative hypothesis would be that participants from low socioeconomic backgrounds would perform less positively when taking a test that assesses intellectual ability than would participants from high socioeconomic backgrounds. My null hypothesis would be that the socioeconomic background of the participant would have no influence on the outcome of taking a test that assesses intellectual ability.
Darius Muniz & Elliot Levie Research Methods I
Professor Godfrey February 21st, 2011
1. The construct of fear will be conceptually defined as an emotional or reactive response to a perceived threatening or harmful situation. The construct of affection will be conceptually defined as the expression of care, fondness, and tender attachment towards another person.
2. Fear will be operationally defined by observing specific behaviors that correspond with an individual reacting to a stressful situation. Fear responses that occur will be identified and checked off during the AMC series, pilot episode of “The Walking Dead” while viewing specific time stamped scenes in which the characters displayed behaviors that corresponded with fear responses defined by my fear checklist. Affection will be operationally defined by observing acts or verbalizations that indicate a loving connection between two individuals such as kissing, hugging, or talking lovingly to one another. Displays of Affection will be identified while watching episode 2 of the TV show “Modern Family” and specific behaviors that are defined by the affection rating scale will be counted and tallied for frequency of occurrence.
4. The T.V. series The Walking Dead has been chosen to demonstrate the construct of fear due to the violent nature of the show and the fear induced responses and survival strategies that would be evoked by constant states of fear. This show is set around an apocalyptic world in which the dead are reanimated and are constantly hunting the living as a source of food. It is apparent that in such a mode of existence where the reality is one of constant fear of death and trying to survive by any means possible, fear would be accurately displayed in overt emotional and physical responses in reaction to threatening and harmful situations throughout the show. The television show Modern Family was chosen to demonstrate the construct of affection due to the family oriented and loving nature of the show. The show is centered on three families that are interrelated through the patriarch of the family Jay Pritchett and his children Claire Dunphy and Mitchell Pritchett and their respective family members. With each of the families’ members interacting with each other there are many instances of them interacting in affectionate ways.
5. Upon completion of utilizing both the fear response checklist and affection ratings scales on our television shows of choice, the data was then recorded and analyzed to determine the reliability of our devised fear and affection measures. The fear response checklist turned out a 1.0 agreement level after the data was compared which deemed the measure efficiently reliable for use. The affection rating scale turned out a 71 percent agreement level after the data was compared. Out of 7 representative behaviors, one was disagreed upon which was a person backed away from a hug or kiss behavior. The behavior did not accurately represent affection but more accurately represented its opposite rejection. Also, one level of frequency was found to differ, which was a character playfully touched another in which it was rated as both high and moderate. For the fear response checklist 9 of the representative behaviors were checked as occurring and 1 was checked as not occurring. Though all items were identically marked by both raters, giving it a 1.0 inter-rater reliability, the fact that a person flinched was not marked as occurring sheds doubt on its validity of representing fear. For the affection ratings scale, item 7 would be omitted on a revised scale due to its lack of validity on measuring affection. For the discrepancy of separate ratings for item 6 in frequency, this occurrence could be due to the specificity of the behaviors outlined by the operational definition of affection in which playfully touching was not specified. We would perhaps revise the operational definition of affection to include specifically the behavior of playfully touching in order to eliminate measurement error due to the lack of clarity and uncertainty on the part of the raters.
Factors Influencing the Amount of Time Students Spend at Upstein Cafeteria
Darius Muniz, Seren Karasu, Christopher Tolo, Rebecca Stein,
Gavriel Franco & Elliot Levie
New York University
Location, Outcome Variable, Predictor Variables, and Hypotheses:
We have chosen “Upstein” at the Weinstein Food Court as our setting for observing student consumer behavior. We have defined our outcome variable as The time a student spends at Upstein and have operationalized it according to our Duration Rating Scale (DRS) which consisted of three levels; Low (students stayed for 5 minutes or less), Medium (students stayed for 6 to 15 minutes), and High (students stayed for 16+ minutes). We next devised 6 Predictor variables (PV’s) that we felt would generate enough variability to record observable differences in students’ consumer behavior. Christopher Tolo’s PV was Gender and was operationalized as; male or female. His hypothesis was that males spend a longer amount of time at “Upstein” than females. Rebecca Stein’s PV was Group Size and was operationalized as; 1 individual or 2 or more individuals which would characterize a group. Her hypothesis was that students in groups will stay for a longer period of time at “Upstein” than an individual. Seren Karasu’s PV was Additional items brought to Upstein and was operationalized as; items such as backpacks, messenger bags, laptops, books or notebooks. Her hypothesis was if an individual brings additional items with them then they are more likely to spend extra time in “Upstein” after buying something. Gavriel Franco’s PV was Day of the Week and was operationalized as; a weekday (Thursday) or a weekend (Saturday). Her hypothesis was students are more likely to spend more time at “Upstein” on a weekend then they would on a weekday. Elliot Levie’s PV was Type of food a person ordered and was operationalized as; the type of vendor a student bought from either “Chick Fil-A”, “Quiznos”, “Over and Easy”, or “To Go meals”. His hypothesis was students who order “To Go Meals” will spend a less amount of time at “Upstein” than students who order from Chick Fil-A, Quiznos, or Over and Easy. Darius Muniz’s PV was Time of Day and was operationalized as; 12:00 pm to 12:30 pm (Day Hours) or 8:00 pm to 8:30 pm (Night Hours). His hypothesis was Students are more likely to stay longer at “Upstein” during night hours than they are during day hours.
Results and Findings:
In Christopher Tolo’s study 67 students were observed entering the “Upstein” cafeteria with a total of 30 males and 37 females. 50% of males stayed for 16+ minutes whereas 41% of females stayed for 16+ minutes. An equal amount of males and females per capita were observed to stay 16+ minutes, however, a higher percentage of observed males that stayed 16+ minutes in proportion to total observed males were higher than observed females who stayed 16+ minutes in proportion to their total which supported his hypothesis. In Rebecca Stein’s study 18 students were observed entering the “Upstein” cafeteria out of which 10 students ate in groups, and stayed for the highest amount of time (16+ minutes). Out of the 8 people who ate individually, 5 got takeout and stayed for 5 minutes or less, while 3 sat in the cafeteria for a range of ten minutes. Students who ate in groups stayed for longer periods of time as opposed to students who ate individually who tended to take their food and leave which supported her hypothesis. In Seren Karasu’s study a total of 27 students were observed entering the cafeteria. For the 6-15 minute time slot there were 12 students with extra items and 10 without. The 16+ minute time slot consisted of 11 students who had extra items and 8 that did not. Students who stayed the maximum amount of time had extra items with them which supported her hypothesis. In Gavriel Franco’s study 55 students were observed entering “Upstein” on Thursday and 58 students were observed entering on Saturday. For Thursday, 12 students stayed a Medium amount of time and 16 students stayed a High amount of time. On Saturday, 16 students stayed a medium amount of time, and 22 stayed a High amount of time. According to the high rating, more students stayed for 16+ minutes on Saturday than they did for Thursday with 22 students as opposed to 16 students. According to these results her hypothesis was supported. In Elliot Levie’s study 57 students were observed entering “Upsteins” and for students who stayed 16+ minutes; Chick Fil-A had 6, Quiznos had 8, and Over and Easy had 6 with 1who ordered a To Go Meal. For students who stayed 6 to 15 minutes; Chick Fil-A had 3, Quiznos had 5, and Over and Easy had 5 with 1 student who ordered a To Go Meal. For students who stayed for 5 or less minutes; Chick Fil-A had 5, Quiznos had 6, and Over and Easy had 5 with 7 students who ordered a To Go Meal. According to the results his hypothesis was supported by all levels of the outcome variable. In Darius Muniz’s study 21 students walked in during (Day Hours). 12 students stayed for 16+ minutes and 4 students were observed staying for 6 or more minutes. 27 students walked in during (Night Hours) with 15 students staying for 16+ minutes and 3 students staying for 6 or more minutes. According to the data, the number of students who stayed for 16+ minutes was greater for night hours then they were for day hours at 15 as opposed to 12. His hypothesis was supported by the data because more students stayed for 16+ minutes during night hours then they did during day hours.
Third Variables, New Variables, and Alternate Hypotheses:
For Christopher Tolo, a third variable that may have affected the predictor variable as it relates to the outcome variable is the design of “Upstein”. It is possible that the presentation of the Upstein cafeteria appeals more to males than it does to females. For a new variable he would explore having backpacks, bags and other carry-ons vs. not having them as a predictor variable for how long someone stays in “Upstein”. He would hypothesize that people who have belongings with them coming into “Upstein” are less likely to stay longer than those who don't have belongings with them. For Rebecca Stein, a third variable could have been a lack of seating in the cafeteria. A new predictor variable could be self confidence, let's say there's a table where a group of 4 girls are sitting having lunch, and a student walked in alone and saw that their table had 2 empty seats. How comfortable and confident would that student be to sit at that table or would they walk out because they’re shy. An alternate hypothesis would be those who see available seats at an already taken table will more likely walk out of the cafeteria as opposed to joining a table where a random group who they didn’t know is already eating. For Seren Karasu, One extraneous variable could have been the day of the week. The study was done on a Sunday afternoon whereas more individuals would be likely to have additional items with them if it were a school day. A new predictor variable would be a day a student has classes as opposed to a day with no classes and the hypothesis would be if an individual comes to “Upstein” on a day with classes, then they are less likely to spend extra time at “Upstein” after buying something. For Gavriel Franco, One confounding variable is that most students take classes during the week and therefore cannot spend as much time in “Upstein” because they need to get to class. A new predictor variable would be time of day as it relates to time spent in “Upstein” and she hypothesized that those who come to “Upstein” during the day will stay longer than those who come to “Upstein” during the evening. For Elliot Levie, a possible third variable could have been the day of the week that the observations were taken. A possible new variable could be weather and he hypothesized that students would stay longer in “Upsteins” during bad weather then they would on a nice day. For Darius Muniz, A possible third variable could be that the study was conducted for one day under a limited amount of time. If data was collected on more than one occasion then the results obtained could either support or disconfirm the results proving if they were constant over time. A possible new variable would be a student’s class schedule and the hypothesis would be Students with a busy class schedule will spend less time at “Upstein” as opposed to students with no classes scheduled for the day.
Research Methods in Applied Psychology I
Lab Assignment 4
Due in class on 4/14/2011
Answer the following five questions for each of the experiments described below. The page limit for this assignment is 3-4 pages.
1. Identify the independent variable(s).
2. Identify the dependent variable(s).
3. Identify whether there are any confounding variable(s) and what they are.
4. Identify two possible sources of error variance.
4. Propose a method to "unconfound" the experiment (if necessary).
Confound selection 1:
Tom Rogers wanted to test a new "singalong" method to teach math to fourth graders (e.g., "I love to multiply" to the tune of "God Bless America"). He used the singalong method in his first period class. His sixth period students continued solving math problems with the old method. At the end of the term, Mr. Rogers found that the first period class scored significantly lower than the sixth period class on a mathematics achievement test. He concluded that the singalong method was a total failure.
Confound selection 2:
An airport administrator investigated the attention spans of air traffic controllers to determine how many incoming flights the average controller can coordinate at the same time. Each randomly selected controller was tested, without his or her knowledge, by a computer program that fed false flight information to a computer terminal. The controller first "received" information from one plane, and then two planes, and so on. By the end of an hour the controller was coordinating 10 planes simultaneously. The administrator analyzed the errors collected by the computer program. The analysis revealed that the maximum number of planes a controller could handle without making potentially fatal errors was six planes. Also, no errors occurred when only one to three planes were incoming. He concluded that a controller should never coordinate more than six incoming flights.
Confound selection 3:
A drug company developed a new medication to control the manic phase of bipolar manic-depression. The firm hired a hospital psychiatrist to test the effectiveness of the drug. He identified a group of manic-depressive patients and randomly assigned them to a drug or placebo group. Nurse Ratched was told to administer the drug and Nurse Johnson was told to administer the placebo. Each nurse made daily observations of her patients during treatment. A month later the observations were compared. In general, patients in the drug group had behaved more "normally" than patients in the placebo group. The drug company publicized its product's effectiveness.
Confound selection 4:
Dr. Goodrich wanted to demonstrate that his tires were better than those of his competitor, Dr. Goodyear. From car registration and leasing records, he found 40 salespeople who drove the same model of automobile approximately the same number of miles per week. Anonymously, Dr. Goodrich hired an independent research assistant, who was unaware of the purpose of the study, to randomly assign to 20 of the salespeople a new set of unmarked Goodrich tires, and to the other 20 a new set of unmarked Goodyear tires of the same price and quality. After six months and an average of 15,000 miles traveled by both groups, the assistant arranged for the salespeople to exchange tires. After another six months, and similar mileage, the assistant measured the amount of tread wear and reported that the Goodrich tires had actually worn more than the Goodyear tires.
Confound selection 5:
An investigator was interested in studying the effect of taking a course in child development upon attitudes toward childrearing. At the end of the semester, the researcher distributed a questionnaire to students who had taken the child development course. Questionnaires were also given to an equal number of students who had not taken the course. The students who had taken the child development course had different attitudes from the students who had not taken the course (e.g., they had more positive attitudes about having large families).
Research Methods in Applied Psychology I
Lab Assignment 5
Due in class on 4/28/2011
A researcher is interested in seeing whether gender and shoe type cause change in basketball performance as measured by jump height. Read the SPSS output below which is a two-way (2x3) ANOVA. The dependent variable is jump height in inches and the two independent variables are gender (male vs. female) and shoe type (barefoot vs. Air Jordon vs. Nike running). This should be no more than 2 pages.
Interpret the output below in your own words. Make sure you interpret the main effects, the interaction and the post-hoc comparisons if needed. For each main effect and the interaction, interpret what is the meaning of the Sum of Squares between (Systematic), the Sum of Squares within (error), and the F-ratio (systematic average variance divided by average error). Finally, describe what the results are telling the researcher about whether gender and shoe type cause change in basketball performance (keep the post hoc tests in mind).
2.00 Nike running
Tests of Between-Subjects Effects
Type III Sum of Squares
Shoetype * Gender
a. R Squared = .753 (Adjusted R Squared = .731)
ESTIMATED MARGINAL MEANS
95% Confidence Interval
2.00 Nike running
95% Confidence Interval
Mean Difference (I-J)
95% Confidence Interval
2.00 Nike running
2.00 Nike running
2.00 Nike running
Based on observed means.
The error term is Mean Square(Error) = 2.237.
*. The mean difference is significant at the 0.05 level.