1. Worksheet – Redux

2. Introduction to Statistics for the Social Sciences SBS200 - Lecture Section 001, Spring 2019 Room 150 Harvill Building 9:00 - 9:50 Mondays, Wednesdays & Fridays.
February 6

3. Even if you have not yet registered your clicker you can still participate
The
Green
Sheets

4. Schedule of readings Study Guide posted Before next exam (February 8)
Please read chapters in OpenStax textbook
Please read Appendix D, E & F online On syllabus this is referred to as online readings 1, 2 & 3
Please read Chapters 1, 5, 6 and 13 in Plous
Chapter 1: Selective Perception
Chapter 5: Plasticity
Chapter 6: Effects of Question Wording and Framing
Chapter 13: Anchoring and Adjustment

6. Labs continue this week
Lab sessions
Everyone will want to be enrolled
in one of the lab sessions
Labs continue this week

8. Exam 1 Review Seating Chart – Arrive a little early
University ID number – 8-digit (on UAccess)
Class ID – 4-digit (on all of your assignments)
Distributed from left side
Extras go on bottom of pile
Finish by 9:45
Need
ID Card
Two pencils with good erasers
Two simple calculators (four function + square root)
Tissues

9. Summary of 7 facts to memorize
Review

10. Review z score = raw score - mean standard deviation Mean = 100
If we go up one standard deviation
z score = +1.0 and raw score = 105
z = -1
z = +1
68%
If we go down one standard deviation
z score = -1.0 and raw score = 95
If we go up two standard deviations
z score = +2.0 and raw score = 110
z = -2
95%
z = +2
If we go down two standard deviations
z score = -2.0 and raw score = 90
If we go up three standard deviations
z score = +3.0 and raw score = 115
99.7%
z = -3
z = +3
If we go down three standard deviations
z score = -3.0 and raw score = 85
z score: A score that indicates how many standard deviations
an observation is above or below the mean of the distribution
z score = raw score - mean
standard deviation
Review

12. Worksheet – Redux

13. Writing Assignment – Pop Quiz Distance from the mean X Taller 2 inches
Preston is 2” taller than the mean (taller than most)
Taller
2 inches
Shorter X
Mike is 4” shorter than the mean (shorter than most)
Shorter
4 inches
Taller X
Equal to mean
Diallo is exactly same height as mean (half taller half shorter)
0 inches
Half are Shorter

14. Writing Assignment – Pop Quiz Sigma – standard deviation - population
Parameter
mu – a mean – an average - population
Parameter
x-bar – a mean – an average - sample
statistic
s – standard deviation - sample
statistic
The number of “standard deviations” the score is from the mean
population
Sigma squared and s squared - variance
Sigma is parameter (population) s is statistic (sample)
Deviation scores
(x-µ) for population (parameter) (x-x) is statistic (sample)
Sum of squares
On left is statistic on right is parameter
Standard deviation
s is statistic sigma is parameter
Degrees of freedom
sample

15. Do not hand it in until it:
Is correct, complete and stapled
If it is not complete and correct, consider this an “extension” and hand it in next time
If it is complete and
correct, hand it
in now

16. Exam 1 Review

17. Let’s try one
Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. Which of the following is true?
a. The IV is gender while the DV is time to finish a race
b. The IV is time to finish a race while the DV is gender

18. Let’s try one
Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. The independent variable is a(n) _____
a. Nominal level of measurement b. Ordinal level of measurement c. Interval level of measurement d. Ratio level of measurement

19. Let’s try one
Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. The dependent variable is a(n) _____
a. Nominal level of measurement b. Ordinal level of measurement c. Interval level of measurement d. Ratio level of measurement

20. Let’s try one
Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. The independent variable is a(n) _____
a. Discrete b. Continuous

21. Let’s try one
Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. The dependent variable is a(n) _____
a. Discrete b. Continuous

22. Let’s try one
Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. Which of the following is true?
a. This is a quasi, between participant design b. This is a quasi, within participant design c. This is a true, between participant design d. This is a true, within participant design

23. Let’s try one
Albert found that the standard deviation for the weight of the jockeys is 10 pounds, what is the variance?
a. 10 pounds b pounds c. 1,000 pounds d. 10,000 pounds

24. Let’s try one
Albert wanted to know if female jockeys have gotten better over the last 60 years, so he plotted the winning percentages for each year and looked for a trend over time. This is called
a. Cross sectional design b. Time sectional design c. Cross series design d. Time series design

25. Let’s try one
Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. This is called
a. Cross sectional design b. Time sectional design c. Cross series design d. Time series design

26. Let’s try one
Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. This is a _____
a. Between participant design b. Within participant design

27. Let’s try one
Albert wants to know the probability of a particular horse winning the race. Albert measured the weights of the jockeys and found the following distribution. What percent of the jockeys will fall between 95 and 105 pounds (within one standard deviation of the mean)
a. 68% b. 95% c % d. Unknown

28. Let’s try one
Albert wants to actually calculate the standard deviation for the weight of the jockeys. So he found the deviation scores and added them up. What did he get? Σ(x - x) equals
a. 68% b. 95% c % d. 0

29. Let’s try one
Albert wants to know whether weight makes a difference. He created a scatterplot looking at weight and winning percentage and found that smaller jockeys won more often. Describe this relationship
a. Strong positive b. Strong negative c. Weak positive d. Weak negative

30. Let’s try one
Albert wants to know whether weight makes a difference. He plotted their weight in a frequency curve and found this:
a. Positively skewed b. Negatively skewed c. Weakly skewed d. Strongly skewed

31. Let’s try one
Albert wants to know whether weight makes a difference. He plotted their weight in a frequency curve and found this:
a. The mean was bigger than the mode b. The mean was smaller than the mode c. The mean equaled the mode d. Not enough information

32. Let’s try one
Albert wants to know whether weight makes a difference. He measured 5 jockeys and found this: 90, 95, 100, 105 and 110
a. The mean 100 b. The median is 100 c. The mode is 100 d. Both A and B are true

33. a. alphabetically from left to right
Let’s try one
Albert wants to know whether weight makes a difference. He plotted the weight of each jockey using a Pareto Chart. How did he arrange the data?
a. alphabetically from left to right
b. in descending frequency from left to right with most
frequently occurring category first
c. in increasing cumulative frequency from left to right
d. in any order because categorical data can appear in any order b.

34. Let’s try one
Judy is running an experiment in which she wants to see whether a reward program will improve the number of sales in her retail shops. (As described in previous question). She wants to use her findings with these two samples to make generalizations about the population, specifically whether rewarding employees will affect sales to all of her stores. She wants to generalize from her samples to a population, this is called
a. random assignment
b. stratified sampling
c. random sampling
d. inferential statistics

35. Let’s try one
Judy is running an experiment in which she wants to see whether a reward program will improve the number of sales in her retail shops. In her experiment she rewarded the employees in her Los Angeles stores with bonuses and fun prizes whenever they sold more than 5 items to any one customer. However, the employees in Houston were treated like they always have been treated and were not given any rewards for those 2 months. Judy then compared the number of items sold by each employee in the Los Angeles (rewarded) versus Houston (not rewarded) stores. In this study, a _____________ design was used.
a. between-participant, true experimental
b. between-participant, quasi experimental
c. within-participant, true experimental
d. within-participant, quasi experimental

36. Let’s try one
Judy is running an experiment in which she wants to see whether a reward program will improve the number of sales in her retail shops. Judy wants to evaluate the performance of individual sales team members. So she finds how much better (or worse) each sales person performed relative to the mean of their group. This difference between the individual’s performance and the mean for her group (x – μ). This value is call a _____________.
a. mean score
b. deviation score
c. standard deviation score
d. variance score

37. Let’s try one
Judy is running an experiment in which she wants to see whether a reward program will improve the number of sales in her retail shops. Judy wants to evaluate the performance of individual sales team members. So she finds how much better (or worse) each sales person performed relative to the mean of their group. She then added up all of these scores Σ(x – μ). This value is ___.
a. the mean score
b. the deviation score
c. equal to zero
d. variance score

38. Let’s try one
Judy is running an experiment in which she wants to see whether a reward program will improve the number of sales in her retail shops. Judy wants to evaluate the performance of individual sales team members. So she found the deviation scores. But then squared each deviation score and added up the squared deviation scores. Σ (x – μ)2. This value is call a _____________.
a. mean score
b. deviation score
c. standard deviation score
d. sum of squares score

39. Let’s try one
Judy is running an experiment in which she wants to see whether a reward program will improve the number of sales in her retail shops. Judy wants to know (on average) how much all of the team member differ from the mean. So she found the deviation scores. But then squared each deviation score and added up the squared deviation scores and divided that whole thing by the number of scores (n). Σ (x – μ)2. N
Notice she did not yet take the square root. This value is call a _____________.
a. mean score
b. deviation score
c. standard deviation score
d. variance

40. Let’s try one
Naomi is interested in surveying mothers of newborn infants, so she uses the following sampling technique. She found a new mom and asked her to identify other mothers of infants as potential research participants. Then asked those women to identify other potential participants, and continued this process until she found a suitable sample. What is this sampling technique called?
a. Snowball sampling
b. Systematic sampling
c. Convenience sampling
d. Judgment sampling

41. Let’s try one
Steve who teaches in the Economics Department wants to use a simple random sample of students to measure average income. Which technique would work best to create a simple random sample?
a. Choosing volunteers from her introductory economics class
to participate
b. Listing the individuals by major and choosing a proportion
from within each major at random
c. Numbering all the students at the university and then using a random number table pick cases from the sampling frame.
d. Randomly selecting different universities, and then sampling everyone within the school.

42. Let’s try one
Marcella wanted to know about the educational background of
the employees of the University of Arizona. She was able to
get a list of all of the employees, and then she asked every employee how far they got in school. Which of the following
best describes this situation?
a. census
b. stratified sample
c. systematic sample
d. quasi-experimental study

43. Let’s try one
Mr. Chu who runs a national company, wants to know how his Information Technology (IT) employees from the West Coast compare to his IT employees on the East Coast. He asks each office to report the average number of sick days each employee used in the previous 6 months, and then compared the number of sick days reported for the West Coast and East Coast employees. His methodology would best be described as:
a. time-series comparison
b. cross-sectional comparison
c. true experimental comparison d. both a and b

44. Let’s try one
When several items on a questionnaire are rated on a five point scale, and then the responses to all of the questions are added up for a total score (like in a miniquiz), it is called a:
a. Checklist
b. Likert scale
c. Open-ended scale
d. Ranking

45. Let’s try one
Which of the following is a measurement of a construct (and not just the construct itself)
a. sadness
b. customer satisfaction
c. laughing
d. love

46. How many levels of the IV are there?
What if we were looking to see if our new management
program provides different results in employee happiness
than the old program. What is the independent variable?
a. The employees’ happiness
b. Whether the new program works better
c. The type of management program (new vs old)
d. Comparing the null and alternative hypothesis

47. What if we were looking to see if our new management
program provides different results in employee happiness
than the old program. What is the dependent variable?
a. The employees’ happiness
b. Whether the new program works better
c. The type of management program (new vs old)
d. Comparing the null and alternative hypothesis

48. Marietta is a manager of a movie theater. She wanted to know whether
there is a difference in concession sales for afternoon (matinee) movies
vs. evening movies. She took a random sample of 25 purchases from
the matinee movie (mean of $7.50) and 25 purchases from the evening
show (mean of $10.50). She compared these two means. This is an
example of a _____.
a. between participant design
b. within participant design
c. mixed participant design

49. Marietta is a manager of a movie theater. She wanted to know whether
there is a difference in concession sales for afternoon (matinee) movies
vs. evening movies. She took a random sample of 25 purchases from
the matinee movie (mean of $7.50) and 25 purchases from the evening
show (mean of $10.50). She compared these two means. This is an
example of a _____.
a. quasi experimental design
b. true experimental design
c. mixed participant design
quasi

50. Victoria was also interested in the effect of vacation time on productivity
of the workers in her department. In her department some workers took
vacations and some did not. She measured the productivity of those
workers who did not take vacations and the productivity of those workers
who did (after they returned from their vacations).
This is an example of a _____.
a. quasi-experiment
b. true experiment
c. correlational study
quasi
Let’s try one

51. Ian was interested in the effect of incentives for girl scouts on the number
of cookies sold. He randomly assigned girl scouts into one of three groups.
The three groups were given one of three incentives and he looked to see
who sold more cookies. The 3 incentives were: 1) Trip to Hawaii, 2) New
Bike or 3) Nothing. This is an example of a ___.
a. quasi-experiment
b. true experiment
c. correlational study
true
Let’s try one

52. Thank you!
See you next time!!