Research 2 Final Exam Study Guide

Question 1

How is the two-way ANOVA different from the one-way ANOVA?

Answer 1

It has two independent variables. It has a 2 x 2 factorial design.

Question 2

Give your own everyday (non-research examples of an interaction

Answer 2

ice cream + fruit roll up = delicious crunchy treat

Question 3

a) In your own words, what is a synergistic effect? Give your own real life example. b) In your own words, what is a suppression effect? Give your own real life example.

Answer 3

a) when people work best as a team. Apollo 11 is one real life example. The people who made it up are just random guys but the three of them made it to the moon. They also had NASA behind them. b) when one person in a group is held back by the others. NASA is one real life example that held back Katherine Johnson. She had to fight her way to show her smarts and skills and was largely unrecognized for her efforts.

Question 4

a) What types of variables does the two-way factorial anova analyze?

Answer 4

2 IVs: nominal/categorical (2+ levels)
DV: continuous (interval/ratio)

Question 5

What research design does this the two-way factorial anova analyze?

Answer 5

between subjects - two way factorial

Question 6

Give 2 examples of a research question that fits this design. (one related to everyday life and one related to research, make sure both main effects and interaction are clear)

Answer 6

1. Do people lose weight when they diet and exercise? (diet = lose weight, exercise = lose weight, diet and exercise = lose weight) (diet vs normal meal) (15 minute walk vs 15 minute bike ride)
2. Is depression reduced when people spend time outside and take medication? (take medication = reduce depression, go outside, reduce depression, go outside and take medication = reduced depression) (30 minutes outside vs inside all day) (medication vs no medication (placebo)

Question 7

Pose your own 2 x 2 factorial design. a) set up 2 x 2 box to show it. b) include cell and marginal means. c) create a figure based on those means. d) describe the findings for all 3 potential effects to someone who doesn’t know statistics. (or I could give you data already and have you do similar things)

Answer 7

Does suburb versus city and access to food impact obesity?
1. Suburban areas will have lower obesity than city areas. 2. NO access to quality food will increase obesity than access to quality food. 3. No access to quality food and living in the city will have the highest obesity. Compared to city folk, those who live in the suburbs have lower obesity rates. Compared to those with access to food, those without access have increased obesity. Those living in the city and not having access to quality food have the highest obesity rates.

Question 8

a) A 2 x 2 factorial ANOVA combines which two statistical tests? b) A 3 x 2 factorial ANOVA combines which two statistical tests? d) What’s the benefit of combining them?

Answer 8

T-test for independent means x t-test for independent means; anova x test for independent means; one way ANOVA x one way ANOVA< doing lots of test increases chance something gets messed up, combining saves time.

Question 9

A thesis student runs a 3 x 4 x 3 x 2 study. a) How many IVs do they have? b) How many levels in the 3rd IV? c) Is this a good idea? why/why not?

Answer 9

a) 4 b) 3 c) No because there is a lot going on, a lot of variables and a lot of levels.

Question 10

a) In your own words, what does a two-way/factorial ANOVAA do/tell us? (i.e., What are the three F ratios?) b) Explain the logic of each F ratio (i.e., what does each compare?) c) When calculating them, what do they all have in common? d) Why is that?

Answer 10

a) compares two categorical variables on a dependent variable. b) the three f ratios are for each main effect (2 usually) and the interaction effect. c) one f ratio is the denominator, which is the within-groups estimate. It compares each within-cells mean. One f ratio is the two main effects’ between-groups estimates. S2Roaws/S2within, S2columns/S2within
d) denominator for WG- the typical differences based on cell means. How much everyone in all the cells differ.

Question 11

A study has the following independent variables: Stress (high, low), Anxiety (high, medium, low), and Optimism (high, low). Please list out the potential effects.

Answer 11

Main effects stress; anxiety; optimism
2-way interactions stress x anxiety; stress x optimism; anxiety x optimism
3-way interactions stress x anxiety x optimism
2 x 3 x 2 ANOVA

Question 12

Create a bar graph from the following data: (I’d then give you data)

Question 13

Review how to recognize and interpret effects from tables and from figures on the class PowerPoints (numerically & visually). (i’ll give you table/figure and ask you what effects there are)

Answer 13

Is there a difference in the marginal means?
Is there a different pattern in the cell means that when you go from left to right equal different numbers?

Question 14

a) If you have a main effect, what does that tell you about the interaction effect? b) If you have an interaction effect, what does that tell you about the main effect?

Answer 14

a) nothing b) nothing

Question 15

a) What does a median split accomplish? b) Why would you do this? c) What is the downside to doing this? d) What’s the better way to handle it?

Answer 15

a) turns continuous data into categorical data b) if you want to run a two-way ANOVA, your independent variables needs to be categorical, if it is not, then you would want to use a median split to change it. c) it isn’t as accurate; the middle is often not a good representation of either side because they can almost fall into either category, it only depended on where the median is. d) three way split, that way you use the low and high end and exclude those middle people who could’ve fallen either way.

Question 16

F (1, 116) = 6.81, p = .01, n^2 = .055 Which number(s) is/are the degrees of freedom?

Answer 16

1, 116

Question 17

F (1, 116) = 6.81, p = .01, n^2 = .055 Which number(s) is/are the significance level? Is it significant?

Answer 17

.01, yes

Question 18

F (1, 116) = 6.81, p = .01, n^2 = .055 Which number(s) is/are the effect size?

Answer 18

.055

Question 19

F (1, 116) = 6.81, p = .01, n^2 = .055 Which number(s) is/are the F score?

Answer 19

6.81

Question 20

F (1, 116) = 6.81, p = .01, n^2 = .055 Which number(s) is/are the p level? Is it significant?

Answer 20

.01, yes

Question 21

F (1, 116) = 6.81, p = .01, n^2 = .055 How many tails is the test?

Answer 21

two tails

Question 22

F (1, 116) = 6.81, p = .01, n^2 = .055 In APA style, all statistical symbols are formatted in?

Answer 22

Italics

Question 23

Test the effect of a story (having a story vs not) and picture (personal picture vs general picture) on donations to a GoFundMe page.

Answer 23

two-way ANOVA

Question 24

Does taking a nap improve your data entry abilities (measured by number of errors)?

Answer 24

t-test for dependent means

Question 25

Does the type of coffee you’re drinking (Starbucks, Rook Dunkin) influence your attractiveness? If so, which matters?

Answer 25

One-way ANOVA with post hocs

Question 26

Who is more well-rested, people who sleep on their stomachs or people who sleep on their back?

Answer 26

t-test for independent means

Question 27

Test the effect of water level (1/2 vs. 1/3 vs. 1/4) and hand (left vs. right) on number of flips.

Answer 27

two-way ANOVA

Question 28

Can what your drink (Water, Rook Coffee, Energy Drink) influence your happiness? I hypothesize that Rook is better than both the others.

Answer 28

One-way ANOVA with planned contrasts

Question 29

In addition to typical lab things (knowing what to run, when to run it, and how to describe results) also be able to look at output and describe the findings for tests.

Question 30

What does a repeated-measures ANOVA do? (what is the general logic?)

Answer 30

It measures the same person over and over and over (3) times) at three or more different times.

Question 31

What types of variables does this test analyze?

Answer 31

IV1: nominal/categorical 2+ levels (between subjects)
IV2: nominal/categorical 2+ levels (within subjects)
DV: continuous (interval/ratio)

Question 32

What research design does repeated-measures ANOVA analyze?

Answer 32

within and between subjects design

Question 33

Give 2 examples of a research question that fits this design. (one related to everyday life and one related to research)

Answer 33

1. Do acts of chivalry increase ego? (quiz, do something chivalrous, quiz again, do something chivalrous, quiz again)
2. Does driving practice increase confidence in new drivers? (quiz, practice, quiz, practice, quiz)

Question 34

In a one-way ANOVA, within-subject variance (i.e., the denominator of the F ratio comes from who’s scores? b) Where does it come from in the repeated measures ANOVA?

Answer 34

a) Within-subject, so the same person’s scores. You versus typical you. Typical difference of singular test subject.

Question 35

How do we use difference scores in repeated measures ANOVA? What are the differences between? Why is that important?

Answer 35

We use it to compare your ranking of a specific thing to how you rank related things overall. For example, if you were asked to rate candy, we’d want to know how much you rate Hershey’s out of all candy ratings you give. The difference score takes your ranking to the mean ranking. This is important because it would tell us that you really like Hershey’s if you rate it higher than you normally rate candy, or the opposite, or if you are indifferent. By doing it this way, you get answers with lower within-group variation, therefore you are more likely to find the treatment.

Question 36

When looking at participants’ scores, when one participant gives a rating of a “3”, does that mean the same thing when other participants give a “3”? Why or why not?

Answer 36

no because each person’s “3” could potentially be significant to indifferent based on their mean (typical) scoring.

Question 37

a) In a repeated measures ANOVA, is there more or less within-subject variation? b) why? c) what does this do to the F ratio? d) Does this make it easier or harder to get significance?

Answer 37

Less within-subject variation b) because it compare you from one point in time to you at another point in time and again…, typically, those times are quite close together, but because it is still you there isn’t too much change. If you do a longitudinal study than you could expect some more changes but again, still the same person so it is lower than if you had a random sample of individuals. c) This makes the F ratio larger (smaller denominator is bigger result. d) this makes it easier to get significance.

Question 38

a) How does the way we calculate F affect power? b) IN your own words, what is statistical power? (this is review from earlier in the semester)

Answer 38

a) The way we calculate F affects power by already (more likely) having significance, we don’t have to worry as much about a big sample size, or increasing the p-value or alpha level. b) statistical power: the likelihood of getting significance when the hypothesis is true.
There is no better comparison group for you then you, increase ability to find real results.

Question 39

a) In your own words, please describe an order effect. b) What can we do to avoid these in a study?

Answer 39

When we don’t know how to rank something, like cake, and so you eat the first one and that sets the tone when you try another one, and then you have something to compare it to. To avoid this, mix up the order they are presented in for each participant.

Question 40

How is the Mixed ANOVA different from the repeated measures ANOVA?

Answer 40

It is different because it uses a between subjects design. Has both between and within

Question 41

What types of variables does mixed ANOVA analyze?

Answer 41

IV: interval/ratio (3+ levels)
DV: continuous

Question 42

What research design does the Mixed ANOVA analyze?

Answer 42

between-subjects ANOVA

Question 43

Give 2 examples of a research question that fits this design.

Answer 43

1. What shoe brand (Nike, Puma, Adidas) and gender (Male vs female) are most comfortable?
2. What room color (red, white, green) and generational status (first versus continuing) have the most calming effect?

Question 44

Give your own example of a repeated measures design. Now add a between-subjects independent variable to make it a Mixed design.

Answer 44

a) How much does pumpkin spice scent make people think of the Fall season?
b) How much does pumpkin spice scent and gender make people think of the fall season.

Question 45

F (1, 287) = 66.07, p < .001, n^2 = .35 Which number(s) is/are the degrees of freedom?

Answer 45

1, 287

Question 46

F (1, 287) = 66.07, p < .001, n^2 = .35 Which number(s) is/are the significance level? Is it significant?

Answer 46

.001, yes

Question 47

F (1, 287) = 66.07, p < .001, n^2 = .35 Which number(s) is/are the effect size?

Answer 47

.35

Question 48

F (1, 287) = 66.07, p < .001, n^2 = .35 Which number(s) is/are the F score?

Answer 48

66.07

Question 49

F (1, 287) = 66.07, p < .001, n^2 = .35 Which number(s) is/are the p level? Is it significant?

Answer 49

.001, yes

Question 50

F (1, 287) = 66.07, p < .001, n^2 = .35 How many tails is the test?

Answer 50

two-tails

Question 51

F (1, 287) = 66.07, p < .001, n^2 = .35 In APA style, all statistical symbols are formatted in?

Answer 51

italics

Question 52

Students complete three different relaxation techniques (breathing, stretching, and coloring). After each one, they rate their test anxiety.

Answer 52

repeated measures ANOVA

Question 53

What results in more happiness, when groups of people exercise, sleep more, or eat better? Does eating better improve happiness more than both exercise and sleeping?

Answer 53

one way ANOVA with planned contrasts

Question 54

To find the best headphones for listening to house music, students tried 5 different types of headphones and rated the sound quality after each.

Answer 54

repeated measures ANOVA

Question 55

After the first headphone study (in the previous item), in addition to the headphone type (5 different types) researchers wanted to test music type and randomly assigned people to listen to house music, or classical music, then measured sound perception.

Answer 55

Mixed ANOVA

Question 56

Which type of music is better for relaxation, classical or smooth jazz?

Answer 56

t-test for independent means

Question 57

Do people really get tired after eating turkey?

Answer 57

t-test for dependent means