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Research 1 > Research 2 Final Exam Study Guide > Flashcards

Research 2 Final Exam Study Guide Flashcards

1
Q

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

A

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

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2
Q

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

A

ice cream + fruit roll up = delicious crunchy treat

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3
Q

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.

A

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.

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4
Q

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

A

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

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5
Q

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

A

between subjects - two way factorial

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6
Q

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

A
  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)
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7
Q

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)

A

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.

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8
Q

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?

A

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.

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9
Q

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?

A

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

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10
Q

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?

A

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.

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11
Q

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

A

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

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12
Q

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

A
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13
Q

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)

A

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?

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14
Q

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?

A

a) nothing b) nothing

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15
Q

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?

A

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.

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16
Q

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

A

1, 116

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17
Q

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

A

.01, yes

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18
Q

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

A

.055

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19
Q

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

A

6.81

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20
Q

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

A

.01, yes

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21
Q

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

A

two tails

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22
Q

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

A

Italics

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23
Q

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

A

two-way ANOVA

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24
Q

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

A

t-test for dependent means

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25
Q

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

A

One-way ANOVA with post hocs

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26
Q

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

A

t-test for independent means

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27
Q

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

A

two-way ANOVA

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28
Q

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

A

One-way ANOVA with planned contrasts

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29
Q

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.

A
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30
Q

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

A

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

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31
Q

What types of variables does this test repeated measures analyze?

A

IV: nominal/categorical 2+ levels (Within subjects)

DV: continuous (interval/ratio)

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32
Q

What research design does repeated-measures ANOVA analyze?

A

within subjects design

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33
Q

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

A
  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)
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34
Q

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?

A

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

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35
Q

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

A

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.

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36
Q

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?

A

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

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37
Q

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?

A

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.

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38
Q

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)

A

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.

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39
Q

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

A

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.

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40
Q

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

A

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

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41
Q

What types of variables does mixed ANOVA analyze?

A

IV1: (within) nominal
Iv 2 (between) categorical
(3+ levels)
DV: continuous

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42
Q

What research design does the Mixed ANOVA analyze?

A

Within and between-subjects ANOVA

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43
Q

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

A
  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?
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44
Q

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

A

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.

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45
Q

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

A

1, 287

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46
Q

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

A

.001, yes

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47
Q

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

A

.35

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48
Q

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

A

66.07

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49
Q

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

A

.001, yes

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50
Q

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

A

two-tails

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51
Q

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

A

italics

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52
Q

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

A

repeated measures ANOVA

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53
Q

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?

A

one way ANOVA with planned contrasts

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54
Q

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

A

repeated measures ANOVA

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55
Q

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.

A

Mixed ANOVA

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56
Q

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

A

t-test for independent means

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57
Q

Do people really get tired after eating turkey?

A

t-test for dependent means

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58
Q

In your own words, what does correlation do/tell us?

A

a correlation attempts to show a relationship between two things

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59
Q

Give your own example of an everyday life situation that is a) positively corelated b) negatively correlated c) has no correlation

A

a) the lower the amount of light the less you are able to see b) the more you procrastinate, the lower quality of work c) chair presence and mice presence

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60
Q

Draw a scatter diagram for each of your example life situations from the previous item b) Draw a scatter diagram for a curvilinear relationship

A
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61
Q

a) In your own words, what is a spurious correlation? b) Are these significant? c) Why do people think there are real? d) Where do they actually come from?

A

a) Things that seem correlated but are not, people think there is a relationship but not related b) A spurious correlation could be significant, correlated but there is no causal relationship between the variables. Not meaningful c)Superstitions or sometimes things seem correlated but really there is a third variable (summertime more people outside related to more people committing more crimes) d) they come from chance and running a lot of data

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62
Q

a) Does correlation equal causation? b) Does correlation imply causation? c) why or why not?

A

a) Correlation does NOT equal causation b) correlation does imply causation c) because to have causation, you first need a correlation (all causation has a correlation first)

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63
Q

If I give you a correlation (e.g., books and school performance) be able to interpret it. What does it tell us? What does it not tell us?

A

Strength and relationship of variable (direction, weak versus strength). It tells us there is a significant relationship, but not if it is meaningful, does not tell us causation. i.e., If reading more books causes better school performance, other way around etc.

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64
Q

What types of variables does this test correlation analyze?

A

2 continuous variables (no true IV or DV)

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65
Q

What research design does this test correlation analyze?

A

Correlational design non-experiment

66
Q

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

A
  1. Are there more attendants when food is offered?
  2. Do people feel happier when they take more statistics classes?
67
Q

r (145) = .27, p = .14 Which number(s) is the Pearson’s r?

A

.27

68
Q

r (145) = .27, p = .14 Which is the 145 number?

A

Degrees of freedom

69
Q

r (145) = .27, p = .14 Which number(s) is/are the significance level? Is it significant?

A

.14, no

70
Q

r (145) = .27, p = .14 Which number(s) is/are the correlation?

A

.27

71
Q

Which number(s) is/are the p level? Is it significant?

A

.14, no

72
Q

How many tails is the test?

A

two tailed

73
Q

In APA style, all statistical symbols are formatted in?

A

Italics

74
Q

You get a correlation between cups of coffee drank and alertness or r=.87. What can you say

A

there is a strong positive relationship between cups of coffee drank and alertness

75
Q

You read an article online that reports a Pearson r of -.39, but provides no other information. What does that tell you? (be careful answering this) What else would you like to know?

A

All we know is that the correlation is -.39 but we don’t know if it is significant. I would want to know the p value, sample size, and effect size.

76
Q

A newspaper reported discovers that crime rates and homelessness are associated. She the reporter assume one causes the other? why/why not?

A

While correlation implies causation is does not equal it. Therefore, the reporter should not assume one causes the other.

77
Q

In your own words, what is restriction in range? How does it influence interpretation of correlation?

A

When you take only a subset of data that you are looking at. It influences the interpretation because the data alone does not represent the population. For example, if I only looked at family income of those in a sorority then I exclude those not in one which was still a part of my data looking at family income on college students.

78
Q

In your own words, how does unreliability of measurement (attenuation) influence interpretation of correlation?

A

Lower the real effect

79
Q

a) What is an outlier? b) How can you identify outliers? When should you do this? c) How do they influence interpretation of correlation? d) When are outliers especially problematic?

A

a) An outlier is a score that is far apart from the rest of the data. b) Identify by looking at scatterplot ad remove before running Pearson’s r. c) Because correlations are shown via scatterplot, outliers may change the strength of the correlation, making it weaker. d) When they are 2-3 SD away from the mean.

80
Q

In your own words, what does regression do/tell us?

A

make predictions based on the past

81
Q

Give your own example of how regression concepts apply to everyday life. Identify the predictors ad criterion variables.

A

Fall stats extravaganza class grades on taking same professor for research IV thesis (grades 0-100) predictor on (likelihood take same class (1-7 Likert scale 1=very unlikely 7=very likely)

82
Q

a) What types of variables does this test regression analyze?

A

predictor and criterion (x and y no true IV or DV)

83
Q

What research design does this test analyze?

A

simple regression (non-experimental?)(no between or within?)

84
Q

Give two examples of a research question that fits this regression design

A

Everday: Does the number of social events attended predict how egotistical a college student is?
Research: Does the number of exercise days predict how healthy a person is?

85
Q

Why is it so common to use regression in everyday contexts (e.g., working at a counseling center, predicting who will be the best person to hire, etc.)

A

they are useful

86
Q

a) What is better, clinical, or statistical prediction? b) Why? (3 reasons=bias, #people, # predictiors) c) What general lesson does this teach us about statistics’ usefulness?

A

statisticak, bias number of people and number of predictors, c)it is very useful

87
Q

Why is it useful to use multiple predictors in multiple regression?

A

more info= better prediction

88
Q

How could you use multiple regression in your career? Identify the predictors and criterion variables

A

I want to make a prediction about the number of patients who will be admitted in the hospital (child life department).
Predictors= presence of pandemic COVID, number of vaccinated children, and number of risky tiktok videos online. Criterion = number of patients admitted

89
Q

Draw a Venn disgram showing a criterion and 3 predictors with a) no overlap b) with overlapping predictors c) an R of .22 d) R of. 75

A
90
Q

What does an R of .89 tell you?

A

lot of overlap

91
Q

What does an R of .19 tell you?

A

not a lot of overlap

92
Q

Satisfaction has a β=.44, Love has a β=.14, and Relationship Length has a B=.004, which is the best predictor?

A

satisfaction

93
Q

What’s better, percentages or frequency counts? why?

A

percentages are better because they provide more context. A frequency count is 7 and then you ask out of what because it means so little. 50% is better because you can make a proportion of like 1/2 just not the actual number of participants.

94
Q

What are 2 of the common issues with misleading graphs?

A

1) they do not start at 0
2) they have uneven intervals
(cherry picking too)

95
Q

A university asked students how much they liked going there and 99 students rated it a 7. What do we need to know, or what questions do you have before making sense of these numbers? (give 3)

A

1) How many students rated it a 1,2,3,4,5,&6?
2) Are there any outliers?
3) What is the average rating and standard deviation?

96
Q

You want this for the dashboard of your car (for obvious reasons). This is the average rating: 3 out of 5 stars. Based on the data, a) How could this be a good thing? b) How could it be bad?

A

a) 3 out of 5 stars could be good because there could be outliers who brought down the mean. For example, 150 people could have given 4 or 5 stars but the 3 haters gave it one star and the mean balanced around 3.
b) 3 out of 5 stars could be bad if outliers brought the mean up. Maybe the duck melts in the summer when it is left in the hot car so 150 people gave it 1 or 2 stars but the 3 people paid to rate it gave it 5 stars and brought the mean up.

97
Q

Give a real life (non-math, stat jargon) example of high variability. b) Give three numbers (M=5) with low variance. c) Draw a distribution with high variance

A

a) I put the bowl at the bottom of my stairs and I try to throw ping pong balls down the stairs and into the cup. When I do, they bounce everywhere, under the couch, near the front door, down the hallway. None made it into the cup. b) 4,5,6 c)flatter curve than normal because there is high variance, there are less numbers near the mean and more spread out.

98
Q

In your own words, explain step by step how to calculate variance

A

First you take a score (x) and subtract it by the mean. This is your deviation score (X-M). Next you square your deviation score (X-M)^2. Now we want the sum of all squared deviation scores (sum of squares) Sigma (X-M)^2=SS. Then, we divide the sum of squares by the number of scores (N) Sigma (X-M)2/N.

99
Q

You got a 62/100 on your Social Psychology test. a) What combination of M & SD would make you feel really good about this? b) What combination would make you feel bad?

A

a) I would feel really good about a 62/100 if the (M=8; SD=1.3) (low SD)
b) I would feel really bad about a 62/100 if the (M=98; SD =1.3) (low SD)

100
Q

Give your own example of something that is normally distributed. b) Why did it end up that way? c) How does it apply to your example?

A

a) Something normally distributed is the decibel level of laughter at a comedy show. b) THe decibels of laughter is normally distributed because of the nultiplicity of causes. Multiplicity of causes means that the factors affect how something turns out. You only need to be average to fill in the middle, extremes are much less likely because everything has to go exceptionally well or terribly bad. c) Applying multiplicity of causes to my example of laughter, the factors of drunkness, hearing, familiarity, hecklers, and joke telling contribute.

101
Q

Most things most of the time are_____. Most things you see on social and other media are______. Why is this a problem

A

Average
Extremes
People think they are above average (better than average effect) when they are not. Big groups have small difference, the average is where most people fall. This affects mental health because they aren’t as good as they think they can be. Those famous social media influencers are outliers, the average maybe has 250 followers on social media.

102
Q

The average person has 4 really close friends (SD=0.50) a) Build your own distribution including Z scores, and the normal curve. b) Add in the empirical rule. c) If a person has 5 friends, what is their Z? d) What is they have 3 friends? e) What percentage of scores is above 4.50? f) What % falls within one SD of the mean? g) What % has more than 4 friends?

A

b) 68-95-99
c) z=2
d) =-2
e)16%
f)68%
g) 50%

103
Q

M=55.78 SD=22.11; Raw Score=55.78 What is the Z score?

A

z=0

104
Q

Tate has 4000 followers on Tiktok, Olivia has 1000 followers on Snapchat. Based on this information, who is most popular? How could a Z score help us compare them?

A

It seems like Tate is most popular but we do not know because the followers are on different social media platforms, the each have their own means and standard deviations. A Z score helps us compare them because Z scores are universal. When we calculate the Z score of the followers on the different social media platforms, we can compare them all.

105
Q

a) Give your own example when using samples instead of populations occurs in everyday life. b) Why do we study samples instead of populations? c) There are 3 things that help a sample be more representative. Give any two.

A

a) I like to know if my reaction to a confrontation was an average response not an overaction so I ask multiple people how they would have reacted in my situation. b) We study samples because it would be near impossible to collect from an entire population. It would take a lot of time, money, and effort. A sample is a representation of the population which is easier to get data from. c) (1)Sampling technique, random sampling is more representative (2) large sample size, more representative because more likely to capture the population.

106
Q

a) What does it mean to think probabilistically instead of dichotomously? b) Give your own example. C) What’s the benefit of thinking probabilistically.

A

a) Thinking probabilistically is to think continuously instead of dichotomously which is thinking categorically. b) If I want to know how much I should care about climate change, I am on a line of more or less caring rather than if I should care or not. c) It is beneficial because I know how much effort or care I should put in as well as it provides me a way to change my outcome. If I am caring too little, I could do more for the planet and raise my amount of care.

107
Q

If p=.04 what percentage is that? b) Can p=0.00?

A

a) 4% b) No, nothing is impossible only unlikely so we say p<0.001

108
Q

In your own words, explain the subjective interpretation of probability. Give your own example.

A

The subjective interpretation of probability is taking a statistic and choosing how you want to view it. We tend to think something bad won’t happen to us and something good definitely will. If your doctor told you that heart disease affects 50% of the population, your subjective interpretation is that you will be lucky and heart disease will not happen to you. Or, your neighbor said he will plow the snow off 50% of the driveways on your street. You think you are going to be one of the houses that gets your driveway plowed but your chances are still 50/50, it is possible that it will not happen to you.

109
Q

a) In your own words, what is an effect size? b) Draw a set of distributions that represents a small effect c) Draw a set that represents a large effect size. d) Label each with the appropriate effect size convention.

A

AN effect size is how much two distribution’s do not overlap. Thee distance between the means of distributions.

110
Q

IN your own words, what is statistical power? b) There a 4 ways to increase statistical power. List any two.

A

a) Statistical power is the likelihood that we correctly reject the null hypothesis. (The opposite of type II error)
b) In crease sample size and have a big effect size

111
Q

X=

A

score

112
Q

Sigma=

A

sum

113
Q

SD^2

A

variance

114
Q

(X-M)/SD

A

z-score

115
Q

N=

A

number of scores

116
Q

SS=

A

sum of squares

117
Q

SD=

A

standard deviation

118
Q

SS/N

A

variance

119
Q

M=

A

mean

120
Q

Sigma(X-M)^2

A

sum of squares

121
Q

SQRT(SD)^2

A

standard deviation

122
Q

d=

A

effect size

123
Q

Why did I show you a picture of a stack of weights?

A

To show who was weak and who wasn’t as well as show the normal curve

124
Q

What’s the coolest metaphor for samples/populations?

A

Coldstone ice cream example. Coldstone allows you to try a sample of their ice cream, it is a tiny little spoonful. The idea is that if you life the taste you will like the full scoop. Or if you don’t like the taste, you won’t like the full scoop. The idea is that the tiny little spoonful is trying to be representative of the large population that is a full serving of ice cream.

125
Q

a) Distinguish between testing hypotheses & exploratory findings.

A

a) Testing hypotheses must be written before any testing is done (preregistration is best) Whereas exploratory findings are not. Instead you note you are doing this after your study results to see what else your study showed (like fishing).

126
Q

What are the three tenets of open science

A

preregistration (transparency) Open data (reproducibility) and Open Materials (replication)

127
Q

What is the null hypothesis?

A

no difference (change, effect); states everyone is from the same population (opposite of research hypothesis)

128
Q

Is type I error a false positive or false negative?

A

Type I error is a false positive (“fake” positive, claim true ‘reject’ but not real (fail to reject)

129
Q

Explain in your own words how to create a distribution of means

A

You would take a certain number of people’s scores, say 3. You take the mean (average) score and plot it. Then you take another 3 people’s scores, take the mean (average) and plot it. You repeat this over and over and over. The more people you have the more normal the distribution becomes.

130
Q

For each set of means (on a 7pt scale) and p value, indicate what you can say about the difference in the means: a) Is the effect size relatively big or small? b) Is it significant? c) Is it meaningful?
3.48 3.95 <.001
1.66 5.23 .95
3.55 3.86 .50
2.28 5.03 .041

A

small effect yes significant probably not meaningful
big effect not significant can’t be meaningful
small effect size no not significant, can’t be meaningful
big effect size yes significant yes meaningful

131
Q

5.40 1.29 N=155
2.33 2.13 N=27,582
6.73 1.02 N=88

A

almost definitely
most definitely
almost definitely

132
Q

d= .48 N=212
d=.12 N=6,744
d=.81 N=12

A

yes, likely
yes, likely
No, not likely

133
Q

In your own words, what does a t-test for dependent means do/tell us? (i.e., looking at the formula, what is it doing?)
b) Give your own example of a research question that fits this design

A

a) We are looking for change (before/after; pre/post). The formula is to compare your mean difference score to the comparison distribution’s mean (0) over typical difference (SD). The context is that typical difference so that’s what we want to compare our score to. b) DOes 30 min physical activity everday improve a person’s health?

134
Q

You run a study and find the mean difference score in your sample is 3.44. Is that a lot? How would you know?

A

We have no idea at this point. To find out, we would have to compare our score to the typical difference (SD) because that is the context

135
Q

In your own words, what does a t-test for independent means do/tell us? (i.e., looking at the formula, what is it doing?) b) Give your own example of an everyday question that fits this design.

A

a) Independent t-test is looking for difference between groups. The formula has us take the mean of group 1 and subtract it by the mean of group 2 over the typical difference (SD). Context in denominator (SD). that’s what we compare it to.
b) are people who take the bus sketchier than people who drive their car?

136
Q

How is the pooled estimate like the house of representatives?

A

The pooled estimate gives influence to the larger group. (weighted average) IN this way it is like the House of Representatives because they give more votes (influence) to the states with larger amount of people (for fairness)

137
Q

IN an ANOVA, where do our variance estimates come from? (i.e., what are the 2 scoures? b) WHich estimate do we hope is big? WHich do we hope is small?

A

a) The variance estimates come from the between groups (has treatment) and within group (change only). b) We hope the between is big. We hope the within is small.

138
Q

Within-group variation comes from chance factors? What are they? (just list)

A

a) 1. personality 2. experience 3. mood 4. dumb luck

139
Q

Explain the idea of signal to noise ratio as it applies to ANOVA

A

signal is the treatment effect, noise is the chance factors. We want to find a treatment through all the natural variation. (change)

140
Q

Please draw 3 distributions for a one-way ANOVA that shows when the null=not true

A
141
Q

We discussed in class how thinking about the world in terms of within and between group variance is helpful a) WHich type do we pay too much attention to in life? b) WHich type do we not pay enough attention to? c) Give your own example of when this happens

A

a) We pay too much attention to between group variance
b) We do not pay enough attention to which group variance
c) We treat the elderly as though they are different from everyone else (between). THey are not really that different, we have more in common with them than we do differences (within)

142
Q

t(145), p=.55 d=.042 which number(s) is/are the degrees of freedom?

A

145

143
Q

t(145), p=.55 d=.042 Which number(s) is/are the significance level? Is it significant?

A

.55, no

144
Q

t(145), p=.55 d=.042 WHich number(s) is/are the effect size? Is the effect size considered low, moderate, or high?

A

.042, very low

145
Q

t(145)=1.88, p=.55 d=.042 Which number(s) is/are the t score?

A

1.88

146
Q

F(2,129) = 2.74, p=.0052 N^2 =.25 which number(s) is/are the degrees of freedom?

A

2,129

147
Q

F(2,129) = 2.74, p=.0052 N^2 =.25 Which number(s) is/are the significance level? Is it significant?

A

.0052, yes

148
Q

F(2,129) = 2.74, p=.0052 N^2 =.25 WHich number(s) is/are the effect size?

A

.25

149
Q

F(2,129) = 2.74, p=.0052 N^2 =.25 Which number(s) is/are the F score?

A

2.74

150
Q

Who is cooler, people who drink coffee or tea?

A

t-test for independent means

151
Q

Which athletes spend more time in the gym? Football players, cheerleaders, or swimmers? Do cheerleaders workout more than the other two?

A

one way ANOVA with planned contrasts

152
Q

A mesure of optimism had 12 items. Was it s good scale?

A

cronbach’s alpha

153
Q

Are lab groups different in their bottle flipping ability? Is group 4 worse than the other groups?

A

one way ANOVA with planned contrasts

154
Q

IV=Nominal variance with 2 levels-within-subject, DV=continuous

A

t-test for dependent means

155
Q

How many students at Monmouth are psychology majors?

A

descriptives-> frequencies

156
Q

Does appreciation for Dr.L’s statistical jokes change from the beginning to the end of the semester?

A

t-test for dependent means

157
Q

Do people who prefer different types of coffee (Rook, Dunkin, Starbucks) have different numbers of Tiktok followers? If so, which matters?

A

one way ANOVA with post hocs

158
Q

You have data for everyone in class. AMong only those with summer birthdays, how much do they like being outdoors?

A

select cases-> descriptives frequencies

159
Q

DOes mood improve after taking a walk on the Long BRanch boardwalk?

A

t test for dependent means

160
Q

Name 4 animals on our “Keep calm and love spss” slide from lab

A

sloth duck chicks rabbit cat monkey guinea pig

Research 1 (6 decks)

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