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Research 1 > Research 2 Exam 2 Study Guide > Flashcards

Research 2 Exam 2 Study Guide Flashcards

1
Q

What was the point of the video we watched?

A

The point of the video we watched was to show us an example of how a researcher had broken ethics in the way of fraud/fabrication of data. A student showed this to Lewandowski and it is a more interesting way to teach the topic of open science.

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2
Q

What’s the difference between replication and reproducibility? Which is more likely to work? Why?

A

Replication means that we can use the data they provide, run what they ran, and receive the same results as the study. This is a check to make sure that they did their analyses correctly and did not complete an underhanded act of breaking ethics.

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3
Q

Generally, what are questionable research practices?

A

Practices that are sketchy. They seem suspicious and the choice is not appropriate to a researcher.

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4
Q

What is HARKing? b) Distinguish between testing hypotheses & exploratory findings.

A

Harking is hypothesizing after collecting data and analysis. In this questionable practice, they find data and then say oh yeah I expected this to happen. Liar, you just looked for a pattern and then claimed it all along. When our studies go through the IRB, we have to submit our hypothesis among other information in advance to any data collection. Testing your hypothesis means that you formulate your research question and then you set up your experiment, collect data, and then in your findings, determine if your hypothesis was supported or rejected. Exploratory findings are not created before the experiment, but done after you have the data. They are just some thing you wanted to look into or explore but not a hypothesis you test.

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5
Q

What is p-Hacking? What are the 3 ways of doing that?

A

P-hacking is probability hacking, which means you affect your probability.
1.Cherry picking
2.Run your study until it works (e.g., analyze every 5 participants.
3.Play with variables to get them to work (e.g., selectively drop/include items)

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6
Q

How often does falsifying data/fraud happen?

A

Not very often, it was reported 0.6 the BTS nerds who check the data found 1.7, although doubled, is still pretty low.

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7
Q

What is preregistration? Why do we do that? Will this stop fraud/fabrication?

A

stating your hypothesis before conducting your study. Often in a public forum. We do it to show that out hypothesis was in fact created prior to the testing. It will prevent not stop fraud because there are multiple ways to commit fraud, this would only be stopping people from testing the study and then claiming that they knew it all along. It would not stop people from faking data.

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8
Q

What are the three tenets of open science? Describe each. Which one helps with reproducibility? Which one helps with replication?

A

preregistration (keeps you honest), open data (every one can analyze your exact same data) reproducibility and open materials (replicability) can redo your study with their own participants.

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9
Q

a) Give your own example of a double negative (including what it actually says). b) How does this apply to hypothesis testing?

A

a) I don’t disagree with you (I agree with you), you ain’t going nowhere(you’re going somewhere)
b) fail to reject the null
a) I am not never going to study. ( I am going to study)
b)In hypothesis testing, in order to reject the null we make a double negative. The null is that there is no difference, we show that there is not no difference (there is a difference) and we reject the null.

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10
Q

What is the null hypothesis?

A

states there is no effect (change, difference) and the populations are the same The opposite of what we are hypothesizing. SImilar to a straw man argument we are setting it up to hopefully tear it down and support our hypothesis.

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11
Q

Hypothesis test require that we start off assuming we’re wrong (with the null hypothesis). Give your own example of how this approach could be beneficial in another context.

A

This approach could be beneficial if we were in an ethical dilemma. If I thought it was possible my boss was committing fraud, but I did not have proof. It is better to assume I am wrong, and she is not committing fraud. THen I would talk to my boss and ask what they are doing. If it is fraud then I would report it, but if it isn’t then I end the process there. (If i assumed there was fraud when there was not (type 1 error) then they would have done all the extra processing and investigating to turn up with no fraud.

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12
Q

a) List the 5 steps of hypothesis testing? b) In your words, describe what we’re generally doing in each. (we talked a bit about this in class, but also refer to the Table on pg 188)

A

a) First you have to label(establish) your population and your hypothesis. You need to know who you want to test/take a sample from and what you are looking for.
Then you need to build a comparison distribution
Third you establish the critical value cutoff
FOurth you determine the sample results
Finally you decide and interpret
b)state who you want to test/take sample from and what you are looking for (hypothesizing)
Then you need context so you have to see what or who you are comparing to. You should create a normal distribution.
Next, you establish the cutoff you must state what value or percentage you must reach in order for your result to count.
After that, you determine your sample results, conduct your study and receive the findings
Last, you compare the sample results to the cutoff and make a decision about the study.

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13
Q

Distinguish between Type 1 and Type 2 error. Which is a false positive? Which is a false negative?

A

A type 1 error is when we claim something is true when it is false. (We say there is a ghost, when there is not ghost). This is a false positive because we think it is true but in actuality it is not. Fake true.
A type 2 error is when we claim something is false when it is true. We fail to reject the null when we should. (We say there is nothing there when there actually is a ghost). This is a false negative because we think it is false but in actuality it is not. Fake False.

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14
Q

Interpreting p values. is this significant?
a) 5.43 4.47 N=1000

A

most definitely

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15
Q

Interpreting p values. is this significant?
b) 5.43 4.27 N=63

A

probably not if 63 is small sample

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16
Q

Interpreting p values. is this significant?
8.21 8.64 N=1,999,999

A

Most Definitely

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17
Q

Interpreting p values. is this significant?
8.64 8.21 N=6

A

Definitely Not

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18
Q

Interpreting p values. Is this significant?
5.55 5.73 N= 200

A

definitely not

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19
Q

Interpreting p values. Is this significant?
6.78 9.91 N=200

A

most definitely

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20
Q

Interpreting p values. effect size? significant? meaningful?
a) 5.43 4.27 p=.03

A

small effect, significant, not meaningful

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21
Q

Interpreting p values. effect size? significant? meaningful?
5.43 4.27 p=.78

A

small effect, Not significant, not meaningful

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22
Q

Interpreting p values. effect size? significant? meaningful?
8.21 8.64 p=.002

A

small effect size, significant, not meaningful

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23
Q

Interpreting p values. effect size? significant? meaningful?
8.64, 8.21 p=.10

A

small effect size, Not significant, not meaningful

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24
Q

Interpreting p values. effect size? significant? meaningful?
5.55 3.98 p=.000

A

Big effect size, impossible significance, meaningful?

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25
Q

Interpreting p values. effect size? significant? meaningful?
6.78 6.03 p<.001

A

small effect size, significant, not meaningful

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26
Q

Interpreting p values is this significant?
d=.15 N=6000

A

most likely

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27
Q

Interpreting p values is this significant?
d=.85 N=58

A

Most likely

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28
Q

Interpreting p values is this significant?
d=.05 N=100

A

probably not

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29
Q

Interpreting p values is this significant?
d=.62 N=1547

A

most likely

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30
Q

How does the mean of the distribution of means compare to the mean of the population of individuals? Why does this happen?

A

Equal to the mean of popul - all scores from og population. Taking samples plot for distribution, the mean is

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31
Q

Explain in your own words how to create a Distribution of Means.

A

To create a distribution of means one would take the mean of every 5 people in a sample, over and over and then plot those means. Doesn’t always have to be five, taking samples mean of sample over and over.

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32
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? What does it compare?)

A

Compares within-subjects groups to see if there is a change from the first time the group is tested to the second time the same group is tested. For the t-test formula, we have the average difference subtract the comparison distribution to find difference, then divided by the average variability (with standard deviation)

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33
Q

What types of variables to the t-test for Dependent means test analyze?

A

Nominal/categorical independent variables, interval ratio continuous dependent variables

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34
Q

What research design does the t-test for Dependent means test analyze?

A

Strawman, like a z-score test? It is a t-test for dependent means THIS IS A WITHIN SUBJECTS DESIGN

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35
Q

t-test for dependent means Give 2 examples of a research question that fits this design (one related to everyday life and one related to research)

A

Everyday life: Does coffee improve test scores? Research: Are people more willing to break the rules after doing so once?

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36
Q

a) What is a difference score? b) How do you calculate it?

A

A difference score is the subtraction of the post to the pre or after to before. You calculate this by looking at the first time the group was tested and subtract that by the second time the same group was tested.

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37
Q

a) What is the comparison distribution for the t-test for dependent means? b) What is the mean of this comparison distribution? c) Why?

A

a) The comparison distribution is a distribution of means of DIFFERENCE SCORES “sampling distribution”
b) The mean is 0
c) because it always is, it represents the null hypothesis that there is no change from the first time the group was tested to the second time the same group was tested.

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38
Q

Do you want to have a lot of degrees of freedom, or fewer degrees of freedom? Why?

A

Yes, you want to have a lot of degrees of freedom. The degrees of freedom are the number of the participants in the sample minus 1. We want our sample size to be large and more representative. A larger sample would mean a larger degree of freedom. Additionally, by having more df, our Distribution becomes MORE NORMAL.

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39
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

Maybe? We do not yet know. The only way to know is with the context of the typical difference score. (If the typical is 3, than our sample is not that difference, if the typical is 1, than yeah our sample is pretty large.)

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40
Q

You run a study and your sample’s average difference score is 2.78. The comparison distribution has a mean of____and a standard error of 2.59. Is the difference in your sample big or small?

A

0; Small because we would be just slightly higher than one standard deviation from the mean. It is likely that our difference score is not big enough to pass the cutoff.

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41
Q

a) If your study fails to reject the null hypothesis, what does that mean? b) What two things can you do to avoid this happening?

A

Our hypothesis was not supported by the results of the study. We did not reach the cutoff we needed for our test to be significant. increase sample size and increase effect size.

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42
Q

When running a study, why do we include items that we don’t plan on analyzing?

A

Some times we place distractor items within our study?

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43
Q

When communicating results, what is the key with everyday language?

A

Write it as if your grandma or uncle could understand exactly what you are talking about without any statistics knowledge.

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44
Q

t (53) = -2.27, p = .03, d = .42.
Which number(s) is/are the degrees of freedom?

A

53

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45
Q

t (53) = -2.27, p = .03, d = .42.
Which number (s) is/are the significance level? Is it significant?

A

.03, yes

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46
Q

t (53) = -2.27, p = .03, d = .42.
Which number(s) is/are the effect size?

A

.42

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47
Q

t (53) = -2.27, p = .03, d = .42.
Which number(s) is/are the t score?

A

-2.27

48
Q

t (53) = -2.27, p = .03, d = .42.
Which number(s) is/are the p level? Is it significant?

A

.03, yes

49
Q

t (53) = -2.27, p = .03, d = .42.
How many tails is the test?

A

two

50
Q

t (53) = -2.27, p = .03, d = .42.
In APA style, all statistical symbols are formatted in?

A

italics

51
Q

In your own words, what does a t–test for independent mean do/tell us? (i.e., looking at the formula, what is it doing?)

A

We are comparing between-subjects groups to find is they are from two different groups. Are there two kinds of people? The formula for the t-test is to subtract the mean of the first group to the mean of the second group divided by the standard difference (standard deviation)

52
Q

What types of variables does the t-test for independent means test analyze?

A

The IV are nominal/categorical and the DV are continuous (interval/ratio)

53
Q

What research design does this t-test for independent means test analyze?

A

This is a between-subjects research design

54
Q

Give 2 examples of a research question that fits this t-test for independent means design. (one related to everyday life and one related to research)

A

Everyday: Are college students who dress up for Halloween more fun than those who don’t dress up?
Research: Are commuters more stressed than resident students?

55
Q

Please give your own examples of a) Pre-Post Design b) Two-group Design using the same topic/research question for both.

A

Here’ s an example…
Topic: Happiness; Are people happier after they play with puppies?
PrePost: Measure everyone’s happiness, let them play with puppies, measure again.
Two-Group: Randomly assign some people to play with puppies while others don’t. Measure happiness of both groups)
Topic: Creativity; are people more creative after they participate in a free-reign painting activity.
PrePost: Measure everyone’s creativity, let them paint, measure again.
Two-Group: Randomly assign some people to paint while others don’t. Measure creativity of both groups)

56
Q

If I give you the name of the comparison distribution, you should be able to tell me if it is for a t-test for dependent or independent means.

A

Distribution of differences between means = t test for independent means
Distribution of difference scores = t test for dependent means

57
Q

a) What is the mean of the distribution of differences between means? b) What does this number signify?

A

0, this is the theoretical for no difference between populations, it represents the null hypothesis

58
Q

What is the t-test for independent means comparison distribution? How do we create it?

A

The mean of the distribution of differences between means is the comparison distribution for t test of independent means. We create it by estimating the population variance, then take the average, distribution of means, then the pooled estimate of population variance. We get the typical difference (sd) and use the mean of 0 to draw the distribution. (we would also show the t test cutoff)

59
Q

a) How do we learn the population’s variance? b) What is the purpose of pooled estimate? c) How is the pooled estimate like the House of Representatives? d) Why do we do this (i.e., what does it accomplish)?

A

We learn the population’s variance by estimating, we use 2 between subjects samples that represent the population.
b) Larger of the samples gets more influence, the purpose is to make this happen when necessary
c) the pooled estimate is about the amount of influence each group gets. In the house of representatives bigger states with more people in them have more votes/say than small states with less people.
d) we do this to make it fair, more people get more say than fewer people do.

60
Q

a) What is the formula for a t-test for Dependent means?

A

t= mean difference-comparison distribution/ standard difference (deviation)

61
Q

b) What is the formula for a t-test for Independent means?

A

t= mean of group 1 - mean of group 2/ standard difference (deviation)

62
Q

Remember your training from previous labs so that you can not only run t-tests but also run reliability/calculate means as needed.

A
63
Q

What Statistic Is This?
Does watching a tutorial improve students’ understanding of how to use EXCEL?

A

t test for Dependent means

64
Q

What Statistic Is This?
A scale has 10 items that all allegedly measure charisma. Do they all measure the same thing?

A

cronbach’s alpha reliability

65
Q

What Statistic Is This?
How many students at Monmouth live on campus?

A

descriptives frequencies

66
Q

Who is cooler, people who have an iPhone or an Android?

A

t test for independent means

67
Q

How many patients at the local hospital have insurance?

A

descriptives frequencies

68
Q

Who has less anxiety, a group who did CBT therapy, or a group who did psychodynamic therapy?

A

t test for independent means

69
Q

Are 10 self-report items that measure narcissism reliable?

A

cronbach’s alpha

70
Q

Do clients’ depressive symptoms change from using relaxation techniques?

A

t test for Dependent means

71
Q

What is the mean of x-anxiety?

A

descriptives statistics (calculate mean)

72
Q

Does an athlete’s mile time improve after drinking Gatorade?

A

t test for Dependent means

73
Q

At Monmouth, what’s the average GPA of only Psychology majors?

A

select cases - descriptive statistics (average)

74
Q

Taylor sends 1989 texts a day. Is that in the top 5%?

A

z-score

75
Q

IV=Nominal variable with 2 levels-Within-Subject, DV=Continuous

A

t test for Dependent means

76
Q

IV=Nominal Variable with 2 levels-Between-Subjects, DV= Continuous

A

t test for independent means

77
Q

How is an ANOVA similar to a t-test for Independent Means? What is the key difference?

A

Similar because still comparing groups (are they the SAME population?) BUT the key difference is now we want to compare 3 or MORE groups.

78
Q

In your own words, what does an ANOVA do/tell us? What is the goal? What is the null?

A

Compare if there are differences in or between groups consisting of 3 or more groups. The goal is to find variance?

79
Q

a) What happens when you run multiple tests? b) What is an analysis plan? What does it help us do? c) How are exploratory analyses different from hypothesized ones?

A

You are being exploratory not testing your hypothesis. Analysis plan lists all the things we are going to run. It helps use visualize our steps and makes it easier to follow all steps. c) exploratory analyses are just looking for things, a hypothesized analysis is when you have previously stated that you are looking for something, and then you perform that operation (stat).

80
Q

In an ANOVA, where do our variance estimates come from? (i.e., what are the 2 sources?) b) When running a study, what do we hope is big? What do we hope is small?

A

Our variance estimates come from with a within-group or between-group design
b) we hope the between-group population is big. We hope the within-group population is small

81
Q

What types of variables does this test ANOVA analyze?

A

IV = nominal/categorical DV = continuous (interval/ratio)

82
Q

What research design does this test ANOVA analyze?

A

This is a between subjects design? both?

83
Q

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

A

this design. (one related to everyday life and one related to research)
Everyday: Do people who like Sushi have less allergies than those who like pasta or chicken?
Research: Does classical music, popular music, or rock music best help a plant grow?

84
Q

Where does within-group variation come from? (Give several sources with example for each)

A

Dumb luck, mood, experiences, personality
DUmb luck, you could answer questions correct when you merely guessed or incorrect when you should have known them, people may have come in knowing how to dance, so when they are randomly placed they could be in the teaching dance group, but they already know it/find it easy, or they could be in the no treatment/control group and already knows how to dance scoring higher than most.

85
Q

The within-group estimate establishes the baseline. In your own words, what does that mean?

A

your own words, what does that mean?
The baseline is the typical, the group come in with diversity and their own

86
Q

a) Give 3 group means with low variation. Give 3 group means with high variation. b) Which is more likely to represent the null? c) Which is more likely to depict samples that come from different populations?

A

Low variation= 3,4,5 High Variation, 1,4,7
b) the low variation is more likely to represent the null
c) the high variation is more likely to depict samples that come from different populations.

87
Q

In your own words, please explain how we use sample means to estimate the between-groups variance.

A

We compare all sample means of the multiple groups together to estimate between-group variance.

88
Q

In your own words, what is sampling variation?

A

Always some due to change (when the null is true)

89
Q

a) Please draw 3 distributions that show between group estimation of population variance when the Null = True. b) Please draw another 3 distributions that show when the Null = Not True

A

a) like dog example
b) like typical we hope it has more variance

90
Q

For the between-group estimate, the estimate can be different for two reasons. What are they?

A

Change factors
The Effect/Treatment

91
Q

Be sure you’re able to a) Generate Analysis Plans based on descriptions of data (like you did on Exam 1) b) Identify the key statistic based on a research question (like we’ve been practicing in class) c) Draw conclusions to a research question based on SPSS output (you’ve been doing this in lab)

A
92
Q

How are post-hocs and planned contrasts similar/different?

A

Post-hocs just look for anything but running everything planned contrasts look specifically for preplanned hypothesis, you want to know how a specific group compares to another or another two etc.

93
Q

a) Which is more like a fishing expedition, post hocs tests or contrasts? Why? b) Which do you use for exploratory work? c) Bonferroni and Tukey are both what? (hint: not delicious forms of pasta)

A

a) Post hocs, you just hope something bites
b) post hocs are for exploratory work
c) they are both post-hoc tests

94
Q

a) Using a picture (I’d give you something like what I did in class with the 3 cats and a dog), what would an ANOVA test? b) What would a t-test for independent means test? c) What would post-hocs test? d) What would contrast codes test the post-hocs couldn’t test?

A
95
Q

Using the breakfast food study (Pop Tarts, Eggs, Coffee), show contrast codes for the following comparisons: a) Pop tarts vs coffee b) Eggs vs. Pop Tarts & Coffee) c) Pop Tarts vs. Eggs

A

POP Tarts Eggs Coffee
a) 1, 0, -1
b) -1,2,-1
c) 1, -1, 0

96
Q

Based on this table, which groups are different?
Pop Tarts 5.23(.18)ac Eggs 2.58 (.54) b Coffee 4.44 (.23)c N=93

A

Pop Tarts and Eggs are different because they have different subscripts, Eggs and Coffee too.

97
Q

Interpreting Statistics: F(2, 187) = 1.74, p = .048, η² = .09.
Which number(s) is/are the degrees of freedom?

A

2,187

98
Q

Interpreting Statistics: F(2, 187) = 1.74, p = .048, η² = .09.
Which number(s) is/are the significance level? Is it significant?

A

.048, yes

99
Q

Interpreting Statistics: F(2, 187) = 1.74, p = .048, η² = .09.
Which number(s) is/are the effect size?

A

.09

100
Q

Interpreting Statistics: F(2, 187) = 1.74, p = .048, η² = .09.
Which number(s) is/are the F score?

A

1.74

101
Q

Interpreting Statistics: F(2, 187) = 1.74, p = .048, η² = .09.
Which number(s) is/are the p level? Is it significant?

A

.048

102
Q

How many tails is the test?

A

two

103
Q

In APA style, all statistical symbols are formatted in?

A

italics

104
Q

What statistic is this?
Do exam grades improve from the first to second test?

A

t-test for dependent means

105
Q

What statistic is this?
Are there awesomeness differences between students at Monmouth Rider, or Rutgers? If so, what are they?

A

ANOVA post-hoc

106
Q

Do psychology, anthropology, or sociology students send more texts? Do psych students send more than the others?

A

ANOVA and planned contrasts

107
Q

How many texts does a typical high school senior send?

A

descriptives - frequencies

108
Q

A new research student designs a study testing 23 different types of exercise on mood.

A

ANOVA post-hoc

109
Q

Based on midterm grades, do students do better in history or English?

A

descriptives - frequencies

110
Q

Among current students, are 4th year students less stressed compared to the 1st or 2nd year students?

A

ANOVA planned contrasts

111
Q

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

A

signal is the between group and noise is the within group. There is a trend in the data but he have to separate the chance factors and variability that are between every individual.

112
Q

When the null is true, does that affect the between-group or within-group estimate? or both? why?

A

It affects the between-group because it means the treatment is 0.

113
Q

When the null is true, the between group and within group estimates of the population variance are essentially the same. why?

A

they are the same because the treatment is about 0. therefore only chance factors are shown in the between and within estimates leaving a total of 1.

114
Q

in your own words, what does the f ratio show?

A

how different your distribution is from the comparison

115
Q

a) draw and label the F distribution b) what does the shape tell you about F ratios? c) What might this suggest about life?

A

b) most fall around 1, few fall near 5 or higher
c) most people are similar, no difference, only few are outside this and truly different.

116
Q

We discussed in class how thinking about the world in terms of within and between group variance is helpful. a) Give your own example of how you can apply these variance concepts to life. b) Which type do we pay too much attention to in life? Which type do we not pay enough attention to? c) Give your own example.

A

You want to push away the noise and only notice the treatment. I want to make sure that my treatment works and it isn’t the chance factors. WE think about differences between groups but have to consider similarities
We often pay too much attention to the between group
Between groups are fall season lovers and spring, winter, summer season lovers. Within groups is all people who have different personality, mood, experience, and chance luck.

117
Q

Your thesis on how different breakfast foods influence stat exam scores compares Pop Tarts, Organic/Farm Raised Eggs, and Black Coffee on test scores. Your ANOVA test comes back significant. You do a dance of joy (obviously). a) What does the significant result tell you? b) What does it not tell you? (related to your study)

A

It tells you there is a difference between pop Tarts, Organic/Farm Raised Eggs, and Black Coffee on test scores. YOu do not know where the difference is. WHat specifically makes the impact on test scores. Is it pop tarts? Eggs? Coffee? Is one better than the others?

Research 1 (5 decks)

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