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Advanced Quantity Research Methods > Masterclass 1-3 > Flashcards

Masterclass 1-3 Flashcards

1
Q

What is a variable?

A

A variable is a characteristic of things that may take more than one value. In probability, a random variable is an expression whose value is the outcome of an experiment.

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2
Q

What are the two components of Hume’s conception of causality?

A
  1. CONTIGUITY (contiguity in space and time of cause and effect) and
  2. CAUSE-EFFECT (cause occurs before effect), where there is a necessary connection if cause ad(?) effect.
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3
Q

What is a sufficient condition for an effect to occur?

A

if that condition occurs the effect will occur. It may be necessary or not.

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4
Q

What is a necessary condition for an effect to occur?

A

If and only if that condition occurs the effect will occur. It may be sufficient or not.

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5
Q

What are necessary and sufficient conditions?

A

With both necessary and sufficient conditions, they have to occur for the effect to occur and no other conditions will make the effect occur.

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6
Q

Why did David Lewis propose to take into account only the second part of Hume’s definition of causality?

A

There are situations in which the first rule occurs and there is no causality: night follows the day but the day is not the cause of night. The connection between cause and effect may not happen repeatedly (e.g. a person shoots and kills another person).

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7
Q

What does each rug of Pearl’s ladder of causation tell us about causality?

A

Observations: correlations;

Actions: causation by intervention;

Imagination: causation by modelling

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8
Q

What is a chain? And, why is it important?

A

In chain and mediation of X–>V–>Y,

The effect of X on Y is mediated by V.

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9
Q

What is a common cause? And why is it important?

A

In confounding U–>X–>Y, we are studying whether X causes Y. But U–>Y and so U is a common cause of X and Y.

U is confounding the link X–>Y (the confound is on the DV Y).

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10
Q

What is a collider? And, why is it important?

A

In Selection bias X–>Y–>Z, we study whether X causes Y (Z the collider is on BOTH the IV X & DV Y).

Z is a common effect of X and Y or a collider. If we control for Z we may find an association X–>Y that was absent if not controlling. Or we dilute an actual r/s.

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11
Q

In a study, the Bayes Factor (BF10) comparing an H1 to a H0 is 0.05…

A

Meaning: the data is 20x more likely under the H0 than under the H1.

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12
Q

The Bayes Factor (BF10) comparing the H1 and H0 is 0.10…

A

Meaning: the data is more likely under the H0 than under the H1.

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13
Q

The prior distribution in the model of the H0 used by JASP is:

A

A spike over the value 0.

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14
Q

In a study, the Bayes Factor (BF10) comparing an H1 to a H0 is 0.5…

A

Meaning: the data is 2x more likely under the H0 than under the H1.

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15
Q

The probability of the data under the H1 is 0.001 and that of the H0 is 0.0001. What is the value of the Bayes Factor (BF10)?

A

a. 1
b. 0.0001
c. 10.
d. 0.001.

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16
Q

The prior distribution in the model of the H1 used by JASP is:

A

A symmetrical distribution centered around the value 0 (or another H0).

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17
Q

In the H0 testing approach, if the value of the statistic calculated in the sample (e.g. b1), or a more extreme value, has a lower probability than that of a pre-established threshold, what is the correct decision?

A

To reject the H0.

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18
Q

In the Maximum likelihood estimation approach, why is the more complex model penalised?

A

To avoid overfitting.

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19
Q

Which component in the Bayesian approach is the most similar to an equivalent component in the Traditional approach?

A

Probability of the data given a parameter value.

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20
Q

The probability of the data under the H1 is 0.008 and that of the H0 is 0.8. What is the value of the Bayes Factor (BF01)?

A

a. 0.0001.
b. 100.
c. 1.
d. 0.001.

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21
Q

Consider two sampling distributions (A and B) from the sample population of values: A was constructed with samples of 10 values, B was constructed with samples of 100 values.

A

Ans: The standard error of A is larger than the standard error of B.

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22
Q

The sampling distribution of the mean is constructed by generating…

A

Thousands of samples of values and calculating their means.

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23
Q

Prior knowledge combined with likelihood allows to calculate…

A

A posterior distribution.

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24
Q

The mathematical formula that corresponds to the Linear model with one predictor variable:

A

Y ~ Normal (Beta0 + Beta1 X, sigma)

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25
Q

The standard deviation of a population and the standard error of the mean calculated from that population tells us that:

A

The standard deviation of the population is higher than the standard error of the mean.

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26
Q

If the prior distribution for a parameter value is a normal distribution…

A

the parameter values close to the mean of that distribution are more plausible than those far from the mean of that distribution.

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27
Q

The mathematical formula that corresponds to the Simplest linear model?

A

Y ~ Normal (Beta0, sigma)

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28
Q

If the prior distribution for a parameter value is a uniform continuous distribution…

A

All the possible values of that parameter are equally plausible.

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29
Q

In a linear model with B0=70 and B1=2.5, what is the expected value for Y for X=0?

A

a. 95.
b. 0.
c. 72.5.
d. 70.

30
Q

In a linear model in which the expected value for Y is 92, an actual observation of Y is expected to…

A

be 92 or a value close to 92.

31
Q

A psychologist uses the normal distribution as a model for her experiment. How would you best classify this model?

A

a) A model of reality
b) A statistical model.
c) A causal model.
d) A probability model.

32
Q

Which of the following options provides a correct classification of the following variables:

a) Interest in intellectual tasks (possible values: Low, Medium, High)
b) Reaction Time (measured in ms.)

A

Ordinal and Continuous

33
Q

What are the two components of DAG (directed acyclic graphs)?

A
  1. Nodes (represent variables)
  2. Edges (represent causal relationships)
    - They are directed, meaning - they are arrows that indicates that one variable causes the other variable.
34
Q

In DAG (directed acyclic graphs), which of the following paths is allowed?

A

A path from variable A to variable B, with no path from variable B to variable A.

35
Q

A cognitive scientist develops a computational model of the mind. How would you best classify this model

A

a) A model of reality
b) A statistical model.
c) A causal model.
d) A probability model.

36
Q

Counterfactual thinking forces us to…

A

Consider what would have been the effect had the cause did not occur.

37
Q

In DAG, variable A affects variable C, and variable B also affects variable C. How is variable C called?

A

A collider.

38
Q

A psychologist is interested in investigating the effect of variable A on B. She knows that there is a variable she cannot measure that affects both A and B. What is the effect of that unmeasured variable?

A

Confounding

39
Q

A health psychologist develops a model including the variables therapy and depression. How would you best classify this model?

A

a) A model of reality
b) A statistical model.
c) A causal model.
d) A probability model.

40
Q

How is the following structure of a DAG called?

A–>B–>C

A

Chain.

41
Q

What is the difference between the concept of variable in science and research and that in probability?

In science and research a variable refers to…

A

a characteristic or a feature of a thing.

42
Q

What is an event in probability theory?

A

A subset of the sample space.

43
Q

Which of the following statements is true regarding a probability density?

A

It integrates to 1.

44
Q

Consider an experiment in which the sample space contains the following outcomes ω1, ω2, ω3, ω4. Event A is defined as the set containing ω1, ω2, ω3, and event B is defined as containing ω3, ω4. What is the true statement in this situation?

A

The sum of the probabilities of events A and B does not necessarily add to 1.

45
Q

What is the concept that best describes the Bayesian view of probability?

A

Probability is a degree of credence on an event.

46
Q

A psychologist is interested in investigating the effect of variable A on B. She knows that there is a variable she cannot measure that affects both A and B. What is the effect of that unmeasured variable?

A

a) Selection bias
b) Confounding.
c) Collider
d) Chain

47
Q

In a Binomial distribution with parameters pi=.60 and n=10, which of the following values is more plausible?

A

a) One successful trial.
b) Seven successful trials.
c) Three successful trials.
d) Ten successful trials.

48
Q

In a normal distribution with parameters mu = 15 and sigma =5, which of the following values is more plausible?

A

a) Eighteen.
b) Seventeen
c) Ten
d) Five

49
Q

What is a sample space?

A

The set of possible outcomes of an experiment.

50
Q

What does the following expression denote: P (X=0 | Y=1)?

A

The conditional probability of X being 0, given that we know Y=1.

51
Q

In a Beta distribution with parameters a=20 and b=80, which of the following values is more plausible?

A

a) 0.
b) 0.50.
c) 0.30
d) 0.80

52
Q

Where do the values of a random variable come from?

A

From a random process.

53
Q

What is probability?

A

Frequentist view: p is the long run expected frequency of occurrence of an event… e.g. the probability of heads or tails from a toss of a coin several times.
Bayesian view: p is the degree of credence of the occurrence of an event or basically, probability can be assigned to many events and do not need repeated events.

54
Q

What is a random variable?

A

from research view: it is a variable in research with characteristics of something that can take many values.
from probability theory: the variable is an outcome of an experiment but the word “experiment” is not strictly an experiment as in science/research but any experiment, a random process of any event.

55
Q

possible events in sample space…

A

e.g.
As in the events of A, B, C, and D in a sample space of the experiment, the possible events are A, B, C, or D.
We can also create a combo event say, X=A/B or Y=C/D.
Therefore, the possible events can be the events or events based on the combo of the outcomes (e.g. A/B , C/D)

56
Q

What is the sample space of this experiment;

Let’s say event A has 4000 people, B has 3000 people, C has 2000, and D has 100.

A

e.g.

A sample space of this experiment is the set of all the possible outcomes-events A,B, C, and D.

57
Q

What are probabilities;

A

probabilities are the likelihood of events or simply stated as the probabilities of events.

e.g.
the probability of X is 0 and it is written as “P(X=0) =” or the probability of X is 0 or 1 and it is written as “P(X=0 or 1) =”

58
Q

Define a random variable for this experiment;

The random variable X is 0 if lands in A, 1 if lands in B, 2 if C, and 3 is D.

A

The random variable takes these four possible values.
Or
Random variable Q is 0 if the drone lands in A or B, P if the drone lands in C or D.

If we define random variable, we can go beyond the possible outcomes) or just outcomes.

59
Q

What is a conditional probability;

For two random variables X and Y, X=0 if lands in A, =1 if lands in B, =2 if lands in C, and =3 if lands in D.

And Y=0 if inland or Y=1 if coastal.

A

The CP is the probability of an event (Y) given that another event (X) has occurred. It is the combine event of X and Y such that X happens then Y.

60
Q

Name three discrete distributions

A

Bernoulli: Parameter pi. Values of random variable: Success (1) and Failure (0).

Binomial: parameters pi and n. Values of random variable: Number of success in n number of trials.

Categorical: parameters => one pi per category. Values of random variable: categories.

61
Q

What is the r/s of the likelihood and the prior distribution?

A

They are not related.

62
Q

A prior distribution with a spike on zero, is a typical model of…

A

The null hypothesis.

63
Q

If the BF01 is 0.001, what does this mean regarding the null hypothesis?

A

The data is much more likely under the model of the H0 than under the model of the H1.

64
Q

The BF10 comparing the H1 and the H0 is 0.05, meaning that…

A

The data is more likely under the H0 than under the H1.

65
Q

In a normal distribution where is the value most probable?

A

The central values of the distribution.

66
Q

The probability of the data under the H1 is 0.002 and that of the H0 is 0.0002. What is the value of the BF10?

A

a. 10.
b. 0.001.
c. 0.0001.
d. 1.

67
Q

What is the true statement regarding the sampling distribution in relation to the H0 and the H1, respectively?

A

There is one sampling distribution in the model of the null and infinite number of sampling distributions in the model of the alternative.

68
Q

If the BF10 is 125, what is the decision to make regarding the H0?

A

Decisions are not made in the Bayesian approach.

69
Q

What is the product of the prior probability and the likelihood proportional to?

A

The posterior distribution.

70
Q

What effect size is used in the Bayesian alternative to the t test?

A

Difference between means in a population.

71
Q

In a study, the BF10 comparing an H1 to a H0 is 0.5. What is the correct statement?

A

The data is 2x more likely under the H0 than under the H1.

Advanced Quantity Research Methods (1 decks)

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