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Research Design and Statistics > Lecture 1 > Flashcards

Lecture 1 Flashcards

Introduction to Statistics

1
Q

What’s the framework for design and statistics?

A
  1. Hypothesis/question
  2. Propose a study
  3. Design the study, i.e., how we get the data
  4. Collection of data
  5. Use of statistics to test hypothesis on model of data.
  6. Examine and interpret the results
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2
Q

Variable: Infinite Number of possible values, i.e., entities get a distinct score

A. Continuous variable
B. Categorical variable

A

A. Continuous variable

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3
Q

Variable: Cannot take on all values within limits of the variable, i.e., entities are divided into distinct categories

A. Continuous variable
B. Categorical variable

A

B. Categorical variable

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4
Q

Equal intervals on the variable represent equal differences in the property being measured.

A: Interval
B: Ratio
C: Nominal
D: Ordinal

A

A: Interval

Example: Difference between reaction time

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5
Q

Variable has a clear definition of 0:0

A: Interval
B: Ratio
C: Nominal
D: Ordinal

A

B. Ratio

Example: Height

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6
Q

Variable: Two or more categories.

A: Interval
B: Ratio
C: Nominal
D: Ordinal

A

C. Nominal

Example: One person one outcome, vegetarian/vegan/nonveg

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7
Q

Variable: Categories have logical, incremental order

A: Interval
B: Ratio
C: Nominal
D: Ordinal

A

D. Ordinal

Example: Likert Scale, fail/pass/merit, ranked

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8
Q

What’s the measurement area?

A: Validity (instrument measures what it set out to measure)
B: Reliability (ability of measure to produce same results under same conditions)
C: Both A and B
D: None of the above

A

C. Both A and B

The values have to have the same meaning over time and across situations, and must have systematic issues with measurements

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9
Q

Variation: Differences in performance created by a specific experimental manipulation

A: Systematic variation
B: Unsystematic variation
C: Randomization variation
D: Other/NOTA

A

A. Systematic variation

Influencing the outcome based on a controlled experiment

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10
Q

Variation: Differences in performance created by unknown factors

A: Systematic variation
B: Unsystematic variation
C: Randomization variation
D: Other/NOTA

A

B. Unsystematic variation

It influence results, but it’s not accounted for.

Example: Age, gender, IQ

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11
Q

Variation: Minimizes unsystematic variation

A. Systematic variation
B: Unsystematic variation
C: Randomization variation
D: Other/NOTA

A

C. Randomization variation

It minimizes the impact of unsystematic variation

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12
Q

What is the independent variable?

A. Predictor variable
B. Outcome variable
C. Both A and B
D. Neither

A

A. Predictor Variable

Also referred to as Hypothesized cause, manipulated variable.

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13
Q

What is the dependent variable?

  1. Measured variable
  2. Manipulated variable
  3. Both A and B
  4. Neither
A
  1. Measured variable

Also called outcome variable, and is the proposed effect.

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14
Q

NHST

Which hypothesis predicts no effect of predictor variable on outcome variable?

  1. Null hypothesis
  2. Alternative hypothesis
  3. Both 1 and 2
  4. Neither
A
  1. Null hypothesis
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15
Q

NHST

Which hypothis says that there is an effect of the predictor variable on outcome variable?

  1. Null hypothesis
  2. Alternative hypothesis
  3. Both 1 and 2
  4. Neither
A
  1. Alternative hypothesis
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16
Q

NHST

What does NHST compute?
1. Probablity of null hypothesis being true
2. Probability of alternative hypothesis being true
3. Probablity of null hypothesis being false
4. None of the above

A
  1. Probablity of null hypothesis being true

Also called P-value

17
Q

NHST

Which of the following is true?
1. Significant result does not mean effect is important.
2. Non significant result does not mean null hypothesis is true
3. Significant result doesn’t mean that the null hypothesis is false.
4. All of the above

A
  1. All of the above
18
Q

NHST

Which of these refers to hypothesizing after the results are known?
1. p-hacking
2. harking
3. other

A
  1. Harking
19
Q

NHST

What is p-hacking?

A

Selective reporting of significant results

20
Q

EMBERS

What is E?

A

Effect size
It is the standardized measure of how different the means are.

Example: Cohen’s d

21
Q

EMBERS

M - ?

A

Meta analysis
Rule of thumb for effect sizes
Funnel plots: value studies by their sample size and observe bias.

22
Q

EMBeRS

What is BeRS?

A

Baynesian approach, Registration, Sense

23
Q

Distribution

What is a normal distribution?

A
  • Bell curve
  • Symmetrical
  • Two parameters
  • Central tendency (mean)
  • Standard deviation (dispersion)
24
Q

Symmetry and Skew

If mean = median = mode, then:
1. Symmetrical
2. Positive skew
3. Negative skew

A
  1. Symmetrical

Skew is between -1 and 1

25
Q

Symmetry and Skew

Is mean > median > mode, then
1. Symmetrical distribution
2. Positive skew
3. Negative ske

A
  1. Positive skew

x > +1

26
Q

Symmetry and Skew

Is mode > median > mean, then
1. Symmetrical distribution
2. Positive skew
3. Negative skew

A
  1. Negative skew

x < -1

27
Q

Kurtosis

What does kurtosis look at?

  1. Skewness
  2. Shape of graph
  3. Length of graph
  4. Direction of graph
A
  1. Shape of graph

Bulge or bend.
If within +2 and -2, its all good!

28
Q

Kurtosis

Which is leptokurtosis?
1. k > 0
2. k < 0
3. k > +2
4. k < -2

A
  1. k > 0
29
Q

Kurtosis

Which is platokurtosis?
1. k > 0
2. k < 0
3. k > +2
4. k < -2

A
  1. k < 0
30
Q

Tortium Quid/Third factor

Which is NOT the assumptions of tortium quid do?
1. It helps infer causality
2. It has randomized controlled trials
3. It acts as a confounding variable/third factor
4. All of the above

A
  1. All of the above

It creates control settings to see how the 3rd controlled variable affects the data

Research Design and Statistics (7 decks)

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