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EBM Summer 2020 > Lecture 11 > Flashcards

Lecture 11 Flashcards

1
Q

Baseline groups

A

Comparison groups should be similar in terms of baseline characteristics.
Age, gender, ethnicity, variables of interest

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2
Q

Selecting a specific statistical test to perform requires some basic considerations about:

A

Type of
variable (independent vs dependent)
data (continuous vs discrete)
distribution (Normal/gaussian vs skewed)

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3
Q

Define variables

A

Representation of measures in an analysis

Can either be independent (the intervention) or dependent (the response)

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4
Q

Independent variable

A

The intervention. Defines the condition under which the dependent variable is measured. Experiementer controlled.
Independent variable affects change in the dependent variable.

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5
Q

The dependent variable

A

Response. Outcome or endpoint being measured or tested.

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6
Q

Continuous/infinite data

A

Infinite number of equally spaced values are possible. Measured. Cant count how many water drops are coming out of a hose, must measure it in L

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7
Q

Discrete/limited data

A

Limited number of values possible within the range of measurement. Counted, like single drops coming out of a faucet.

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8
Q

Defining data by scales

A

Ratio/interval scales
Ordinal scales
Nominal scales

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9
Q

Ratio or interval scales

A

Uniform intervals between consecutive measurements. Used to measure continuous data. Ex: 1 Km.

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10
Q

Ordinal scales

A

Rank of specific order where the interval values may not be known or constant. used to measure discrete data.
Ex: Leikert scale. Hot, hotter, hottest

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11
Q

Continuous data is measured by ____

A

Ratio/interval scales

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12
Q

Discrete data is measured by ____

A

Ordinal scales

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13
Q

Nominal scales

A

Used when data cannot be ordered, but values are discrete. Ex: age, race, gender. Named data.

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14
Q

1 dependent variable + no independent variable is what kind of analysis

A

Univariate analysis. Does not explain any relationship. Summarize, describe, look for patterns, normal distribution. NOT explanatory.

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15
Q

1 dependent variable + 1 independent variable is what kind of analysis

A

Bivariate analysis

Two variables. Compare.

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16
Q

1 dependent variable + 2 independent variables is what kind of analysis

A

Multivariate analysis

More than two variables. Evaluate relative importance.

17
Q

Examples of univariate analysis

A

Mean, median, mode, range, variance/dispersion, standard deviation, bar charts, histogram, pie chart

18
Q

Relationships evaluated during a bivariate analysis

A

Presence, strength, significant, descriptive or inferential.

Independent on X, Dependent on Y

19
Q

What can you evaluate during a multivariate analysis

A

Relative importance.
Regression with a dependent variable: Linear (continuous) or logistic (dichotomous). Can model and form predictions. Strong statistical test.

Correlation. When no dependent variable: Relationship or connection. Not as strong as regression.

20
Q

Difference between regression and correlation (both multivariate analysis)

A

Regression has a dependent variable. Strong statistical test.

Correlation does not have a dependent variable. Weaker statistical test.

21
Q

n vs N

A

n samples

N entire population

22
Q

Distribution of sample population measures

A

Central tendency.

23
Q

Normal/gaussian distribution

A

Symmetrical, bell shaped distribution of values where the mean represents the highest point of the curve and whose spread is defined by the standard deviation.

24
Q

Gaussian/normal curve.

__ represents the highest point of the curve
Spread is defined by ____

A

Mean, SD

25
Q

Skewed distribution

A

Asymmetrical distribution of values. Look at tail to determine

Tail to the left = negative skew
Tail to the right = positive skew

26
Q

How can you transform skewed values into a normal distribution

A

Taking the log, square root of the original value.

27
Q

Types of quantitative tests

A
  1. Parametric.
    Assume: same population normally distributed.
    Allows comparisons between groups, typically based on sample means.
  2. Non parametric.
    Sample populations skewed. Comparisons typically made on sample medians.
28
Q

What is a parametric test. What does it assume and what does it allow comparisons between

A

A type of quantitative test.
Assume: same population normally distributed.
Allows comparisons between groups, typically based on sample means.

29
Q

What is a non parametric test. Comparisons are made based on

A

A type of quantitative test.

Sample populations skewed. Comparisons typically made on sample medians.

30
Q

What is the first step after collecting data

A

Analyze data for a normal distribution.

31
Q

Statistical test for sample sizes less than 50

A

Shapiro-Wilk

32
Q

Statistical test for sample sizes more than 50

A

Kolmogorov smirnov

33
Q

What does it mean about your data distribution if the P value is less than 0.05

A

It is not normally distributed

34
Q

Do you use parametric or nonparametric tests to measure proportions?

A

Non parametric only. Chi square and McNemar’s tests. No parametric equivalent.

35
Q

Do you use parametric or nonparametric tests to measure predictions?

A

Parametric tests only. Regression and multiple regression by least squares method. No non-parametric equivalent.

36
Q

Why are parametric tests favored over non-parametric test?

A

The golden egg is to predict. You can only predict with parametric tests of regression.

37
Q

Least squares

A

Type of parametric test. Algebraic procedure for fitting linear equations to data. Grew out of astronomy.

38
Q

Goal of least squares

A

Minimize error of estimation

39
Q

Why are parametric tests preferred over non-parametric tests

A

Parametric stats are considered more powerful
Continuous outcomes are generally more specific than categorical
Greater confidence is present for normally distributed data
Can generate predictions

EBM Summer 2020 (14 decks)

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