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Stats 1 > Stats > Flashcards

Stats Flashcards

1
Q

What should a conceptual definition have?

A

1) key characteristics of the concept
2) empirical referents to which the concept refers
3) level of abstraction at which the concept is operating (how general or specific a concept is)

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2
Q

What is the operational definition?

A

statement that describes how a concept will be measured

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3
Q

What is a variable/indicator?

A

a set of observations that results form applying the operational definition

concept+error

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4
Q

Which two errors happen in measurement

A

Systematic Error (Validity)
Random Error (Reliability)

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5
Q

Where does the systematic errror come from?

A

From influences other than the desired concept

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6
Q

Where does the random error come from?

A

From non-sysematic influences on the observed measure

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7
Q

What does the random error imply?

A

Implies that if another researcher used the same measurement
technique, they would not get the same result - this makes the
measure unreliable.

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8
Q

What happens if a systemati error occurs?

A

If the measure is capturing other influences, it will systematically
over- or underestimate the true concept

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9
Q

What are the two types of validity?

A

Face Validity - on first glance it appears to be valid

construct validity - correlates strongly with other measures that are accepted as valid, comes from the concepts relationshi with other measures

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10
Q

Example for concept, conceptual definition, operationalization

A

Concept - religiosity

Conceptual definition - degree to which an individual adheres to the tenets of their religion

Operationalization - Survey individuals on their relgiosity

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11
Q

What are the major challenges to reliability?

A

Subjectivity - measurement relies on the judgment of the measurer

Lack of precision - Too much uncertainty to replicate

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12
Q

What are the three different levels of measurement?

A

Nominal - categories with no order

Ordinal - categories with order

interval - numerical values

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13
Q

What does validity in measurement mean?

A

Whether one is over or underestimating the concept?

NOT THE SAME AS VALIDITY IN RESEARCH DESIGN

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14
Q

What are the two types of research design?

A

Exploratory - form theories ad identify variables and relationships that need to be tested separately

Confirmatory - hypothesis testing

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15
Q

What are the two types of variable?

A

Dependent variable - what you are trying to explain

Independent variable - what explains or impacts the dependent variabl

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16
Q

What is a hypothesis?

A

An explicit statement to be tested or examined wth actual data. Mostly about the expected relationship between DV and IV

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17
Q

What are the characteristics of a Hypotheses?

A

1) should be as specific as possible
2) clear direction of effect
3) must be falsifiable
4) must be empirically testable

18
Q

What is a theory?

A

A set of propositions - some of which are testable as hypotheses - intended to explain an outcome

19
Q

What is an assumption?

A

A proposition in a theory that is not testable.

20
Q

What is the simplified, testable form of a theory called?

A

Model

21
Q

What is the problem with causality?

A

It is impossible to provem, but can be argued

22
Q

What is necessary to convincingly argue for causality?

A

Time order - Cause must precede effect

Covariation - Changes in the IV must be associated with changes in the DV

Non-spuriousness - relationship should not be driven by a third variable (or by time)

Theoretical consistency - A theoretical arguments for the causal relationship.

23
Q

What is the best was attain non-spuriousness?

A

Experiments

24
Q

What is HARKing?

A

The practice of hypothesising after the result are known –> NOT GOOD

  • write down your hypthesis before
25
Q

What is a research design?

A

Set of procedures for testing a hypthesis –> i.e. determining the effects of the IV on the DV

26
Q

What is an “effect”?

A

Dependent variable before –> change in IV happens –> Dependent variable after

27
Q

What are the broad types of research designs?

A

True Experiements - treatment and control group; variables are measure before and after; researcher controls the environment

Quasi-Experiments - Researcher does not control the environment, obervational designs; natural experiments

28
Q

What are the two types of validity for research designs?

A

Internal Validity - Within the study, are alternative explanations possible? Confounding variables

External Validity - Beyond the study, to what etent can the reults be inferred to hold true?

29
Q

How do the types of research design perform in terms of validity?

A

True experiments - High internal, low external

Obervational/Quasi-Experiments - Low internal, high external

30
Q

What are common threats to internal validity?

A

Omitted variables (Spuriousness)

Regression to the mean: Observations with extreme scores will tend to display lower scores next time when sample is homogenous.

Non-random sample selection: Difference in comparison groups is not due to treatment, but to the fact that the groups were different from the start.
▶ e.g.: Selecting on the dependent variable

31
Q

What are the common threats to external validity?

A

Selection - Groups not representative of the larger population (not randomized)

Out of Sample Extrapolation - setting is not within bounds of independent variable
i.e. study administers 20 mg dosages while clinicians in real life always administer 100

32
Q

What are the two types of statistics?

A

Descriptive - describe a sample

Inferential statistics - draw inferences to a larger population

33
Q

What is a distribution?

A

the way in which observations are
spread over possible values

34
Q

What are the main features of a histogram?

A
  • visualises the distributiion of on numerical varable
  • bins are a range of observations, depending on the size of the bins
35
Q

What are the measures of central tedency?

A

Mode - most frequent value

Median - the middle value

Mean - “average” value

36
Q

What measures apply to which level of measurement?

A

▶ Nominal – Mode
▶ Ordinal – Mode & Median
▶ Interval – Mode, Median & Mean

37
Q

What is the formular for variance?

A

Variance of a sample is given by the formula:
s2 = (X(i) - Mean) / n-1

38
Q

What is the standard deviation?

A

Square root of variance;

small std dev. –> data points tend to be very close to the mean

large std. dev. –> greater dispersion and indicates that data points are spread out ove a wide range

39
Q

When is something skewed in which direction?

A

mean higher than mode/median - right-skewed

mean lower than mode/median - left skewed

39
Q

What do you do with too many extreme values?

A

Log it –> only for positively skewed distributions

or recode it into ordinal categories

40
Q
A

Stats 1 (1 decks)

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