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Research Design & Stats > IR WEEK 1 > Flashcards

IR WEEK 1 Flashcards

1
Q

describe ways of knowing

A

scientific method, deductive reasoning, inductive reasoning, trial and error, authority, tradition

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2
Q

includes deduction and induction in systematic empirical and controlled analysis

A

scientific method

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2
Q

gather facts and observations without preconceived notions, facts about a sample leads to conclusions about about a whole

A

inductive reasoning

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3
Q

start with a premise or known scientific principle, tie that premise in with other observations and make a conclusion

A

deductive reasoning

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4
Q

keep trying until something works

A

trial and error

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5
Q

turn to an expert

A

authority

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6
Q

we accept certain truths as givens

A

tradition

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7
Q

3 major parts of the research continuum

A

Descriptive, exploratory, experimental

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8
Q

the researcher attempts to describe a group of individuals on a set of variables, to document their characteristics

A

descriptive

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9
Q

researcher examines a phenomenon of interest and explores how it relates to other factors (find relationships)

A

exploratory

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10
Q

compares two or more conditions (determines cause and effect)

A

experimental

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11
Q

5 major phases of the research process

A
  1. identify the research question
  2. design the study
  3. carry out methods
  4. data analysis
  5. communication
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12
Q

enough evidence accumulates to discredit an existing theory; may cause a change in perspective

A

paradigm shift

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13
Q

List and describe the 7 parts of a primary research article

A

Abstract, Introduction, Methods, Results, Discussion, Acknowledgements, References

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14
Q

overview and purpose

A

abstract

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15
Q

statement of the problem; specific purpose and hypothesis

A

introduction

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16
Q

study design; data analysis and procedures

A

methods

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17
Q

narrative description of statistical outcomes

A

results

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18
Q

interpretation of statistical outcomes

A

discussion and conclusions

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19
Q

technical assistant; funding source

A

acknowledgments

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20
Q

all references cited in articles

A

references

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21
Q

synthesizes the data from multiple research studies and provides an argument or interpretation of the state of the field

A

review paper

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22
Q

data sets are split and published separately instead of being presented in unified way

A

salami science

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23
Q

a characteristic that can be manipulated or observed an that can take on different values, either quantitatively or qualitatively

A

variable

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24
Q

variables that might change in response to some intervention

A

dependent

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25
Q

condition, intervention or characteristic that will predict or cause a given outcome

A

independent variable

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26
Q

declarative statement that predicts relationships between independent and dependent variables in a specific population

A

hypothesis

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27
Q

alternative/research hypothesis

A

best guess answer to the research question; the expectation that is to be tested

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28
Q

null hypothesis

A

opposite of the research hypothesis; challenging the alternative hypothesis.

29
Q

non directional hypothesis

A

no indication of change, higher or lower

30
Q

directional hypothesis

A

predicting that something is either going to go higher, lower, or stay the same

31
Q

the larger group to which research results are generalized

A

population

32
Q

researcher chooses a subgroup of the population; reference group for estimating characteristics or drawing conclusions about the population

A

sample

33
Q

individuals selected for a sample over-represent or under-represent certain population attributes that relate to the study variables

A

sampling bias

34
Q

factors that preclude or prevent someone from being a subject

A

those with conditions/ diseases additional to the disease under study
extreme ages
certain disabilities or deficits that can affect how they can participate

35
Q

factors of inclusion; population that qualifies someone as a subjec

A

gender
disease state having particular treatment
only certain symptoms

36
Q

every unit has equal chance of having some of the characteristics throughout the population. The sample should be representative of the population

A

random sampling (probability sampling)

37
Q

groups should be equivalent. differences between groups have been distributed as a function of chance alone

A

random assignment

38
Q

the difference between average (statistics) and population averages (parameters); should be due to chance, not intentional error or bias

A

sampling error

39
Q

experiment requirement through which change is observed. If we observe a change in the treatment group but not the control, we can attribute the change to the treatment.

A

control group

40
Q

factors that contaminate the independent variable so that separate effects of the variable are obscured

A

extraneous variables

41
Q

not measured in the study but can have confounding effects

A

lurkers

42
Q

either the subjects are unaware of treatment type but the researcher knows or vice versa

A

single blinding

43
Q

neither subjects or investigators know the identity of treatment groups until after data are collected or analyzed

A

double blinding

44
Q

List the three essential characteristics of an experiment

A
  1. Independent variable manipulated by researcher
  2. A control group must be incorporated into the design
  3. Subjects must be randomly assigned to groups
45
Q

participants knowledge of treatment status or the investigators expectations can influence performance or reporting of outcomes

A

experimental bias

46
Q

every person in the population has an equal chance of being chosen; sample should be representative of the population

A

random sampling

47
Q

convenience sampling chosen on basis of availability. Ex: asking for volunteers by posting signs or advertisements

A

non-probability sampling

48
Q

Explain why statistics are important in research

A

Application of mathematical methods to research conclusions about the world made from observations (experiments)
Allows us to make accurate inferences from our incomplete observations

49
Q

an educated guess, a process of inferring features of a population by looking at a small sample
Address the questions of the likelihood that an observed difference between two groups could have arisen by chance

A

inferential statistics

50
Q

describe data only (does not manipulate or compare data)
Measures of central tendency
Measures of variability

A

descriptive statistics

51
Q

numbers that tend to cluster around the middle of a set of values

A

measures of central tendency

52
Q

the score that occurs most frequently in a distribution

A

mode

53
Q

the value above which there are as many scores as below it. Divides a rank-order distribution into 2 equal halves

A

median

54
Q

(X-bar) sum of a set of numbers divided by the number of scores, n

A

mean

55
Q

an observation that falls well above or well below the overall bulk of the data

A

outliers

56
Q

difference between largest value and the smallest value. Only uses the largest and smallest observations ignoring the rest of the data

A

range

57
Q

the typical value of how far the data falls from the mean, by summarizing the deviations from the mean

A

standard deviation

58
Q

Statistical inference is an educated guess, a process of inferring features of a population by looking at a small sample; addresses questions of likelihood

A

inferential statistics

59
Q

choosing of samples as representatives of the entire population

A

sampling

60
Q

the distribution obtained by computing the statistic for a large number of samples drawn from the same population

A

sample distribution

61
Q

FALSE POSITIVE when we conclude that a real difference exists, when the differences are in fact due to chance.

A

type 1 error (alpha)

62
Q

FALSE NEGATIVE when we conclude that the differences are due to chance when the samples are truly different.

calling the results not statisitically significant when they really are

A

type 2 error (beta)

63
Q

consists of arbitrary labels with no implied order; unranked; categorical

can be numbered but numbers have no significance

A

nominal data

64
Q

consists of numerical ranked data that is ranked according to some criterion; each rank is different from the others, but the differences might not be equal. Ex: pain ratings, (on a scale of 1-10)

A

ordinal data

65
Q

consists of ranked data with intervals between each other being equal but with no meaningful zero point ex: IQ scores

A

interval data

66
Q

like interval data but zero point is meaningful; highest level of measurement. Ex: most lab values, no negative values, height, weight, age

A

ratio data

67
Q

acquisition of new knowledge for its own sake, without reference to potential practical use

A

basic research

68
Q

directed toward solving immediate practical problems with functional applications and testing theories that direct practice

A

applied research

69
Q

structured process of investigating facts and theories in medicine exploring connections, improving patient care

A

clinical research

70
Q
A

Research Design & Stats (5 decks)

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