RMS Interim 1 > RMS 1 > Flashcards

RMS 1 Flashcards

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1
Q

Evidence-based-treatments

A

Therapies that are supported by research

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2
Q

Producer of research

A

Study things, analyze data, present

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3
Q

Consumer of research

A

Reading about research so they can apply it to their work, hobbies, life

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4
Q

Gaining knowledge method: Experience

A

Gaining knowledge on the basis of experience

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5
Q

Good story bias

A

We believe good stories

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6
Q

Availability heuristic

A

People put more weight on information that comes to mind easily

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7
Q

Present/present bias

A

People incorrectly estimate the relationship between the event and outcome. They only focus on the times the events were together, not when they were not?

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8
Q

Confirmation bias

A

Focusing on information that agrees with your view

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9
Q

Bias blind spot

A

Tendency to think you are less likely ton engage in bias than others

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10
Q

Gaining knowledge method: authority

A

Accept something because authority said so

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11
Q

Induction

A

Specific to general. Observation to theory. Coming up with a theory to explain your observation

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12
Q

Deduction

A

General to specific. Theory to hypothesis. Deriving a prediction that follows from your theory by means of a hypothesis

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13
Q

Hypothesis

A

An answer to your research question derived from your theory

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14
Q

Prediction

A

A specific event that will occur if your hypothesis is true. Prediction about new events

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15
Q

Theory

A

A set of statements that describe general principles about how variables relate to one another

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16
Q

Operationalize

A

Determine how the conceptual variables in the hypothesis are measured or manipulated

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17
Q

Falsifiability

A

Possible to observe something that contradicts the theory –> good theory is falsifiable

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18
Q

Variables

A

Vary across people or time

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19
Q

Reliability

A

How consistent are the measures

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20
Q

Validity

A

Does the instrument measure what it’s supposed to measure

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21
Q

Test-retest reliability

A

The participant gives the same score each time when measured

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22
Q

Interrater reliability

A

The scores are the same, no matter who measures the variable

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23
Q

Internal reliability

A

The participant gives a consistent answer pattern no matter how the questions are phrased

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24
Q

Face validity

A

Is it subjectively considered to be a plausible operationalisation of the conceptual variable

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25
Content validity
The measure captures all parts of a defined construct
26
Criterion validity
The measure is associated with a concrete behavioral outcome that it should be associated with
27
Convergent validity
The extent to which a self-report measure correlates with other measures of a theoretically similar construct
28
Discriminant validity
The measure should not correlate with measures of different constructs
29
Construct validity
Does a test measure what it's supposed to measure
30
Empirical cycle steps (start with observation)
Observation - Induction - Theory - Deduction - Prediction - Testing - Results - Evaluation
31
Statistics meaning
The art and science of learning from data
32
Three components statistics and their meaning
Experimental design: stating the goal and planning how to get the data Descriptive statistics: summarising/analyzing the data and seeing if there are patterns Inferential statistics: making decisions and predictions based on the data
33
Population
All scores/data we are interested in
34
Sample
The part of the population we have actually observed
35
Inference
Drawing conclusions about the population, based on the sample
36
Margin of error
The measure of the expected variability from one random sample to the next random sample
37
Statistically significant
Difference between results is so large that it would be rare to see such a difference by ordinary random variation
38
Categorical variable
Each observation belongs to a category (color)
39
Quantitative variable
Each observation has a numerical value representing a magnitude (length)
40
Discrete
Every observation is one of a specific set of values (0,1,2)
41
Continuous
Every observation comes from a range, it has an infinite region of values, (length)
42
Pareto chart
Bar chart with the categories ordered by their frequency
43
Pareto principle
Small subset of the categories often contains most of the observations
44
Bar graph vs Histogram. For what different variables are they
Bar graph: categorical variable Histogram: quantitative variable
45
A statistic
A numerical summary of the data
46
A parameter
A numerical summary of the population
47
Interpretation of the standard deviation
A typical distance of an observation from the mean
48
Association
A particular value of one variable is more likely to occur with certain values of the other variable
49
Two variable types:
Response variable: the variable for which you want to explain/predict the outcome (dependent) Explanatory variable: the variable that you use to predict (independent)
50
Law of large numbers
If the number of trials increases, the proportion of occurrences of any outcome approaches a given number (only for independent trials)
51
Probability
The proportion of times that a random phenomenon occurs in the long run over independent trials
52
Gamblers fallacy
The mistaken belief that, if an event occurs more frequently than expected in previous trials, it is less likely to occur in next trials (or vice versa)
53
Independent trials
The outcome of a trial is not affected by the outcome of another trial e.g. tossing a coin
54
Independent events
Knowing one event does not tell you anything about the other event e.g. wearing glasses - liking chocolate
55
Sample space
The set of all possible outcomes i.e. a particular outcome or a group of possible outcomes e.g. throw with a die (6)
56
Event
Subset of a sample space. An outcome or a group of possible outcomes (e.g. you throw six with a die = 6, you throw an even number with a die = 2,4,6, a randomly selected student is born in Berlin = Berlin
57
Complement of A
Everything but A
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Disjoint
The two events do not overlap (can't be born in the Netherlands and Germany)
59
Intersection
You have both (A AND B): P(blond AND glasses)
60
Union
You have one or the other (A OR B): P(blond OR glasses)
61
Sensitivity
The probability of a positive outcome, given that you have the disease
62
Specificity
The probability of a negative outcome, given that you don't have the disease
63
Conditional proportion
The proportion of the response variable, for one level of the explanatory variable
64
Independence
The occurrence of event B does not change the probability of event A occurring
65
Statistics
The art and science of learning from data
66
Variable
Any characteristic observed in a study
67
Median
Middle number
68
Mean
Average
69
Outlier
An observation that falls well below or above the overall bulk of the data
70
Resistant
A numerical summary of the observations is resistant if extreme values have little, or no influence on its value
71
Mode
The value that occurs the most frequently
72
Range
Difference between the largest and smallest observations
73
Whisker
Show the stretch of the rest of the data, except for potential outliers (these are shown separately)
74
Deviation
The difference between the observation and sample mean
75
Pth percentile
P percent of the observations fall below or at that value
76
Z-score
The number of standard deviations it falls from the mean
77
Dependence
The occurrence of event B changes the probability of event A occurring
78
Conditional probability
Chance of an event, when you know that another event has already occurred. e.g probability of your pet wanting to cuddle when it wags its tail
79
Empiricism
Use verifiable evidence as the basis for conclusions
80
Ordinal scale
The numeral scales of a quantitative variable represent a ranked order
81
Interval scale
The numerals represent equal distances between levels and there is no true zero (0 doesn't mean nothing)
82
Ratio scale
The numerals have equal intervals and 0 truly means nothing (0 answers correct)
83
Known-groups-paradigm
Researchers see if the scores differ from a group whose behavior is already known
84
Association
Particular variables for one variable are more likely to occur with certain values of the other variable
85
Conditional proportion
Refer to a particular row or column on the contingency table (so conditional on one thing)
86
Marginal proportion
Refer to the sum of a row or column on the contingency table. So the total of one row or collumn, compared to the overall total of everything
87
A scatterplot is a graphical display for two ... variables
Quantitative
88
Correlation
Summarizes the strength and direction of the linear association between two quantitative variables
89
Testing
The process of verifying your prediction
90
Variance
Average of the squared deviations
91
For analysing the association between two quantitative variables, we use the
Correlation coefficient
92
For analysing the association between two categorical variables, we use the
Contingency table
93
Base rate neglect
People ignore that P(D/Pos) and P(Pos/D) are different concepts. They differ strongly if the prevalence (base rate) is low. Death if aliens invade earth - aliens invade earth when you die
94
Probablistic
Findings do not explain all the cases all of the time
95
Confound
An alternative variable that gives an alternative explanation for the relation between x and y
96
Phenomena
Any observable occurrences
97
Law of large numbers
With more trials in a random experiment, the average of the outcomes will go to a true expected value or average
RMS Interim 1
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