Week 2 Flashcards
1
Q
Data Types - Nominal Data
A
- Uses numbers as labels
- Denotes group membership
- Can be called Categorical Data
- Does not reflect magnitude or order in any way
E.g. gender: 1 = woman, 2 = man, 3 = non-binary, 4 = another gender
2
Q
Nominal date
A
- Common in psychological research
- Often used in experimental design
- Can be presented as frequency or percentage
3
Q
Data Types Ordinal Data
A
- Also called Rank Data
- Used to rank order, so has magnitude
- Does not tell us how different the rankings are
e.g. positions in a race 1st, 2nd, 3rd but not individual times - cannot use ordinal data to make inferences
4
Q
Data Types - Interval Data
A
- Also called Continuous Data
- Does not have a real Zero Point
- No way of assessing absence of a construct this way
e.g. Although Celcius temperature has a zero value it can continue beyond into negative figures. Temperature gets colder, it does not dissappear
5
Q
Zero Point
A
- Point on a scale that denotes zero
- From here, positive and negative readings can be made
- The point from which progress can be charted.
6
Q
Data Types - Ratio Data
A
- Also called continuous Data
- has a highest level of measurement
- Similar to interval data equal in scale
But in addition to order intervals might be different i.e. times in a race - Has equal magnitude and an Absolute Zero
e.g. weight data, bunch of apples equals a kilo, as we remove apples weight goes down but when last apple is removed we get zero
7
Q
Types of Data
A
- Nominal Data
- Ordinal Data
- Interval Data
- Ratio Data
Different statistical tests such as test of Correlation has miminum requirement for Interval Data.
Many psychological measures area hybrid of Interval and Ratio Data
8
Q
Correlation
A
- Correlation is key when looking at measurement
- Basis for reliability, validity, factor analysis
- Allows for assessment of individuals
- Individual variations are important factors of biological and social beings
9
Q
Correlational Psychologists
A
- their goal is to predict variation within a treatement
- demand uniform treatment for each case
10
Q
Explain Within Groups Variability
A
- Look for an association with the within groups variability of another variable
- Scatter Plot charts with x & y axis can measure two variables
e.g. marks on a test within the group of a classroom
11
Q
Quantify Relationships Between Variables
A
- Measures magnititude (strength) and direction (Positive or negative)
- Is it a strong or weak relationship
- Are there other variables at work
- The more the dots look like a line the stronger the relationship.
e.g. income vs Happiness
12
Q
Scatterplots
A
Linearity can described in two ways:
* Magnitude.
- i.e., strength.
* Direction.
- Positive or negative.
13
Q
Null Hypothesis Significance Test
A
- Also NHST
- Tests if sampling error is responsible for correlation
- Tied to sample size - the bigger sample the better likleyhood that difference is significant
- How likely is our result if the variables are unrelated in the real world
14
Q
Effect Size
A
- How practically meaningful a result is is
- regardless of its significance
15
Q
NHST - Formula
A
- H0: r = 0
- Variables are independent.
- r > 0 is due to sample error.
- H1: r ≠ 0 (two tailed); r > 1 or < 1 (one tailed).
- Variables are correlated
16
Q
Cohen’s Effect Size
A
Cohen’s (1988) Conventions for Psychological Research:
1. Large: > .50
2. Moderate: Approximately .30
3. Small: Approximately .10
Look at magnitude/size of the correlation
* Anything above 0.05 is a large correlation or effect
* Around 0.32 is moderate correlation
* between 0.12 and .3 is a small correlation
17
Q
Power Tables
A
- Important to consider when evaluating effects
- Need to assess if your measure has been properly tested
- This measures your effect based on sample size.
- The smaller the anticipated effect the larger the sample size you require
18
Q
Reliability Coefficient
A
- Must not apply Power Tables too rigidly
- r = .50 would be a poor measure
- Would not use the scale
- Correlation Coefficients need to be put into context
19
Q
Rosenthal 1995 - Correlation Coefficient
A
- Physicians identify most important medical breakthrough.
- Most popular was drugg cyclosporine- reduces organ donation rejection
- The relationship between patient survival and use of drug showed r = .15.
- This small correlation actually saved thousands of lives
- Needs to be interpreted in context
20
Q
Factors Affecting Magnitude of Correlation
A
- Reliability of Measures
- Outliers
- Restriction of Range
21
Q
Reliability of Measures
A
- The extent of a measure producing stable & consistent scores over time
- Not influenced by random errors
- Unreliable measures reduce the size of the correlation
22
Q
Non Linearity
A
where there is not a straight-line or direct relationship between variables
23
Q
Restriction of Range
A
- Variable has less variability in a sample than in the full population.
- Restricted range can be present for observed variables or in other variables that were not measured.
- Scores outside of a cenratin range can change the size of the effect
24
Q
Pearson’s R
A
- Most common statistic for correlation
- Indexes the degree of linearity only
- Not related to the slope of the line. if there is one.
- Measure of the linear relationship between two variables
- r = 0 Not necessarily absence of relationship
25
Q
Pearson’s R Magnitude
A
- Should not be confused with direction
- +.05 is the same magnitude as -.05
26
Q
Bivariate Correlation
A
- Determine the existence of relationships between two different variables
- Correlation does not equal Causation
- Two correlated variables does not mean they cause one another
- Global Warming does not decrease the number of pirates
27
Q
Causation
A
For causation we need:
* Covariation
* Temporal Ordering _ arrangement of events in time
* No Confounding - another variable that affects covariation
28
Q
Covariation
A
Bivariate Correlation can only tell you about covariation
29
Q
Reasons for a Correlational Relationship
A
- A direct causal relatioship exists
- Not possible to establish through correlation alone
- Non Causal Associations
- Reverse Causation
- Spuriousness - A covariation that appears to be causal but is not
- Measurement Association - Complex causal associations
- Indirect Causation - Mediation
- Interactive Causation - Moderation
30
Q
Normal Distribution
A
- The way scientists assume the majority of distributions look like
- Assuming normal distribution allows more complex analysis and inferences
- Mean, Median and Mode are all the same
- If actual scores are too different from normal then inferences become flawed
- Always check for normal distribution before inferring
31
Q
Non Normal Distributions
A
- Postive and Negative Skew have longer tails to the left or right
- Mean mode & Median
32
Q
Standardisation Groups
A
- When we have one score we compare it to the other scores in the group
- Single scores are not really useful
- This relies on large groups to strengthen normality
- Standardisation could be specific or general
33
Q
Measuring Physical Characteristics
A
- So long as the measurements are accurate will give the same true measurement
- Like a ruler with mm cm metres
- Not arbitrary or guessing
34
Q
Measuring Psychological Responses
A
- Cannot be directly observed
- Inferred from things we can observe
- Actions
- Behaviour
- Responses to self report scales
35
Q
Test Scores
A
- Usually expressed by numbers
- Used to describe and draw conclusions
- Make inferences based on their outcome
36
Q
Measurement
A
- Assigning numbers or symbols to characteristics
- Dictated by rules that represent magnitude
37
Q
Measurement
A
- Assigning numbers or symbols to characteristics
- Dictated by rules that represent magnitude
38
Q
Scale
A
- A set of numbers with the same properties
- Model empirical properties the objects that they are assigned
e.g.inches and centimeters
39
Q
Sample Space
A
The values that a variable can take on.
e.g. Gender: male, female, nonbinary
or
e.g. Age in Years: 0, 1, 2 etc
40
Q
Discrete Scale
A
- A sample that can be counted
- Numbers between spaces are not allowed
e.g. cannot have 3.4 vists to hospital
41
Q
Categorical Scale
A
Divides sample into categories
e.g. Freshman, Sophomore, Junior, Senior
42
Q
Quantitative Scale
A
Discrete scales because they can be countable
Can have numbers between spaces
e.g. 3.45 kgs of flour
43
Q
Continuous Scale
A
- Values can be any real number in a scale.
- Can have fractions and decimals
- In theory can have irrational numbers
- Best to round these to certain decimal places to make them easier to work with
- Usually always contain errors
44
Q
Error
A
- Refers to collective influence of factors beyond those specific to measure
- There can be many sources of error
- Usually connected to uncertainty of measuring rather than mistakes
e.g. decimals are thought of as Real Numbers but a score of 25 might actually be 24.98793 or 25.2134
45
Q
NOIR
A
N - Nominal Scale
O - Ordinal Scale
I - Interval Scale
R - Rational Scale
46
Q
Nominal Scales
A
- Simplest form of Measurement
- Involve Classification or Categorisation of distinguishing features
- Mutually exlusive categories
- Demongraphic factors can be categorised so are nominal
- Indicate quantities but can be Unique Identifiers
- Not always quantified
47
Q
Nominal Scales
A
- Simplest form of Measurement
- Involve Classification or Categorisation of distinguishing features
- Mutually exlusive categories
- Demongraphic factors can be categorised so are nominal
- Indicate quantities but can be Unique Identifiers
- Not always quantified
48
Q
Ordinal Scales
A
- Assign people to categories
- Have clear distinct order
e.g. how often do you show thrill seeking behaviour never, sometimes, often
or
e.g. I am a thrill seeker Strongly agree, agree, disagree, Strongly disagree - rankings and percentiles are ordinal
- Alfred Binet developed Intellegence Tests said they were ordinal as they both measure and classify people by intelligence
49
Q
Rokeach Value Survey
A
- An Ordinal Scale that lists personal values
e.g 1. Empathy - most important
2. Care - second most important
3. Respect - Third most important - Values are put in order according to importance 1 - 10
- It is assumed there is some importance of each values
- Values have no Absolute Zero - this creates issues
e.g you cannot rank average 1. Empathy importance and 3. Respect Importance to acheive 2. Care importance
50
Q
Interval Scales
A
- Have similar features to nominal and ordinal
- Have meaningful distances between each unit - A ruler
- Each unit is the same as the others - mm, cm, metre
51
Q
Absolute Zero
A
- indicates absence of the unit being measured
e.g. 20 apples minus 20 apples = 0 apples
52
Q
Ratio Scales
A
- Ratio Scales have true Absolute Zero
- Countable quantity that indicates absence of the unit
- represent the magnitude of the unit being measured
- Can be compared as proportions like 25 kilos is half of 50 kilos
- In psychology can be used in neurological functioning
53
Q
Measurement Scales in Psychology
A
- Ordinal measurement is used most in psychology
- Tests that indicate the amount of intelligence, aptitude or traits etc
- Data is measured and converted to make them manageable or understandable
- Use graphs or tables to demonstrate ordinal and nominal level data
54
Q
Frequency Distribution
A
- Scores are listed alongside the number of times each score occured
- Often refferd as Simple Frequency Distribution indicates data has not been grouped
- Is simple to read and usually represent actual test scores
- Grouped Frequency Distribution is used to summarise data and uses class intervals
- This summary may result in lack of detail
55
Q
Ways to Display Frequency Data
A
- Graphs
- Histograms
- Bar Graphs
- Frequency Polygons
56
Q
Measure of Central Tendency
A
- Indicates average or middle point between extreme
- Can be defined in different ways
- Usually defined by ** Arithmetic Mean** or the average
- Mean might not be used if there are extreme scores
- Could also use the Median and the Mode
57
Q
What are Interviews in Psychology
A
- A tool in the biopsychosocial assessment kit
- Utilised in research & practice
- Ways of collecting information
58
Q
How is data collected in psychology
A
- Interviews
- Questionnaires
- Survyers
- These are supplemental, often working hand in hand
59
Q
Intake Interview
A
- Used for Clinical Purposes to find out where someone is at and building rapport
- Typically covers
* Demographic data
* Reason for referral
* Past medical history
* Present medical condition
* Familial medical history
* Past psychological history
* Past history with medical or psychological professionals
* Current psychological conditions
60
Q
Mental Status Examination (MSE)
A
- Can be an Intake interview but can be separate
- Describes appearance, behaviour, consciousness and alertness, motor/speech, mood and affect, thoughts and attitudes and cognition
- Alertness, language, memory, constructional ability, and abstract reasoning are the most clinically relevant
- Also can be used to monitor progress over time
61
Q
Different Types of Interviews
A
- Structure
- Unstructured
- Semi-Structured
62
Q
Structured Interview
A
- Questions prepared in advance
- Mainly Closed Questions
- Read out exactly as written
- Schedule for Affective Disorders and Schizophrenia (SADS)
63
Q
Schedule for Affective Disorders and Schizophrenia (SADS)
A
Detects symptoms of schizophrenia, major depression, bipolar and anxiety
64
Q
Unstructured Interview
A
- No specific predetermined
- May have certain topics in mind
- Guided conversation or discovery interview
- Questions can change as we go
- Ask follow up questions
65
Q
Semi Structured Interview
A
- Some structure and some flexibility
- Combination of open & closed questions
- Structured Clinincal Interview for DSM-5 (SCID)
