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PSYC3112 - Individual Differences > Week 2 > Flashcards

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
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2
Q

Nominal date

A
  • Common in psychological research
  • Often used in experimental design
  • Can be presented as frequency or percentage
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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
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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
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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.
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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
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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

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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
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9
Q

Correlational Psychologists

A
  • their goal is to predict variation within a treatement
  • demand uniform treatment for each case
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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
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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
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12
Q

Scatterplots

A

Linearity can described in two ways:
* Magnitude.
- i.e., strength.
* Direction.
- Positive or negative.

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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
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14
Q

Effect Size

A
  • How practically meaningful a result is is
  • regardless of its significance
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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
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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

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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
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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
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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
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20
Q

Factors Affecting Magnitude of Correlation

A
  1. Reliability of Measures
  2. Outliers
  3. Restriction of Range
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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
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22
Q

Non Linearity

A

where there is not a straight-line or direct relationship between variables

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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
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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
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25
Q

Pearson’s R Magnitude

A
  • Should not be confused with direction
  • +.05 is the same magnitude as -.05
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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
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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)

PSYC3112 - Individual Differences (10 decks)

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