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Evidence Based Approach > T3 Slide W5 > Flashcards

T3 Slide W5 Flashcards

1
Q

The Measurement Process

A
  • What is the point of research if it can’t be measured
  • Measurement is the assignment of values to outcomes
  • How do we measure height?
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2
Q

Principles of measurement in Research - 3 ideas

A
  1. an outcome variable belongs to one of four levels of measurement (Nominal, Ordinal, Interval, and Ratio)
  2. The qualities of one level, are also characteristic of the next level
    • e.g., ratio measures such as height also capture ordinal information
  3. The higher the level, the more precise the measurement process, and closer you will be to the true outcome of interest.
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3
Q

Levels of Measurement

A
  • The relationship between what is being measured and the numbers that represent what is being measured
  • Every variable must be operationally defined:
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4
Q

Variables are Categorical or Continuous

A
  • Categorical
    • Names are distinct entities
    • Simplest form is binary variable; can only go in one of two categories. eg male v female
    • Continuous Variable
  • Can take on any value on the measurement scale. eg: time on a stopwatch
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5
Q

Levels of measurment in order of complexity

A
  • Nominal
  • Ordinal
  • Interval
  • Ratio
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6
Q

Nominal Variable

A
  • Nomin = name
  • Differ in quality rather than quantity
  • Characterises observations in a manner where they can only be placed in one category eg: eye colour
  • May be given names or numbers but these have no intrinsic value. such as with NRL Jerseys
  • Most IV’s are nominal
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7
Q

Ordinal Variable

A
  • Like nominal they permit classification tell us the order in which things have occurred
  • Ordinal scales have no absolute zero point. ie: Horse racing
  • Imply nothing about how much greater one ranking is than another
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8
Q

Interval Variable

A
  • Equal intervals on the scale represent equal differences in the value measured
    • eg: temperature, although equal, be sure to consider interpretation of values along the scale.
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9
Q

Ratio Variables

A
  • Ratio meaning calculation
  • Build on interval but also requires the ratios of values are meaningful
  • Requires a true zero point not an arbitrary one
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10
Q

Continuous variables are continuous or discrete

A
  • Continuous = any level of precision such as time
  • Discrete = certain defined values such as number of children in a family
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11
Q

Categorical - Distinct Category

A
  1. Nominal Variable - more than two
  2. Ordinal variable - Same as nominal but a logical order ie: fail, pass, credit, distinction, high distinction.
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12
Q

Continuous - Distinct Score

A
  1. Interval variable - equal entities represent equal difference
  2. Ratio variable - Same as interval but scores are meaningful ie: 50kg is twice as heavy as 25kg
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13
Q

Levels of Measurement and complexity

A
  • Nominal - Categorie
  • Ordinal - orders
  • Interval - meaningful distance
  • Ratio - absolute zero
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14
Q

Principles of measurement in research

A
  1. An outcome variable belongs to either nominal, ordinal, interval and ratio
  2. Characteristic of the next level eg: ratio measurements such as height also capture ordinal information
  3. The higher the level the more precise the result and closer you will be to the true outcome of interest
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15
Q

Principles of Measurement in Research - Points to Ponder

A
  • More information increases the power and utility of your results
  • Sometimes you will be limited to what is available to you
  • Always define your variables in ways that maximise the use of your information
  • In behavioural and social sciences most data is usually nominal or ordinal however test scores yield interval level data
  • How you choose to measure an outcome defines the level of measurement
  • Variables may not completely fit this rigid framework in the real world
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16
Q

Reliability and Validity

A
  • You’re only as good as your tools
  • You can have a great research question but will not succeed if your tools are unreliable
  • The consistency and validity of a measurement tool are critical to good research
  • Faulty tools lead to errors in accepting or rejecting the null hypothesis
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17
Q

Reliability

A
  • When measuring we assume that there will be a discrepancy found
  • The True value of measurement
  • Reliability decreases as error increases
  • Reliability = True Scories

True Score + Error

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18
Q

Ways to increase measurement reliability

A
  • Increase number of items or observations
  • Eliminate ambiguity
  • Standardise conditions
  • Moderate difficulty
  • Minimise effects of external events
  • Standardise instructions and Standardise scoring
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19
Q

How to measure reliability

A
  • We use correlation; a measure of relationships between things
  • We can calculate a number that provides a gauge of relationship direction and strength
  • Called Correlation Coefficient
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20
Q

Correlation Coefficient

A
  • This is a measure of the direction and extent of the relationship between two sets of scores.
  • Range of a correlation coefficient is from -1 to +1
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21
Q

Pearson’s r

A
  • Pearson’s product moment correlation coefficient
  • This coefficient will provide a gauge of how similar scores on a test are from time 1 to time 2
  • This is one form of reliability
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22
Q

Types of Reliability - Test-Retest

A
  • A Measure of stability; how stable is a test over time,
  • Measuring the same individuals at two points in time
23
Q

Operational Definition

A

The operational definition of a variable is the specific way in which it is measured in that study

24
Q

Name the different types of Reliability (4)

A
  • Test-Retest
  • Parallel Forms
  • Interrater
  • Internal Consistency
25
Q

Types of Reliability - Parallel Forms

A
  • Different forms of the same test given to the same group of participants
  • You might see this in a practice effects test
26
Q

Types of Reliability - Interrater

A
  • Evidence of reliability when multiple raters agree in their observations of the same thing
  • Rater to Rater, rather than time to time

eg: observational research

27
Q

Types of Reliability - Internal Consistency

A
  • Uses responses at only one time
  • Focusses on consistency of items
28
Q

Types of Reliability

A
  • Test-Retest
  • Parallel Forms
  • Interrater
  • Internal Consistency
29
Q

Measuring what we intend to . . .

A
  • Our measues should be reliable and valid
  • Validity refers to the results of the test
  • It is never all or nothing
  • Validity of the results interpreted in teh context where the test occurs
  • Are the results understood within the context of the purpose of research?
30
Q

Name the Types of Validity

A
  • Face Validity
  • Content Validity
  • Criterion-related Validity
  • Construct Validity
31
Q

Types of Validity - Face Validity

A
  • Extent to which items on a test appear to measure the construct
  • Does it look like the items are asking relevant questions
  • Will the test taker understand what is being measured
32
Q

Types of Validity - Content Validity

A
  • The content of the measure compares with the universe of content that defines the construct
  • are the items a representative sample of all possible items
33
Q

Types of Validity - Criterion-Related Validity

A
  • A score indicates the level of performance on an external criterion against which it is compared.
  • A measure of the extent to which a test is related to a criteria
  • Two Types of Criterion Validity.
    • Predictive - the future
    • Concurrent - the present

eg: GPA predictive validity for performance in Honours?
- Beware the importance of identifying the criterion

34
Q

Types of Validity - Construct Validity

A
  • An assessment corresponds to other variables , as predicted by some rationale or theory.
  • Links the practical components of a test score to some underlying theory or model of behaviour
  • Firstly, similar to criterion validity, you will look for correlations between your newly developed test and test already shown to tap the target construct
35
Q

Thoughts on construct validity

A
  • If a measure has convergent validity, it should correlate with questionairres that measure:
    • The same construct
    • Related Constructs
      • eg: Beck Depression Inventory II positivley associated with Hamilton Depression scale
  • If a measure has discriminant validity it should not correlate questionairre that measure
    • Different Constructs
    • Unrelated Constructs
36
Q

The Relationship between Reliability and Validity

A

Reliability is a necessary but not sufficient condition of validity.

37
Q

Finding Reliability and Validity Information (7)

A
  • Mental Measurements Yearbook
  • Tests in Print
  • Buros Institute of Mental Measurements - http://www.unl.edu/buros/
  • Test Link – ETS Test Collection - http://www.ets.org/testcoll/index.html
  • APA - http://www.apa.org/science/testing.html)
  • PsycINFO
  • Test manuals
38
Q

Sampling & Generalasibility

A

* Choose a research question and decide how to test it

  • Decide who you will ask to participate in your research
  • Vital Stage of researh development
  • Who should I study
    eg: I want to look at attitudes toward taking drugs in sport
  • Success of your research hinges on the way in which you select participants.
39
Q

Describe a Population

A
  • Population = the collection of units to which we want to generalise our research findings.
  • Populations can be large or narrow
  • Generally, researchers aim to infer about general populations
  • rare to access all members
  • Data collected from a subset of the population is the Sample
40
Q

Describe a Sample

A
  • A smaller collection of observations from a population that are used to infer characteristics about the population
  • The bigger the sample the more likely it is an accurate reflection of the population
  • Results vary slightly from sample to sample but tend to average out as similar
  • Results have meaning when they can be generalised from sample to population
41
Q

Two Types of Sampling Strategies

A
  1. Probability Sampling
  2. Non Probability Sampling
42
Q

Probably Sampling

A
  • The likelihood of any one member of population being selected is known
    eg: If 4,000 students are at ACAP and 20 of them are in the Psychology Honours year, then the odds of selecting one Honours student as part of a sample is 20/4,000 → 0.005
43
Q

Non Probability Sampling

A
  • The likelihood of selecting any one member from the population is not known
  • If I don’t know how many Psychology Honours students are at ACAP, then likelihood of one being selected cannot be computed
44
Q

4 Types of Probability Sampling

A
  1. Simple Random Sampling
  2. Sustematic Sampling
  3. Stratified Sampling
  4. Cluster Sampling
45
Q

Simple Random Sampling

A
  • Each member of the popluation has an equal chance of being selected to be part of the sample
    • Equal - No bias that one individual will be chosen over another
    • Independent - The choice of one individual does not create bias the reasearch nor is there bias in choosing another participant
46
Q

Four Steps of Simple Random Sampling

A
  1. Definition of the population
  2. List all members of that population
  3. Assign numbers to each members of the population
  4. Use of criterion to select sample that is wanted
47
Q

Systematic Sampling

A
  • Every kth name on the list is chosen
  • where k = any number 0 and the size of the sample to be selected 0 and the size of the sample to be selected.
  • Easier than random sampling
  • However
    • less precise
    • violates the assumption that each unit has equal chance of being selected

Procedure:

  • We want a random sample of 8 names–Divide size of population by size of desired sample → 32 / 8 = 4
  • 4 is the size of the step we want.
  • Select one name from the list at random, and use this as your starting point (again, use table of random numbers)
  • Then select every 4th name until a sample of 8 has been achieved
48
Q

Stratified Sampling

A
  • Individuals in sample are NOT equal
    and
  • these unequal characheristics are related to what is being studied in the research
  • Can be done through SPSS
  • Ensures profile of sample matches the population
  • layers (or Strata) are fairly represented in the sample
49
Q

Stratified Sampling Procedure

A
  • All males and females are listed separately
  • Each member of each group receives a number
  • From a table of random numbers, four males are selected at random from the list of 13 using the procedure outlined for simple random sampling
  • From a table of random numbers, six females are selected at random from the list of 22 using the procedure outlined for simple random sampling
50
Q

Nonprobability Sampling Strategies

A
  • Probability of selecting a single individual or unit is not known
  • Impedes ability to generalise results obtained from sample to the larger population
  • TWO primary types:
    • Convenience
    • Quota
51
Q

Convenience Sampling

A
  • Uses a captive audience,
  • i.e., much psychology research uses first year university students who must participate for credit

•THEREFORE:

  • Sample is convenient, but not random
  • Sears (1986) cautions a “A narrow database…”
52
Q

Quota Sampling

A
  • Used where research requires stratified sample but proportional stratified sampling is not possible
  • Selects individuals with characteristics desirable for the research but does not randomly select from the population a subset of all individuals
  • Researcher continues to enlist participants in each strata until quota for research is achieved
  • Easier than stratified sampling
  • Less precise than stratified sampling
  • Limits ability to generalise to population of interest
53
Q

Sample Size - How big is big?

A
  • You want to have a sample representative of the population; less representative more error, and the less precise your test of the null hypothesis
  • Sample size also has implications to the power of your test; its sensitivity in detecting a significant result….

Evidence Based Approach (15 decks)

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