1. Reasoning in Psychology Using Statistics
2017

2. Announcements Don’t forget Quiz 1, due Friday, Jan. 27
Exam 1 not far away, Wed Feb 8th
Announcements

3. From last time Scientific Method Ask research question
Identify variables and formulate hypothesis
Define population
Select research methodology
Collect data from sample
Analyze data
Draw conclusions based on data
Repeat
Where do the data come from?
Experiments method
Independent variables
Dependent variables
Observational method
Explanatory variables
Response variables
A process
From last time

4. Measuring and Manipulating Variables
Response (dependent) variable
Claim: Absence makes the heart grow fonder
What are the variables in this claim?
Explanatory (independent) variable
I. Go over worksheet from last lab
Scientific Method
Lots of ways to know things: common sense, authority, reasoning/logic, etc.
The Scientific method is testing hypotheses based on observational data. The emphasis of this course is to develop the statistical reasoning and tools to assist in this process of evaluating hypotheses.
Measuring and Manipulating Variables
We’ve talked about manipulating (independent) and measuring (dependent) these. Let’s focus a bit more on HOW we manipulate or measure these things.
Measuring and Manipulating Variables

5. Measuring and Manipulating Variables
Claim: Absence makes the heart grow fonder
Two levels of variables
Conceptual level of variables
What theory is about (absence, fondness)
Operational level of variables
What actually manipulated/measured in research
Duration of time apart
Rated fondness
Operational definition
Specifies relationship between conceptual & operational levels
Measuring and Manipulating Variables

6. Measuring and Manipulating Variables
Operational definition
Specifies relationship between conceptual & operational levels
Describes set of operations or procedures (the instrument) for measuring conceptual variable
Defines the variable in terms of measurement
Measuring and Manipulating Variables

7. Claim: Absence makes the heart grow fonder
What do we mean by absence?
Two people involved in relationship having to be apart for a long time.
How do we measure (or manipulate) absence?
Amount of time apart, number of visits, distance
one of these
or perhaps a combination
Measuring Variables

8. Claim: Absence makes the heart grow fonder
So what do we mean by heart grow fonder?
Strength of relationship
Level of desire
Survey
How do we measure fondness of the heart?
Have couple rate fondness for one another
Hook each to brain monitor & record while seeing pictures of sweetheart & pictures of other people
Brainwave machine
I. Go over worksheet from last lab
Scientific Method
Lots of ways to know things: common sense, authority, reasoning/logic, etc.
The Scientific method is testing hypotheses based on observational data. The emphasis of this course is to develop the statistical reasoning and tools to assist in this process of evaluating hypotheses.
Measuring and Manipulating Variables
We’ve talked about manipulating (independent) and measuring (dependent) these. Let’s focus a bit more on HOW we manipulate or measure these things.
An operational definition defines a construct in terms of specific operations or procedures and the measurements that result from them. Thus, an operational definition consists of two components:
 First, it describes a set of operations or procedures for measuring a construct.
 Second, it defines the construct in terms of the resulting measurements.
Data are often collected in an attempt to measure something we want to learn about (e.g., does the heart grow fonder with greater numbers and durations of absences?).
Measuring Variables

9. Claim: Absence makes the heart grow fonder
Choosing your instrument
How might these measures be different?
What impact might these differences have?
Survey
How fond are you of your partner?
Not at all
Somewhat
Very much
Brainwave machine
I. Go over worksheet from last lab
Scientific Method
Lots of ways to know things: common sense, authority, reasoning/logic, etc.
The Scientific method is testing hypotheses based on observational data. The emphasis of this course is to develop the statistical reasoning and tools to assist in this process of evaluating hypotheses.
Measuring and Manipulating Variables
We’ve talked about manipulating (independent) and measuring (dependent) these. Let’s focus a bit more on HOW we manipulate or measure these things.
An operational definition defines a construct in terms of specific operations or procedures and the measurements that result from them. Thus, an operational definition consists of two components:
 First, it describes a set of operations or procedures for measuring a construct.
 Second, it defines the construct in terms of the resulting measurements.
Data are often collected in an attempt to measure something we want to learn about (e.g., does the heart grow fonder with greater numbers and durations of absences?).
Measuring Variables

10. Measurement: Quantitative Research
Properties of measurement
Unit of measurement
Scale of measurement
Error in measurement
Validity
Reliability
Measurement: Quantitative Research

11. Measurement: Quantitative Research
Properties of measurement
Unit of measurement - minimum sized unit
Scale of measurement
Error in measurement
Validity
Reliability
Measurement: Quantitative Research

12. Units of Measurement Continuous variables Discrete variables
Variables can take any number & be infinitely broken down into smaller & smaller units
E.g., For lunch I can have 2, 3,
or 2.5 cookies
Discrete variables
Broken into a finite number of discrete categories that can’t be broken down
E.g., In my family I can have
1 kid
, but not 2.5
or 2 kids
Units of Measurement

13. Measurement Properties of measurement Unit of measurement
Scale of measurement - correspondence between properties of numbers & variables measured
Stevens (1946) Typology
Categorical variables
Nominal scale
Ordinal scale
Quantitative variables
Interval scale
Ratio scale
Measurement

14. Scales of measurement Categorical variables Quantitative variables
Set of discrete kinds of things (categories)
Can attach names to these categories
Distinct levels with differing amounts of characteristic of interest
Can attach numbers to these amounts
Which scale you use will impact what statistics you can perform
and how you should interpret your analyses
Scales of measurement

15. Nominal Scale: Consists of a set of categories that have different names.
Measurements on a nominal scale label and categorize observations, but do not make any quantitative distinctions between observations.
Example:
Eye color:
blue,
green,
brown,
hazel
Scales of measurement

16. Ordinal Scale: Consists of a set of categories that are organized in an ordered sequence.
Measurements on an ordinal scale rank observations in terms of size or magnitude.
Example:
T-shirt size:
Small,
Med,
Lrg,
XL,
XXL
Scales of measurement

17. Interval Scale: Consists of ordered categories where all of the categories are intervals of exactly the same size.
With an interval scale, equal differences between numbers on the scale reflect equal differences in magnitude.
Ratios of magnitudes are not meaningful.
Example:
Fahrenheit temperature scale
40º
20º
“Not Twice as hot”
Scales of measurement

18. Ratio scale: An interval scale with the additional feature of an absolute zero point.
With a ratio scale, ratios of numbers DO reflect ratios of magnitude.
It is easy to get ratio and interval scales confused
Consider the following example: Measuring your height with playing cards
Scales of measurement

19. Ratio scale
8 cards high
Scales of measurement

20. Interval scale
5 cards high
Scales of measurement

21. Scales of measurement Ratio scale Interval scale 8 cards high
0 cards high means ‘as tall as the table’
0 cards high means ‘no height’
Scales of measurement

22. In SPSS Scale of Measure: Nominal, Ordinal, Scale (interval or ratio)
Scales of measurement

23. Measurement: Quantitative Research
Properties of measurement
Unit of measurement
Scale of measurement
Error in measurement
Validity
Reliability
Measurement: Quantitative Research

24. Errors in measurement Validity Reliability
Does our measure really measure the construct? (accuracy/precision)
Think about the operational definition
Is there bias in our measurement?
Systematic error
Reliability
Do we get the same score with repeated measurements?
Measurement Error
There are other features of variables that we’re concerned about. Do they really measure what we want, do they measure the same thing over and over, etc?
Validity – does your measure really measure what it is supposed to measure?
 Face validity – at the surface level, does it look as if the measure is testing the construct?
 Construct validity – usually requires multiple studies, a large body of evidence that supports the claim that the measure really tests the construct
 External Validity – Are experiments “real life” behavioral situations, or does the process of control put too much limitation on the “way things really work?”
 Internal Validity Refers to the precision of the results, did the change result from the changes in the Dependent variable or does it come from something else
 Predictive validity - - a measure has predictive validity if it can predict success on tasks related to the measure
Reliability – if you measure the same thing twice (or have two measures of the same thing) do you get the same values?
 Test-retest: give the same test before and after the treatment. However, the second test may be affected by the first test
 Alternate form: have two tests that are designed to test the same thing, but are different (e.g., different questions)
 Split-half: split one test "in half"; give one half before the treatment and one half after (want to have both halves test the full range of variables)
The accuracy of your measurement can be affected by the reliability of the measure, various forms of measurement error (such as data recording errors), and bias (such as experimenter bias).
Errors in measurement

25. Dart board represents Population of all possible scores
Center represents the true score
Collection of ‘darts’ is a sample of measurements
The center of the sample is the estimate of the true score
Dart board example

26. Dart board example Reliable & Valid measure Low variability/low bias
Points are all close together (similar)
&
Centered on the target
Reliable & Valid measure
Dart board example

27. Dart board example Reliable but Invalid measure
Points are all close together (similar)
Low variability/high bias
&
NOT centered on the target
Reliable but Invalid measure
Dart board example

28. Dart board example Valid but Unreliable measure
Points are NOT all close together (dissimilar)
High variability/low bias
&
Centered on the target
Valid but Unreliable measure
Dart board example

29. Dart board example Unreliable & invalid measure
Points are NOT all close together (dissimilar)
High variability/high bias
&
NOT centered on the target
Dart board example

30. Today’s lab: Measurement
Questions?
SPSS
Wrap up