Reasoning in Psychology Using Statistics 2019

Announcements Don’t forget Quiz 1, due this Friday, Jan. 25 Exam 1 not far away, Wed Feb 6th Announcements

From last time Scientific Method Measuring and Manipulating Variables 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 Measuring and Manipulating Variables A process From last time

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

Measuring and Manipulating Variables Claim: Absence makes the heart grow fonder Two levels of variables Conceptual level of variables (abstract) What theory is about (absence, fondness) Operational level of variables (concrete) What actually manipulated/measured in research Duration of time apart Rated fondness Operational definition Specifies relationship between conceptual & operational levels Measuring and Manipulating Variables Read the methods of actual example: Jiang & Hancock (2013)

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

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

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

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 1 - 2 - 3 - 4 - 5 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

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

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

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

Measurement: Quantitative Research Properties of measurement Unit of measurement Scale of measurement - correspondence between properties of numbers & variables measured Stevens (1946) Typology Categorical variables: Nominal & Ordinal Scales Set of discrete kinds of things (categories) Can attach names to these categories Quantitative variables: Interval and Ratio Scales 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 Measurement: Quantitative Research

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

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 Think about Restaurant ratings

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

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

Ratio scale 8 cards high Scales of measurement

Interval scale 5 cards high Scales of measurement

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

In SPSS Scale of Measure: Nominal, Ordinal, Scale (interval or ratio) – in the variable view Scales of measurement

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

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

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

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

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

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

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

Wrap up Today’s lab: Measurement Questions? SPSS Operational Definitions: Measuring Happiness (~2 mins) Operational Definitions (~20 mins) Scales of measurement (~6 mins) Scales of measurement (~7 mins) Scales of measurement (~15 mins) Reliability and validity (~ 2 mins) Reliability and validity (~ 2 mins) Wrap up