Reasoning in Psychology Using Statistics Psychology

Social Science Reasoning Using Statistics Announcements Don’t forget Quiz 1, due Friday, Jan. 23rd Exam 1 not far away, Wed Feb 4 th

Social Science Reasoning Using Statistics 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

Social Science Reasoning Using Statistics Basic Research Methods Observational (correlational) study –Observe & measure variables of interest to find relationships –No attempt to manipulate or influence responses Experimental methodology –Independent variable manipulated while changes observed & measured in another variable (dependent) –Can establish cause-and-effect relationships –Extensive controls to minimize extraneous sources of variability Quasi-experimental methodology –Independent variable a pre-existing characteristic (e.g., sex, age, etc.) –Groups to compare, but don’t know relevant psychological variable

Social Science Reasoning Using Statistics Basic Research Methods Observational (correlational) study –Observe & measure variables of interest to find relationships –No attempt to manipulate or influence responses Experimental methodology –Independent variable manipulated while changes observed & measured in another variable (dependent) –Can establish cause-and-effect relationships –Extensive controls to minimize extraneous sources of variability Quasi-experimental methodology –Independent variable a pre-existing characteristic (e.g., sex, age, etc.) –Groups to compare, but don’t know relevant psychological variable

Social Science Reasoning Using Statistics Claim: Absence makes the heart grow fonder What are the variables in this claim? Explanatory (independent) variable Response (dependent) variable Measuring and Manipulating Variables

Social Science Reasoning Using Statistics 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 theseor perhaps a combination

Social Science Reasoning Using Statistics Measuring and Manipulating Variables So what do we mean by heart grow fonder? Strength of relationship Level of desire 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 Claim: Absence makes the heart grow fonder

Social Science Reasoning Using Statistics Measuring and Manipulating Variables 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

Social Science Reasoning Using Statistics Measuring and Manipulating Variables Operational definition – Specifies relationship between conceptual & operational levels 1.Describes set of operations or procedures for measuring conceptual variable 2.Defines variable in terms of measurement

Social Science Reasoning Using Statistics Measurement: Quantitative Research How to measure a variable? –Instrument: Tool to measure dependent variable –e.g., fondness Survey Brainwave machine How might these measures be different? What impact might these differences have?

Social Science Reasoning Using Statistics Measurement: Quantitative Research Two properties of measurement –Unit of measurement - minimum sized unit –Scale of measurement - correspondence between properties of numbers &variables measured Error in measurement

Social Science Reasoning Using Statistics Measurement: Quantitative Research Two properties of measurement: –Unit of measurement - minimum sized unit –Scale of measurement - correspondence between properties of numbers &variables measured Error in measurement

Social Science Reasoning Using Statistics Units of Measurement Continuous 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 or 2 kids, but not 2.5

Social Science Reasoning Using Statistics Measurement Two properties of measurement: –Unit of measurement - minimum sized unit –Scale of measurement - correspondence between properties of numbers &variables measured

Social Science Reasoning Using Statistics Scales of measurement Categorical variables Quantitative variables Set of categories Distinct levels with differing amounts of characteristic of interest Can attach numbers to these amounts

Social Science Reasoning Using Statistics Scales of measurement Categorical variables –Nominal scale

Social Science Reasoning Using Statistics Scales of measurement 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

Social Science Reasoning Using Statistics Scales of measurement Categorical variables –Nominal scale –Ordinal scale

Social Science Reasoning Using Statistics 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

Social Science Reasoning Using Statistics Scales of measurement Categorical variables –Nominal scale –Ordinal scale Quantitative variables –Interval scale

Social Science Reasoning Using Statistics Scales of measurement 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 20º40º “Not Twice as hot”

Social Science Reasoning Using Statistics 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

Social Science Reasoning Using Statistics Scales of measurement Ratio scale 8 cards high

Social Science Reasoning Using Statistics Scales of measurement Interval scale 5 cards high

Social Science Reasoning Using Statistics Scales of measurement Interval scaleRatio scale 8 cards high5 cards high 0 cards high means ‘no height’ 0 cards high means ‘as tall as the table’

Social Science Reasoning Using Statistics Scales of measurement Interval scaleRatio scale 8 cards high5 cards high 0 cards high means ‘no height’ 0 cards high means ‘as tall as the table’ Rescale: 0 = Mean Ht = X - M

SPSS Scale of Measure: Nominal, Ordinal, Scale

Social Science Reasoning Using Statistics Measurement: Quantitative Research Two properties of measurement: –Unit of measurement - minimum sized unit –Scale of measurement - correspondence between properties of numbers &variables measured Error in measurement

Social Science Reasoning Using Statistics Errors in measurement Validity –Does our measure really measure the construct? –Is there bias in our measurement? Reliability –Do we get the same score with repeated measurements?

Social Science Reasoning Using Statistics Dart board example 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

Social Science Reasoning Using Statistics Dart board example Low variability/low bias Points are all close together (similar) & Centered on the target Reliable & valid measure

Social Science Reasoning Using Statistics Dart board example Low variability/high bias Points are all close together (similar) & NOT centered on the target Reliable but invalid measure

Social Science Reasoning Using Statistics Dart board example High variability/low bias Points are NOT all close together (dissimilar) & Centered on the target Valid but unreliable measure

Social Science Reasoning Using Statistics Dart board example High variability/high bias Points are NOT all close together (dissimilar) & NOT centered on the target Unreliable & invalid measure

Social Science Reasoning Using Statistics Wrap up Today’s lab: Measurement Questions? SPSS