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Thursday August 22, 2013

Thursday August 22, 2013. Course Overview, Frequency Distributions, & Review of SPSS Data Entry. Brief Overview: Where are we Headed in PSY 340?. How is this course different from PSY 138? Format: Longer lectures, but no separate lab times Content: review of PSY 138 and beyond

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Thursday August 22, 2013

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  1. Thursday August 22, 2013 Course Overview, Frequency Distributions, & Review of SPSS Data Entry

  2. Brief Overview: Where are we Headed in PSY 340? • How is this course different from PSY 138? • Format: Longer lectures, but no separate lab times • Content: review of PSY 138 and beyond • Dealing with more complex situations (more than 2 variables) • Hypothesis testing with: • Correlation and regression • Multiple regression • Tests for differences with more than two groups

  3. PSY 138

  4. This course

  5. Our decision tree helps us ask the right design questions which will lead us to the appropriate statistical test Statistical analysis follows design

  6. Decide if there is a difference Decide if there is a relationship between variables Experimental & Quasi-experimental studies Testing for differences between groups (conditions) Vs. Observational studies Testing for similarities between variables This is a generality, there are exceptions. Towards the end of the course we’ll see that the two may be considered essentially the same kinds of analyses Statistical analysis follows design

  7. Basic Research Methods • Observational study • Researcher observes and measures variables of interest to find relationships between the variables • No attempt is made to manipulate or influence responses • Experimental methodology • One (or more) independent variable(s) is manipulated while changes are observed in another variable (dependent) • Used to establish cause-and-effect relationships between variables • Uses extensive methods of control to minimize extraneous sources of variability • Quasi-experimental methodology • One (or more) of the independent variables is a pre-existing characteristic (e.g., sex, age, etc.)

  8. Example Observational Experimental • What’s the best way to study for a test? • Randomly select individuals • Watch their study habits • See how they do on a test • Randomly select individuals • Randomly assign to groups • Crammed study group • Distributed study group • See how they do on a test

  9. First, consider the experiment (two groups, different studying conditions)

  10. In general, if you are comparing groups of scores (i.e., using experimental or quasi-experimental research designs), you will use methods in the top section of the flow chart (z-test, t-test, ANOVA). Statistical analysis follows design

  11. Statistical analysis follows design The next question in the tree How many separate samples?

  12. We test these folks and then generalize the results to the population as a whole What are samples? • Whom do we test? • Population • The set of all individuals of interest • Typically we don’t have access to all of the population • Sample • A subset of the population from whom data is collected

  13. What are samples? condition C condition B condition D condition A • “Sample” • may also be used to refer to the participants (randomly) assigned to a particular condition of the experiment

  14. Statistical analysis follows design Next question in the tree Are the samples matched or independent?

  15. Now, consider the observational study (one sample, measure study habits and test performance & see if they are related)

  16. In general if you are examining the associations among variables, you will use methods in the bottom section of the flow chart (correlation, regression, chi-square test of independence). Statistical analysis follows design

  17. Self-monitor your understanding Any questions about research methods terms and concepts, and how they relate to the statistical analysis decision tree? Get ready to switch gears to concepts/terms related to measurement

  18. Measurement Properties of our measurement? Units of measurement - whether the measurement has a minimum sized unit or not Levels (Scales) of measurement - the correspondence between the numbers representing the properties that we’re measuring

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

  20. Levels (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: brown, hazel blue, green,

  21. Levels 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: XXL XL, Lrg, Med, Small,

  22. Levels 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 40º 20º “Not Twice as hot”

  23. Levels 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.

  24. Levels of measurement in 340 • For our purposes in this class, interval and ratio scales are treated the same way (in SPSS, they are both designated as “scale” measures) • SPSS also has “ordinal” and “nominal” measure designations. • As long as a variable is “numeric,” most SPSS procedures that we use in here won’t be affected by which measure designation is selected, but it’s helpful to understand the differences.

  25. Levels of measurement in 340 • Grouping variables (used to designate experimental or quasi-experimental conditions) are usually nominal variables (e.g., store brand vs. name brand; treatment vs. control). These may also be thought of as independent or quasi-independent variables • Dependent variables are often measured on interval or ratio scales (how depressed, how happy, how many, how much) • In correlational studies, two or more variables are measured on each participant. These are typically measured on interval or ratio scales.

  26. Grouping (independent) variables in experiments or quasi-experiments are often nominal, and outcome (dependent) variables are usually interval or ratio variables. Statistical analysis is also influenced by measurement

  27. In observational studies, relationships among interval/ratio variables are analyzed using correlation or regression, whereas relationships among nominal/ordinal variables are often analyzed using chi-square tests. Statistical analysis is also influenced by measurement

  28. Our variables from survey Discrete or continuous? What level of measurement? Male or female Height (in inches) How many pairs of shoes in your closet Number of chips in a cookie Rating on a likert scale Cookie brand (“Sample A” vs. “Sample B”) Now let’s consider how we would enter these variables into SPSS But first: Self-monitor your understanding… Any questions about measurement terms/concepts and how these relate to the statistical analysis decision tree?

  29. Brief review of SPSS Data view This is where you type in all of the data Two view windows: To switch between the views click on the tabs

  30. Brief review of SPSS Variable view This is where you specify the details about the variables Two view windows:

  31. Variable view Type of variable: numeric, text, monetary, date, etc. Name of the variable

  32. The Data View Each row corresponds to an experimental unit (called “cases” in SPSS lingo) Each column corresponds to a variable So each column in the data view corresponds to a row in the variable view

  33. Self-monitor your understanding Any questions about SPSS? Get ready to switch gears to discussing frequency distributions

  34. Distributions The distribution of a variable is a description of all of the tokens of the variable within a sample (or population if you’ve got the data) A picture of the distribution is usually helpful Gives a good sense of the properties of the distribution Many different ways to display distribution Frequency distribution table Graphs

  35. The values of the variable Steps for Making a Frequency Table(do this for class soda drinking variable) • Make a list down the page of each possible value, from highest to lowest

  36. The values of the variable The number of tokens of each variable Steps for Making a Frequency Table • Go one by one through the scores, making a mark for each next to its value on the list, count up how frequently each value appears and include this in the table

  37. The values of the variable The percentage of tokens at each value The number of tokens of each variable Steps for Making a Frequency Table • Figure the percentage (or proportion) of scores for each value % = (f/N)*100 N=total

  38. The values of the variable The percentage of tokens at each value The number of tokens of each variable Cumulative percentage Steps for Making a Frequency Table • Figure the cumulative percentage (or proportion) of scores for each value % = (f/N)*100 N=total

  39. SPSS Output (A Little Different from Textbook) Pairs of Shoes Freq % Valid % Cumulative % Valid 2 3 11.5 12.5 12.5 5 3 11.5 12.5 25.0 6 1 3.8 4.2 29.2 7 1 3.8 4.2 33.3 8 1 3.8 4.2 37.5 10 2 7.7 8.3 45.8 12 2 7.7 8.3 54.2 13 1 3.8 4.2 58.3 14 2 7.7 8.3 66.7 15 2 7.7 8.3 75.0 20 4 15.4 16.7 91.7 25 1 3.8 4.2 95.8 30 1 3.8 4.2 100.0 Total 24 92.3 100.0 Missing System 2 7.7 Total 26 100.0

  40. Grouped Frequency Table A frequency table that uses intervals (range of values) instead of single values Pairs of Shoes X Values Freq % Cumulative %↑ Cumulative %↓ 0-4 3 13 12 100 5-9 6 25 38 87 10-14 7 29 67 62 15-19 2 8 75 33 20-24 4 17 92 25 25-29 1 4 96 8 30-34 1 4 100 4 Total 24 100

  41. Frequency Graphs • Histogram • Plot the different values against the frequency of each value

  42. Frequency Graphs • Histogram (create one for class height) • Step 1: make a frequency distribution table (may use grouped frequency tables) • Step 2: put the values along the bottom, left to right, lowest to highest • Step 3: make a scale of frequencies along left edge • Step 4: make a bar above each value with a height for the frequency of that value

  43. Frequency Graphs • Frequency polygon - essentially the same, but uses lines instead of bars

  44. Properties of distributions • Distributions are typically summarized with three features • Shape • Center • Variability (Spread)

  45. Shapes of Frequency Distributions • Unimodal, bimodal, and rectangular

  46. Shapes of Frequency Distributions • Symmetrical and skewed distributions • Normal and kurtotic distributions

  47. Distribution for “pairs of shoes” Pairs of Shoes Frequency 2 3 5 3 6 1 7 1 8 1 10 2 12 2 13 1 14 2 15 2 20 4 25 1 30 1 Total 24 Missing 2 Total 26

  48. Next time • In addition to using tables and graphs to describe distributions, we also can provide numerical summaries

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