CMPDLLM002 Research Methods

1. CMPDLLM002Research Methods Lecture 9:Quantitative Analysis: Basic Statistics (Using SPSS)

2. Overview of the session • Week 1: Introduction to Quantitative Analysis • Week 2: Basic Statistics (using SPSS) • Week 3: Statistical Testing (using SPSS)

3. Basic Statistics Topics • Basic Statistical Measures • Data Collection • Visualizing Data • Summarizing Data • Exploring Frequency Distributions

4. Basic Statistical Measures • Measurement Scales (recall 3 main scales) • Nominal: place units into categories with no relationship • Ordinal: place units into categories with ordered relationship • Interval: measurement on scale of equal interval (length, time) • NOTE: SPSS calls the interval scale “Scale” itself (confusing)?? • 3 basic things we wish to consider of data measurements • The average value or central tendency (mean, median) • The type of distribution (normal or skewed?) • The spread or dispersion of values around the mean (variance and standard deviation) • Standard deviation = sqrt(variance)

5. Collecting Data • Data collected in a matrix • Rows represent members of a sample • Columns represent measurements or variables • SPSS data entry • SPSS immediately starts with an empty “Data Entry Editor” • Select Data | Define Variables from the main menu to enter a meaningful variable name (defaults are VAR0001, VAR0002 etc) • Specify type of Measurement (nominal, ordinal or scale) • Select Type to specify a type for each data element of the variable (numeric, scientific, string date etc.) • Next 3 slides show data entry for a New Drug study • drug is the independent variable an resp1, resp2, resp3, pulse1, pulse2, pulse3 are the dependent variables

7. Select Data | Define Variable from the main menu Click here to display the “Labels” Dialog

8. “Define Labels” lets us assign meaningful names that will be used in subsequent plots and tables

9. Visualizing Data • Select Graphs from the main menu and then one of the many chart options • Bar…, Boxplot…, Histogram…, Line… etc. • Bar chart and pie chart • Useful for visualizing nominal and ordinal independent variables • Bar charts are preferred because they can compare categories that are not part of a whole (i.e. 100%) • Histogram and box plot • Useful for displaying interval scale frequency distributions • Box plots are preferred for comparing two or more groups (one box per group) on the same scale • Line graphs and scatter plot • Useful when both independent and dependent variables are measured on an interval scales

10. Visualizing Data • Example: a clustered bar chart for New Drug study • 1. Select Graphs | Bar … from the main menu • 2. Select Clustered • from the • Bar Chart Dialog • 3. Click the • Summaries • of Separate • Variables • 4. Click Define • to display • the “Define • Clustered • Bar” Dialog

11. Visualizing Data Click here to move Drug independent variable into the “Category Axis” field

12. Visualizing Data Click here to copy each of the Respiratory and Pulse dependent variables into the “Bars Represent” text box

13. Visualizing Data Finally click OK to display the bar chart

14. You can select this chart and paste it into your thesis

15. Summarizing Data • Select Analyze | Explore for a tabular summary of data (mean, median, standard deviation etc) which can be pasted into your thesis

16. continued . . .

17. etc. etc . . .

18. Exploring Frequency Distributions • Many experiments lead to frequency distributions • Mean is the average value of distribution • Median is the mid-way value of distribution • Standard deviation and variance give measures of spread or dispersion around the mean • SPSS can provide frequency distribution tables and plots, for inclusion in reports or theses • SPSS frequency distribution tables • Select Analyze | Descriptive Statistics | Descriptives for a brief summary table of minimum, maximum, mean and standard deviation values • Select Analyze | Descriptive Statistics | Explore for full listing as shown on previous slides

19. Exploring Frequency Distributions • SPSS frequency distribution plots • Histograms and box plots are used to display frequency distributions • Use histograms for single group distributions • Box plots are preferred for comparing two or more groups (one box per group) on the same scale • Box plots are like a “plan view” looking down on the mid-way slice of a histogram • If you try tutorial question 1 you will see why boxplots are useful Frequency Mean value Variable

20. Exploring Frequency Distributions • Example: A Plant Growth study produced the data shown below (12 plants measured, 6 shown below)

21. Exploring Frequency Distributions • Does the plant growth follow a normal distribution? • Select Analyse | Descriptive Statistics | Frequencies to display the “Frequencies Dialog” 2. Click “Charts” to display “Charts Dialog” • Select Plant Growth and • click here to transfer to “Variables”

22. Exploring Frequency Distributions 5. Finally click “OK” to show the Histogram plotted against a Normal Curve as on the next slide 4. Select “Histogram” and “With Normal Curve” and click “Continue”

24. Summary • The bad news • Statistical analysis is a complex subject • The good news • SPSS makes statistical analysis easy for non-statisticians (like me) • Once data is captured with SPSS, you easily visualize data and explore relationships • Frequency distributions tell us a great deal about relationships between independent and dependent variables • SPSS is an exploratory package, with good on-line help that lets us explore data from an experiment and go on to test our hypothesis

25. NOTE: SPSS v10.0 The screen shots on the slides are for SPSS v9.0, which is the version in labs 722 and 728. SPSS v10.0 has just been installed in all 3rd floor labs. SPSS v9.0 and v10.0 are very similar the only difference is in defining variables. If you use v10.0 you will notice two tabs in the Data Entry Editor, a “Data View” tab and a “Variable View” tab. To define a variable in SPSS v10.0 click the “Variable View” tab and enter “Name” (e.g. drug), “Type” (e.g. Numeric), “Measure” (nominal, ordinal, scale). If the variable is nominal or ordinal you can assign meaningful names (e.g. “New Drug” and “Placebo” for variable drug) by clicking “Value” and assigning appropriate text strings for your numerical values (e.g. 1 = “New Drug”, 2 = “Placebo”). When you have defined your variables click the “Data View” and enter your data into the columns for each variable.

26. SPSS v10.0 Variable Definition Click “Variable View” tab to define variable

27. SPSS v10.0 Variable Definition Click “Values” and enter meaningful names for nominal or ordinal scale variables

28. SPSS v10.0 Data Entry Click “Data View” tab to enter you data