Crash Course in Statistics

Transcription

1 Neuroscience Center Zurich Crash Course in Statistics Introduction to SPSS July 2014 Dr. Jürg Schwarz Slide 2 Program 8 July 2014: Morning Lessons ( ) First Part Introduction - A typical example - Resources - First steps Exercises Program 8 July 2014: Afternoon Lessons ( ) Second Part Additional Topics - Analysis functions - Charts Exercises

2 Table of Contents Slide 3 Introduction 5 A typical example... 5 "How-to" in SPSS First Impression Resources 11 Manuals Sample Files Using the Help System (Core System User s Guide) Online-Resources First Steps 18 Change the Application Language Starting SPSS & Opening a Data File Data Editor & Data Organization Running an Analysis & Viewing Results Intermezzo: Alphabetical view of the variables in the dialog boxes Working with Syntax Modifying Data Values Select Cases & Split File Data Entry Data Editor: Defining Variables, Entering Data & Missing Values Importing Data Exercise 01: First Part Introduction 52 Slide 4 Analysis functions: Analyze 53 Descriptive Statistics Inferential Statistics Creating charts 70 Manuals Creating and Editing Charts Bar Chart Self-Study 45 minutes Exercise 02: Second Part Additional Topics 73

3 Introduction Slide 5 A typical example Medical research: What are the factors affecting body weight? Data set (EXAMPLE00.SAV) Sample of n = 198 men and women Body weight [kg] Typical questions Is there an impact of the factors K body size [cm] age [years] sex [0/ 1] K on body weight? Body size [cm] How can this impact be modeled? How strong is the impact of each factor? Slide 6 A closer look: Joint representation of the relationships between the variables Body weight Body size Age Age Body size Body weight

4 Questions Slide 7 Question in everyday language: How do individual characteristics influence body weight? Research question: Is there an impact of the factors K body size age sex K on body weight? How strong is the impact of the factors? Is there a model? Is linear regression analysis the right model? Statistical question: H 0 :"No model" (= No overall model and no significant coefficients) H A :"Model"(= Overall model and significant coefficients) Can we reject H 0? Solution Multiple linear regression model with body weight as the dependent variable body weight = β 0 +β1 age+β2 body size+β3 sex+ u body weight= dependent variable age,... sex= independent variables β,... β = coefficients 0 3 u= error term Slide 8 "How-to" in SPSS Scales Dependent variable: metric Independent variables: metric, categorical (coded as dummy variables) SPSS: AnalyzeRegressionLinear... Method: Enter (All variables are entered into the model simultaneously) Method: Stepwise (Each variable is individually tested for fitness and included) Method: Blockwise (Variables are entered in a predefined sequences of blocks)

5 Slide 9 Result Significant overall model (table not shown) High value for "Adjusted R Square" (R 2 adj. 1) Significant coefficients (Sig. <.05) SPSS Output age has highest impact: Standardized Beta =.596 weight = size age gender_d Example interpretation: One more year of age increases body weight by.476 kilograms, holding all the other independent variables constant. "How-to" in SPSS First Impression Structure of SPSS Slide 10 Data File (in Data Editor) Output Syntax File (in Syntax Editor)

6 Resources Slide 11 Manuals This introduction refers to the manual "IBM SPSS Statistics 22 Brief Guide" Find this manual and also "IBM SPSS Statistics 22 Core System User s Guide" here: www-01.ibm.com/support/docview.wss?uid=swg #en Sample Files Slide 12 This introduction uses the data file demo.sav Find it here: Data View Variable View

7 Slide 13 The data file demo.sav is a fictional survey of several thousand people (n = 6400), containing basic demographic and consumer information. Name age marital address income inccat car carcat ed employ retire empcat jobsat gender reside wireless multline voice pager internet callid callwait owntv ownvcr owncd ownpda ownpc ownfax news response Label Age in years Marital status Years at current address Household income in thousands Income category in thousands Price of primary vehicle Primary vehicle price category Level of education Years with current employer Retired Years with current employer Job satisfaction Gender Number of people in household Wireless service Multiple lines Voice mail Paging service Internet Caller ID Call waiting Owns TV Owns VCR Owns stereo/cd player Owns PDA Owns computer Owns fax machine Newspaper subscription Response Using the Help System (Core System User s Guide) Slide 14 Help is provided in many different forms: Help menu (the most important) Topics: Provides access to the Contents, Index, and Search tabs, which you can use to find specific Help topics. Tutorial: Illustrated, step-by-step instructions on how to use many of the basic features. Case Studies: Hands-on examples of how to create various types of statistical analyses and how to interpret the results. Statistics Coach: A wizard-like approach to guide you through the process of finding the procedure that you want to use. Command Syntax Reference: Detailed command syntax reference information is available in two forms: integrated into the Help system and as a separate document in PDF form. Context-sensitive Help Dialog box Help buttons: Most dialog boxes have a Help button that takes you directly to a Help topic for that dialog box. Pivot table context menu Help: Right-click on terms in an activated pivot table in the Viewer and choose What s This? from the context menu to display definitions of the terms. Command syntax: In a command syntax window, position the cursor anywhere within a syntax block for a command and press F1 on the keyboard.

8 Help menu Slide 15 : => Dialog box Help buttons => Tutorials Slide 16 :

9 Online-Resources SPSS Solutions for Education www-01.ibm.com/software/analytics/spss/academic/students/resources.html IBM-ID Password 7mydevelopper Slide 17 SPSS Support (especially Knowledgebase Search) User spssswitzerland Password spssswitzerland SPSS Support (resources for all levels of users and application developers) User Password 7mydevelopper Other Resources / Forum / Discussion => First Steps Slide 18 Change the Application Language The language can be selected through the Language tab under EditOptions:

10 Starting SPSS & Opening a Data File Slide 19 From the Start menu choose: IBM SPSS Statistics IBM SPSS Statistics 21 Find data file demo.sav here: Other possibility: Double click on SPSS data file Data Editor & Data Organization Slide 20 The Data Editor displays the contents of the active data file Data View Columns represent variables and rows represent cases (observations) Variable View Each row is a variable, each column is an attribute of that variable

11 Slide 21 SPSS data is organized by cases (rows) and variables (columns) Data View Cases (rows) For a survey of individuals, each row would represent a respondent. In an experiment, each row might correspond to a single recorded observation. Variables (columns) Each column in the data editor corresponds to a specific measurement. In many areas of research, these measurements are called variables. Running an Analysis & Viewing Results The "Analyze" menu contains different methods of analysis. For example a simple frequency table with histogram: AnalyzeDescriptive StatisticsFrequenciesK Slide 22

12 Intermezzo: Alphabetical view of the variables in the dialog boxes The default settings of SPSS show labels for the variables in the dialog fields: Slide 23 Variables are shown with a label. This could make the search for particular variables difficult. Slide 24 SPSS can be adjusted so that variables are displayed with their names and in alphabetical order. To do so, select the following setting under the General tab of EditOptions: Variables are displayed alphabetically by names. Place the cursor in the box that contains the variables, and enter a character from the keyboard. The first variable beginning with this character will appear. This allows you to quickly search through the variable box to find a variable.

13 Slide 25 Create an additional histogram Slide 26

14 Working with Syntax Slide 27 Open a new syntax file through the menu: FileNewSyntax Data Editor Output Syntax-Editor *.sav files *.spv files *.sps files Slide 28 How do you get the command syntax? Option I: Perform an analysis through the menu Example: AnalyzeDescriptive StatisticsFrequencies Data Editor Output

15 Slide 29 Where is the syntax for this analysis? => The syntax is displayed in the output. Double-click the syntax part in the log, highlight and copy the syntax. Paste the syntax into the Syntax Editor. Slide 30 Option II: Paste the syntax directly from the dialog box ("Paste" button). Option III: Write the syntax yourself. Executing the Syntax Place the cursor inside the syntax in the syntax editor and run the analysis through the menu RunSelection.

16 Slide 31 Typical Syntax File Why should you use syntax? Rapidly leads to greater efficiency. Documentation Reproducing the results Automatically process many commands Allows access to all commands Communication with other persons Opens the world of macros What if the syntax is not displayed in the output? Through the menu EditOptionsKViewer, choose Display commands in the log Slide 32 The syntax is now displayed in the output.

17 Modifying Data Values Slide 33 The data may not always exist in a form that can be used for analysis or reporting. For example, you may want to: convert a scale variable into a categorical variable. merge different response categories into a single category. calculate a new variable from the difference between two existing variables. Slide 34 Computing a new variable New variables can be computed based on existing ones, for example by averaging scores, summing them up etc. For example you may want to compute the equivalence income (based on the household income and the number of persons in the household). TransformCompute VariableK Syntax COMPUTE income_equiv = income / SQRT(reside).

18 Slide 35 Recoding a variable Example: creating a categorical variable from a scale variable. For example, based on age in years we could build age categories. Menu: TransformRecode into Different VariablesK Syntax Slide 36 RECODE age (Lowest thru 24=1) (25 thru 44=2) (45 thru 60=3) (61 thru Highest=4) INTO age_r. FREQUENCIES VARIABLES=age age_r /ORDER ANALYSIS. Result Scale values (age) Categorical values (age_r) ==> : Categories 1: up to 24 years 2: years 3: years 4: over 60 years

19 Select Cases & Split File Select cases A particular subset of the data can be analyzed by selecting specific cases. Through this, all undesired cases of your data set are either temporarily or permanently deleted. For example, you may want to analyze only respondents who are older than 45 years. Menu: DataSelect CasesK Slide 37 Slide 38 Syntax Result USE ALL. COMPUTE filter_$=(age > 45). FILTER BY filter_$. EXECUTE. FREQUENCIES VARIABLES=age /FORMAT=NOTABLE /HISTOGRAM /ORDER=ANALYSIS. FILTER OFF. USE ALL. EXECUTE. These lines remove the "filter" for all analyses to come.

20 Split File Slide 39 Sometimes data in different categories should be analyzed separately. To do this, the data can be split up, and the same analysis can be performed on two or more datasets. For example, we could split the dataset by means of the variable age_r which means we are conducting separate analyses for each of the age categories. Menu: DataSplit FileK Syntax Result Slide 40 SORT CASES BY age_r. SPLIT FILE SEPARATE BY age_r. EXECUTE. FREQUENCIES VARIABLES=income /FORMAT=NOTABLE /HISTOGRAM /ORDER=ANALYSIS. SPLIT FILE OFF. This line removes the split for all analyses to come.

21 Data Entry Slide 41 There are different ways to enter data into SPSS. Data can be directly entered into SPSS or can be imported from many different sources: Direct: SPSS Data Editor From a spreadsheet program (such as Excel) From a database program (such as Access) From other applications (such as a text editor) Scanners may be efficient for entering large amounts of data. Data Editor: Defining Variables, Entering Data & Missing Values Slide 42 Entering (new) numerical data Open a new data file (through the menu FileNewData) At the bottom of the Data Editor window, switch to Variable View. Enter age in the first row of the first column. Enter marital in the second row. Enter income in the third row. New variables are automatically assigned the "Numeric" data type.

22 Slide 43 Switch to the Data View in order to enter values. To suppress the decimal place for the variables age, marital and income: At the bottom of the Data Editor window, switch to Variable View. Select the Decimals column and enter a 0 for age. Select the Decimals column and enter a 0 for marital. Adding variable labels and value labels Enter "Age in years" into the age cell of the "Labels" column. Do the same for "Marital Status", and so on. Slide 44 Select the Values cell for marital and open the dialog box. For Value, enter 1. For Label, enter "single". Click on Add so that this designation is registered.

23 Handling missing values In general, missing or invalid data should not be ignored. Sometimes survey participants refuse to answer particular questions. They may not know an answer, or may respond in an unexpected way. If these data are not identified or filtered out, your analysis may not yield correct results. Slide 45 Empty data cells, or cells that contain invalid input, are converted to missing values, which are displayed as a period. Slide 46 The reason why data is missing could be important for your analysis. For example, for a particular question, it could be useful to distinguish between those who refused to answer and those for whom the question was not applicable. In "Variable View" select the Missing cell for income and open the dialog box. In this dialog box you can specify up to three different missing values, either by defining a range of values, or particular single values.

24 Importing Data Slide 47 Data can be imported from different sources. Reading an SPSS Data File SPSS data files have a file extension of *.sav. Importing data from a spreadsheet In addition to entering data into the data editor, you can import from programs such as Microsoft Excel. The column headings serve as variable names. Importing data from a text file Text files are common sources of data. Many spreadsheet programs and databases can save their contents in text file format. For example, in CSV files, variables are separated with commas or tabs. Importing data from a database (not in this course) Data from a database can be imported with the help of a database wizard. Importing data from Spreadsheets Find the Excel file "demo.xls" in Slide 48 Column headings are variable names.

25 Slide 49 Open the Excel file through the SPSS File menu (Excel file must be closed) Importing data from a text file Find the text file "demo.txt" in Slide 50 Open the text file through the SPSS File menu (text file must be closed)

26 Slide 51 Exercise 01: First Part Introduction Slide 52 Ressources => => Exercises SPSS => Exercise 01

27 Analysis functions: Analyze Slide 53 Descriptive Statistics Summary information about the distribution, variability, and central tendency of variables. FrequenciesI Provides statistics and graphical displays for describing many types of variables. For a frequency report and bar chart, you can arrange the distinct values in ascending or descending order or order the categories by their frequencies. The frequencies report can be suppressed when a variable has many distinct values. You can label charts with frequencies (the default) or percentages. Statistics and plots: Frequency counts, percentages, cumulative percentages, mean, median, mode, sum, standard deviation, variance, range, minimum and maximum values, standard error of the mean, skewness and kurtosis (both with standard errors), quartiles, user-specified percentiles, bar charts, pie charts, and histograms. Menu FrequenciesI Slide 54

28 Example FrequenciesI FREQUENCIES VARIABLES=age /FORMAT=NOTABLE /STATISTICS=STDDEV VARIANCE MINIMUM MAXIMUM MEAN MEDIAN SKEWNESS SESKEW KURTOSIS SEKURT /HISTOGRAM /ORDER=ANALYSIS. Slide 55 DescriptivesI Displays univariate summary statistics for several variables in a single table Calculates standardized values (z scores). Variables can be ordered by the size of their means (in ascending or descending order), alphabetically, or by the order in which you select the variables (the default). When z scores are saved, they are added to the data in the data editor Statistics: Sample size, mean, minimum, maximum, standard deviation, variance, range, sum, standard error of the mean, and kurtosis and skewness with their standard errors. Slide 56

29 Example DescriptivesI DESCRIPTIVES VARIABLES=age /SAVE /STATISTICS=MEAN STDDEV MIN MAX. Slide 57 z scores are saved in the data editor ExploreI Produces summary statistics and graphical displays either for all of your cases or separately for groups of cases Use for: data screening, outlier identification, description, assumption checking, and characterizing differences among subpopulations (groups of cases). Slide 58

30 Example ExploreI EXAMINE VARIABLES=income BY gender /PLOT BOXPLOT /COMPARE GROUPS /STATISTICS DESCRIPTIVES /CINTERVAL 95 /MISSING LISTWISE /NOTOTAL. Slide 59 : Example ExploreI Numbers indicate cases in the dataset Slide 60

31 CrosstabsI Forms two-way and multiway tables and provides a variety of tests and measures of association Slide 61 Example CrosstabsI CROSSTABS /TABLES=gender BY inccat /FORMAT=AVALUE TABLES /CELLS=COUNT ROW /COUNT ROUND CELL. Slide 62

32 Inferential Statistics Slide 63 SPSS offers univariate and multivariate analysis techniques, including (among many others): General linear models (GLM) Survival analysis procedures Non-parametric procedures One-Sample t-test Use to test the claim that a population mean is equal to a specific value. Example: Test the hypothesis that in the population the mean age is 45 years. Hypothesis structure H 0 : µ = 45 years H A : µ 45 years Slide 64 SPSS output Mean of sample age = years The probability of the t-test is p =.000, assuming the null hypothesis H 0 can be rejected: The mean age in the population is significantly different than 45 years.

33 Two-Sample t-test Slide 65 Comparing two independent Means. Example: Test the hypothesis that the mean income of men and woman is different. Slide 66 Hypothesis structure H 0 : µ men = µ women H A : µ men µ women SPSS output Mean of men's income = [1000 $] Mean of women's income = [1000 $] When comparing groups, their variances must be relatively similar for the t-test to be used. Levene's test checks for this. If the significance for Levene's test is 0.05, then the row "Equal variances not assumed" is used > 0.05, then row "Equal variances assumed" is used Men and women in the population do not differ significantly in terms of their mean income. (t-test: df = 6398, t =.702, p =.483).

34 Slide 67 Paired Samples t-test Very often the two samples to be compared are not randomly selected: The second sample is the same as the first after some treatment has been applied. Example: Influence of diet on body weight of overweight men. Data set: body_weight.sav weight_0 = weight at beginning of diet weight_1 = weight after ¼ year Slide 68 Hypothesis structure H 0 : µ beginning = µ after H A : µ beginning µ after SPSS output Mean weight at beginning = [kg] Mean weight after ¼ year = [kg The mean weight at beginning of the diet is not significantly different from the mean weight after ¼ year of diet. (t-test: df = 98, t = , p =.229).

35 How to choose a statistical test? Use an inferential statistics decision-making tree! Slide 69 Tree from UZH (German) Creating charts Slide 70 Manuals This introduction refers to the manual "IBM SPSS Statistics 22 Brief Guide" Find this manual and also "GPL Reference Guide for IBM SPSS Statistics" here: www-01.ibm.com/support/docview.wss?uid=swg #en

36 Creating and Editing Charts SPSS provides a large number of options for producing charts and diagrams. The graphics options are available on the Graphs menu. Slide 71 Either use Chart Builder or Legacy Dialogs which are the old styled commands Bar Chart Self-Study 45 minutes Create a bar chart of mean income for different levels of job satisfaction. Slide 72 From the menus choose: GraphsChart Builder... Click OK

37 Exercise 02: Second Part Additional Topics Slide 73 Ressources => => Exercises SPSS => Exercise 02 Notes: Slide 74