Organizing Your Data. Jenny Holcombe, PhD UT College of Medicine Nuts & Bolts Conference August 16, 3013

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1 Organizing Your Data Jenny Holcombe, PhD UT College of Medicine Nuts & Bolts Conference August 16, 3013

Learning Objectives Identify Different Types of Variables Appropriately Naming Variables Constructing a Variable Code Book Developing Excel

2 Learning Objectives Identify Different Types of Variables Appropriately Naming Variables Constructing a Variable Code Book Developing Excel Spreadsheets & Effectively Entering Data Identifying Differences between: Descriptive & Inferential Statistics Parametric & Nonparametric Statistics 2

3 TYPES OF VARIABLES 3

can be measured Operational Definition a definition of the

4 Variables A characteristic or condition that changes or has different values for different individuals Anything that can be measured Operational Definition a definition of the variable in terms of how, specifically, it is to be measured 4

5 Qualitative Variables Differ in kind rather than amount Differ in quality, not quantity or magnitude Also referred to as categorical or nominal Examples favorite color, treatment group, gender, race 5

Quantitative Variables Assigned number values that represent differing quantities of the characteristics Examples medication dosage, # of doctor visits, annual income Quantitative data can

6 Quantitative Variables Assigned number values that represent differing quantities of the characteristics Examples medication dosage, # of doctor visits, annual income Quantitative data can either be: Discrete a finite number of values (i.e., # of doctor visits last year) Continuous infinite continuum of possible real number values (i.e., # of minutes it takes to finish a book) 6

Quantitative Variables Three types of quantitative variables: Ordinal categorical scales that have a natural ordering of values (i.e., SES Class low, middle, high) Interval distances between adjacent scores are equal & consistent throughout the scale with no absolute zero point (i.

7 Quantitative Variables Three types of quantitative variables: Ordinal categorical scales that have a natural ordering of values (i.e., SES Class low, middle, high) Interval distances between adjacent scores are equal & consistent throughout the scale with no absolute zero point (i.e., IQ scores, temperature) Ratio same as interval with a true zero point (i.e., length, distance, time) 7

8 Variables Final Points It is possible to measure data on more than one scale Variables should always be measured on the highest scale possible Ratio Interval Ordinal Nominal 8

9 Fictitious Data Four measurement levels for daily amount of sodium intake Participant Ratio - Actual mg Interval - Values above 2500mg Ordinal - Rank order Alan Nathan Chris Mike Vadim Daniel Nominal - 1=not high 2=high Source: Polit, D. F. (2010). Statistics and data analysis for nursing research. (2 nd ed.). Boston: Pearson. ISBN:

10 NAMING VARIABLES 10

11 Naming Variables The first row should include variable names this makes transfer to other programs easier (i.e., SPSS, SAS) Variable names can be up to 32 characters in length but anything more than 8-12 becomes very cumbersome to manage Each variable name must be unique; duplication is not allowed & names are not case sensitive 11

Naming Variables Variable names should begin with a letter Avoid periods, #, @, $, and only use underscores within the variable name (not at the beginning or

12 Naming Variables Variable names should begin with a letter Avoid periods, $, and only use underscores within the variable name (not at the beginning or end) No spaces are allowed in variable names Use meaningful names for variables Makes variables more self explanatory Some exceptions balance length/meaning 12

13 Naming Variables Acceptable Names Q1; Q_1 Question1; Question_1 Q1_food Food DRS1; DRS_1 Unacceptable Names Q 1; 1Q; Q-1 Question 1; Question-1 Q1 food; Q1-food _Food_ DiabetesRiskScale1 The main thing is to be consistent when naming variables 13

Naming Variables Examples http://www.ciser.cornell.

14 Naming Variables Examples What is wrong with this file? 14

15 CONSTRUCTING A VARIABLE CODE BOOK 15

16 Variable Code Books Purpose: To create a data entry system To assist with data entry For statistical analysis When archiving data files for follow-up 16

17 Code Book Construction Elements to include: 1) Description of the Study 2) Sampling Information 3) Technical Information 4) Structure of the Data Variable Name Variable Label Value Labels 5) Text of the Questions/Survey Instrument 17

Code Book Construction Word or Excel format is acceptable A columned list or table is acceptable All variables should be included with appropriate labeling information

18 Code Book Construction Word or Excel format is acceptable A columned list or table is acceptable All variables should be included with appropriate labeling information Variable labels can be any length but no longer than 256 characters is recommended The variable labels can contain spaces & characters not allowed in variable names 18

19 Code Book Examples Polit (2010) Data Files Swedish Institute for Social Research ACHA NCHA II 19

20 Code Book Final Points Be consistent in your coding! Update the code book as you enter your data if you make a change while entering your data, make sure you update your code book as well Check & double check your code book acts as a form of communication between you and your data analyst (and possibly between you & your future self!) 20

21 DEVELOPING EXCEL SPREADSHEETS & DATA ENTRY 21

22 Proper Data Layout Allows you the ability to: Combine data Separate data Create charts that give insight into what the raw data has to say When you enter your data without consideration of how you will use the data later, it becomes much more difficult to conduct any data analysis 22

Excel Basics Each individual row of data is known as a record, an observation, a case Do not leave any blank rows There cannot be information about an item in more

23 Excel Basics Each individual row of data is known as a record, an observation, a case Do not leave any blank rows There cannot be information about an item in more than one row Each column is a field labeled to identify the data it contains All data in each column should be formatted the same Do not leave blank columns in the table 23

24 Excel Basics Once a database is created you can use Excel tools to manage the data Sorting Data Filtering Data 24

for the variable in question Excel will recognize these missing

25 Missing Values Should be entered consistently use 9 or 99 or 999 The value should be something that cannot represent a real numeric value for the variable in question Excel will recognize these missing values as real values so be careful if you are using Excel for analysis 25

Additional Points Ensure rows below data are not activated so they are not mistaken during transfer as additional cases/observations Numeric values are always best

26 Additional Points Ensure rows below data are not activated so they are not mistaken during transfer as additional cases/observations Numeric values are always best to use for data entry regardless of the type of variable (quantitative vs. qualitative) Values/labels can always be assigned in a code book or data analysis program 26

27 DESCRIPTIVE VS. INFERENTIAL STATISTICS 27

Descriptive vs. Inferential Descriptive Statistics Used to summarize, organize, and simplify data for better understanding Means, standard deviations, percents, frequencies, proportions, etc.

28 Descriptive vs. Inferential Descriptive Statistics Used to summarize, organize, and simplify data for better understanding Means, standard deviations, percents, frequencies, proportions, etc. Inferential Statistics Statistical procedures that allow researchers to study samples & then make generalizations about the population from which they were selected Allows the researcher to draw conclusions 28

29 Descriptive Statistics in Excel This is the status bar. It will display various information about a selected set of values in the spreadsheet. To change the information displayed you simply right click on the status bar. 29

30 Central Tendency in Excel The AVERAGE Function Calculates the arithmetic mean =AVERAGE(A1:A100) The MEDIAN Function Calculates the median (center value) =MEDIAN(A1:A100) The MODE Function Calculates the most frequently occurring value =MODE(A1:A100) 30

31 Variability in Excel There is no range function in Excel, but =MAX(A1:A100) MIN(A1:A100) The VAR Function Calculates sample variance =VAR(A1:A100) The STDEV Function Calculates sample standard deviation =STDEV(A1:A100) Remember: STDEV = sqrt(var); STDEV 2 = VAR 31

32 PARAMETRIC VS. NONPARAMETRIC STATISTICS 32

Parametric Statistics A class of inferential statistical tests that involves: assumptions about the distribution of the variables, the estimation of a parameter, and usually the use of

33 Parametric Statistics A class of inferential statistical tests that involves: assumptions about the distribution of the variables, the estimation of a parameter, and usually the use of interval or ratio measures Statistical tests designed to be used when data have certain characteristics when they approximate a normal distribution & are measured with interval or ratio scales 33

Parametric Statistics Bivariate One-sample test

measures ANOVA Pearson s product moment correlation

ANCOVA MANOVA MANCOVA Mixed design RM-ANOVA Canonical

34 Parametric Statistics Bivariate One-sample test Two-sample test Analysis of variance (ANOVA) Repeated measures ANOVA Pearson s product moment correlation (r) Multivariate Multiple correlation/regression ANCOVA MANOVA MANCOVA Mixed design RM-ANOVA Canonical analysis Discriminant analysis Logistic regression Factor analysis 34

Nonparametric Statistics A general class of inferential statistical tests that does not involve rigorous assumptions about the distribution of the variables; most often used with small samples, when

35 Nonparametric Statistics A general class of inferential statistical tests that does not involve rigorous assumptions about the distribution of the variables; most often used with small samples, when data are measured on the nominal or ordinal scales, or when a distribution is severely skewed Statistical tests that are designed to be used when data being analyzed depart from the distributions that can be analyzed with parametric statistics 35

test Mann-Whitney U test Kruskal-Wallis test Wicoxon signed ranks

36 Nonparametric Statistics Chi-square goodnessof-fit test Chi-square test of independence Fisher s exact test McNemar test Cochran s Q test Mann-Whitney U test Kruskal-Wallis test Wicoxon signed ranks test Friedman test Spearman s rank order correlation Kendall s tau 36

categories 1. Tests of differences between groups (independent samples) 2.

37 Comparison of Parametric & Nonparametric Statistics There is at least one nonparametric test equivalent to a parametric test These tests fall into several categories 1. Tests of differences between groups (independent samples) 2. Tests of differences between variables (dependent samples) 3. Tests of relationships between variables 37

runs test Mann Whitney U test Kolmogorov- Smirnov two sample test Multiple groups

38 Differences Between Independent Groups Two groups/samples compare mean value for some variable of interest Parametric t-test for independent samples Nonparametric Wald-Wolfowitz runs test Mann Whitney U test Kolmogorov- Smirnov two sample test Multiple groups Parametric Analysis of Variance (ANOVA/MANOVA) Nonparametric Kruskal-Wallis analysis of ranks Median test 38

matched pairs test If more than two variables are measured in same sample Parametric

39 Differences Between Dependent Groups Compare two variables measured in the same sample Parametric t-test for dependent samples Nonparametric Sign test Wilcoxon s matched pairs test If more than two variables are measured in same sample Parametric Repeated measures ANOVA Nonparametric Friedman s two way analysis of variance Cochran Q 39

Spearman R Kendal Tau Coefficient Gamma Chi-Square Phi

40 Relationships Between Variables Two variables of interest are categorical Parametric Correlation coefficient Nonparametric Spearman R Kendal Tau Coefficient Gamma Chi-Square Phi coefficient Fisher exact test Kendall coefficient of concordance 40

41 Parametric vs. Nonparametric Parametric Assumed Distribution Normal Any Assumed Variance Homogenous Any Nonparametric Typical Data Ratio or Interval Ordinal or Nominal Data Set Relationships Independent Usual Central Measure Mean Benefits Can draw more conclusions Any Median Simplicity; Less affected by outliers 41

42 EXCEL ANALYSIS TOOLPAK 42

43 More Statistics Using Excel To get more statistics power from Excel, you need to add in the Analysis ToolPak Refer to the screenshots on the next few pages I followed this process in my version of Excel 2007 and had no trouble adding the ToolPak 43

44 Analysis ToolPak Allows you to conduct: Summary descriptive statistics Correlation Histograms Rank & Percentile Regression z-tests t-tests ANOVAs 44

45 Add in the Analysis ToolPak Click the Microsoft Office button, then Excel Options 45

46 Add in the Analysis ToolPak Click Add ins. In the Manage box, select Excel Add ins. Click Go 46

47 Add in the Analysis ToolPak Click the checkbox for the Analysis ToolPak, then OK Install it if it is not installed When you have added it in, it will appear on the Data tab all the way on the right hand side of your screen 47

48 Questions? Jenny Holcombe, PhD UTC School of Nursing (423)

Nuts and Bolts Research Methods Symposium

Nuts and Bolts Research Methods Symposium Organizing Your Data Jenny Holcombe, PhD UT College of Medicine Nuts & Bolts Conference August 16, 3013 Topics to Discuss: Types of Variables Constructing a Variable Code Book Developing Excel Spreadsheets