Data Analysis in Excel and SPSS

Transcription

1 Data Organization & Creating Reproducible Analyses Nick Jackson University of Southern California Department of Psychology 09/20/2013

2 Overview Raw Data Organization Vocabulary Entering/Storing Data in Excel Data Checks/Reducing Errors Data Backup/Security Creating Reproducible Analyses Directory Structures Workflow for Projects (cleaning->archiving) Workflow for Analyses (cleaning->analysis- >present/archive)

3 Vocabulary Database An organized collection of data -Wikipedia Most of us don t actually use real databases SQL, Oracle, dbase, MS Access More Specifically: where the links between different data files are automatic (ie. no merging) We mostly store data in Excel or SPSS and pretend it is a database.

4 Vocabulary Wide File: One row of data per subject Pseudonym: Flat File Long File: more than one row of data per subject Pseudonyms: Relational File

5 Wide File: Primary Index Variables Cases An Observation Observations Long File: Primary Index Secondary Index Variable Cases An Observation Observations

6 Developing an Excel Database System Basic Rules No Mississippi tables Excel tables that contain ALL of your data at once can cause problems Enter Wide Data in wide format, Long Data in long format. One Excel File per survey/measure (not Sheets!)

7 Developing an Excel Database System-Variable Name Rules The primary (and secondary) index should have the EXACT same name in every file. Only the primary (and secondary) index should have the same names within and between files. Be consistent with variable names, keep them simple and free of unusual characters or spaces Use _ character to join two words together instead_of_a_blank_space. Never leave a blank column between variable names-this causes random sorting.

8 ESS: Primary Indices Don t Match: id Id Inconsistent Naming: Underscore _ should be removed OR other variables should have underscores. Inconsistent Capitalization: These should start lower case, OR all other variables should be capitalized PSQI: Blank Column: There should be no empty columns between variables Improper Spaces: Variable names shouldn t have spaces in it.

9 Types of Data

10 Types of Data-Considerations Default formatting in excel is General which is fine for everything but dates. Collect discrete variables as continuous when possible. DOB vs Age vs age groups. NEVER enter data as already reverse coded. Only use strings when you cannot assign a number

11 Types of Data-Considerations Be consistent with entering string data Medications: Aspirin vs ASPIRIN vs Apsirin Use Alphanumeric codes when possible (ATC\DDD) Time and Date data Use the correct formatting 12:01 PM 09/20/2013

12 Handling Missing Data Have indicators of missing data Use numbers as the indicator NOT a possible real value in any excel sheet Typically -9 or -99 or -999 Not for String/Dates/Times

13 Handling Unknown/Don t Know/NA Responses Same rules as missing data indicator Usually -8 or -88 or -888

14 The Data Dictionary Key Elements Variable Name the name exactly as it appears in the data Variable Label- a lengthier description of the variable Variable Values-tells you what discrete values mean (ie. 2=Male, -99=Missing)

15 Data Dictionary

16 Reducing Errors in Data Entry Data Validation tools in Excel Restrict possible values

17 Using Excel Data Validation Tools to Restrict Variable Values Step 1: Highlight Variables/Columns. Select Data Tab, Click on Data Validation B) Select Data Tab C) Click on Data Validation A) Highlight Variables Step 2 (For Discrete Data): Select to Allow: List. Input the potential values for the variable (including the missing data indicator) in the Source: box D) Select to Allow: List E) Input Potential Values

18 Step 2 (For Interval/Ratio Data): Select to Allow: (Whole Number OR Decimal). Determine the criteria for the Data Box (Between or Not Between) Input Minimum and Maximum values. D) Select to Allow: Whole Number OR Decimal F) Select Appropriate Minimum and Maximum E) Choose Between OR Not Between from Data: box

19 Reducing Errors in Data Entry Use Data validation tools in Excel Enter with a number pad % Error Rate Use split windows to keep case numbers and variable names visible

20 Splitting windows in Excel Click on this, and drag the window down

21 Splitting windows in Excel

22 Reducing Errors in Data Entry Use Data validation tools in Excel Enter with a number pad Use split windows to keep case numbers and variable names visible Use Column Borders to denote separate physical pages of your survey within your excel file.

23 Applying a border in Excel Select this Then this Which will produce.this!

24 Checking for Errors Use the excel data filters Re-enter data because No hand-entry method protects against row shifts Acceptable, but incorrect values can be present In general, a 10% re-entry.

25 Errors Happen.

26 Backing up Your Data Should exist in at least 2 physically separate locations. Regular backups Easy to reconcile versions Servers, Clouds, RAID Drives? Dropbox, Skydrive, Google Drive: Not HIPPA or FERPA compliant Back-up of your data every time you change something in the raw data.

27 Data Security Protecting the Identity of your subjects Assign ID s that have NO significance to any identifying characteristic of the subject Identifying characteristics: names (initials), dates (of birth, death), years, addresses, phone number, , zip codes, geo-codes, SSN, MRN, VIN, DL #, License Plate, IP address, health plan/insurance numbers Never store Identifying characteristics in files with your other data.

28 Data Security Keeping people out of your data Physical Threats Use anti-theft devices (cables) BIOS Password on the hard drive + Logon Password Remote wipes Cyber Threats 2-step verification for any cloud/ services Traffic encryption, VPNs Servers should have restricted access Also how often backups sent offsite.

29 Data Security You can encrypt your excel file. 1) Click the Office button. 2) Scroll over the Prepare button. 3) Click on Encrypt Document.

30 Creating Reproducible Analyses Without replication we cease to have science can you replicate your own findings? Is everyone reinventing the wheel with your rawdata? Journals are beginning to require submission of rawdata and syntax The NIH recently declared that the sharing of data is essential for translating research into knowledge and products that improve health. As such, all investigators seeking more than $500,000 in grant support per year are now required to include a plan for data sharing Savage & Vickers 2009

31 Creating Reproducible Analyses The difficultly of replication Can you find it? Issues of version control (multiple versions) Everything is labeled final Undocumented steps Don t end up on here:

32 Creating Reproducible Analyses Data Workflow (Long, 2009) Clean -> Analyze-> Present-> Archive Replication is our guiding principle Simple Complex systems don t get used (usability) High efficiency Standardized Create a template Be consistent Scalable Easy to do with 2 or 200 (automation)

33 Setting up a directory structure Standardized directory structures (templates) Easy to find things

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35 The naming of data files in your directories 2-4 letter acronym_nameyyyymmdd Ensures proper sorting and version control

36 Data Workflow Project Level (Data Manager) Clean - > Archive Analysis Level (Data Analyst/Researcher) Clean -> Analyze -> Present / Archive

37 Data Workflow: Project Level (Cleaning) Clean Data isac_master_widedate.sav isac_master_longdate.sav Merge: cesd, bdi, wasi Programs setup.sps Rawdata isac_cesd xlsx isac_bdi xlsx isac_wasi sav

38 Data Workflow: Project Level (Cleaning) Programs: Setup Cleans and aggregates the files

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40 Data Workflow: Project Level (Archive) Rawdata isac_cesd xlsx isac_bdi xlsx isac_wasi sav isac_cesd xlsx Archive

41 Data Workflow Analysis Level Clean -> Analyze -> Present /Archive

42 Data Workflow: Analysis Level A reminder of the directory structures for the Analysis Contents of my \Analysis folder:

43 Data Workflow: Analysis Level (Cleaning) Method 1 Analysis Example For Presentation (Jackson) Analysis Data Analysis Rawdata isac_analysisdate.sav Syntax cleaning.sps Clean Data isac_master_wide sav isac_master_long sav Rawdata isac_cesd xlsx isac_bdi xlsx

44 Data Workflow: Analysis Level (Cleaning) Method 2 Analysis Example For Presentation (Jackson) Clean Data Analysis Data Analysis Rawdata Syntax cleaning.sps isac_analysisdate.sav I use this folder only for rawdata files that are unique to the specific analysis isac_master_wide sav isac_master_long sav Rawdata isac_cesd xlsx isac_bdi xlsx

45 Data Workflow: Analysis Level (Analyze) Analysis Example For Presentation (Jackson) Analysis Data Analysis Rawdata isac_analysisdate.sav Syntax cleaning.sps analysis.sps

46 Data Workflow: Analysis Level Syntax: Cleaning.sps Cleans and aggregates the files Analysis.sps Conducts the analysis Makes NO changes to the data DOES NOT write out any files

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48 Data Workflow: Analysis Level (Present/Archive) You have analyzed your data and put together a paper, abstract, poster, etc. ARCHIVE your syntax on the date of submission Put dates at the end of the syntax file DO NOT TOUCH THE SYNTAX FILE AGAIN (I usually comment out the commands) Create Abstact.sps syntax file

49 Data Workflow: Analysis Level (Present/Archive) Analysis Example For Presentation (Jackson) _Abstract Analysis Data apsabstract_ docx isac_analysis sav Syntax cleaning sps analysis sps abstract sps cleaning.sps analysis.sps Add dates to Syntax Create abstract Syntax file Probably still analyzing stuff for creating abstract into a poster, so I keeping working versions of my needed syntax files

50 Data Workflow: Analysis Level (Present/Archive) I generally do not archive my analysis folders, but rather focus on archiving the syntax when major events happen in an analysis (submissions, revisions, acceptance).

51 The important things Never overwrite a rawdata file. Every change to your rawdata should be a new file with a new date. Files are only opened or saved with syntax All changes to files are done with syntax Cleaning/Setup Syntax: We read files in and write separate files out Analysis Syntax: We read files in and NEVER write or change files

52 The important things When important things happen, we archive the SYNTAX by commenting out all of the commands and placing dates on the end.