Tips for Data Entry, Storage, & Quality Control

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1 Tips fr Data Entry, Strage, & Quality Cntrl Data Frmats Cmmn file frmats fr data entry and their advantages: MS Excel almst everyne has MS Excel thers can easily view yur data file and/r help with data entry SPSS can add mre identifying infrmatin abut the variables mre sphisticated statistical analyses are easily available MS Access mre cntrl ver the data that is entered t prevent errrs can enter data in survey frmat and have it create the data file Determining the best frmat fr data entry T cnduct quantitative analyses, yu may be limited if data is nt in numerical frmat. Statistical analyses cannt be run frm a table in a wrd dcument and will usually require the data t be in MS Excel, SPSS, r anther database frmat. If yu want t d cmplex statistical analyses, yu will want t use a statistical package (such as SPSS). The frmat used in SPSS is nt always cnducive t qualitative analyses. T allw fr srting and merging f files, each participant shuld have a numeric ID number. Many data analysis prgrams have a limited ability t read each ther s data. It can ften be dne (usually by imprting a file frm anther frmat r exprting data t a new frmat), but yu need t be vigilant abut checking fr errrs. Fr example, the prgram might try and guess the variable type and get it wrng. Always check the data after imprting r exprting files! 1

2 Tips fr Data Entry Create a system t keep track f which data yu have already entered int the data file and which still need t be entered. Always use an identifier t link data in the electrnic file t the paper cpy. Data entry ften requires sme decisin-making. Keep a recrd lg f the issues that arse, hw yu chse t handle the situatin, and which participants it applies t. Keep a recrd lg f the issues that arse, hw yu chse t handle the situatin, and which participants it applies t. Data entry can take time. Remember t set aside time fr it in yur wrk plan/timeline. Data Strage and Retentin Data strage It is ur duty t ensure the identity f the participants is prtected and that the infrmatin prvided is kept cnfidential. Best t stre data: Inside a secure building and pssibly even inside a lcked cabinet. NOT in smene s hme r left in the car With passwrd prtectin and/r encryptin n electrnic files r memry devices Data retentin Never destry data (paper cpies) r data files (electrnic) at least until the final reprt has been accepted. Sme grants and/r rganizatins may have regulatins abut the duratin the data is t be stred and hw it is t be destryed upn cmpletin. 2

3 Number f Peple Basic Quantitative Analysis Techniques Descriptive statistics can be used t give yu a sense f the data: N (number f participants) Mean (average) Standard Deviatin (measure f variability) Minimum, Maximum Crsstab (hw many f a certain categry answered in a certain way) Frequencies (# f times yu gt a certain respnse) Tests fr significance A result is called statistically significant if it is unlikely t have ccurred by chance. Recall that yu nly ever test a sample (a small grup) wh are meant t represent everyne in a ppulatin (a larger grup). Because yu randmly select peple, their scres will never be exactly what the whle ppulatin s scre wuld be. Significance testing gives yu an idea f the prbability f yur result. Ppulatin Sample Lw scres High scres Cautins: The term significant des nt mean imprtant r meaningful, as it des in everyday speech. Yu can have a statistically significant difference between tw grups, but the difference is s small that it wuld nt make sense t change yur prgram because f it. Statistical significance des nt imply that yur thery is true. Crrelatin des nt imply causatin! The p-value is nt an indicatr f the strength f the relatinship ( effect size indicates strength). 3

4 Number f Peple Number f Peple Basic Quantitative Analysis Techniques Several imprtant factrs cntribute t the prbability f yur result: Hw much variability there is in data (are mst peple s scres similar, r is there a wide range f scres?) Hw many peple are included in the sample. Variability in data VS Lw scres High scres Lw scres High scres Sample size VS If yu tested a lt f peple and there isn t a lt f variability in the data, then yur grup s scres are likely t be clser t the true ppulatin scres. Hw prbable des it have t be t be significant? The p-value is the prbability f finding the result yu did by chance. Traditinally use a cut-ff pint t say whether a result is significant p <.05; less than 5% prbability f btaining the result yu did withut a true difference p <.01; less than 1% prbability f btaining the result yu did withut a true difference In the statistical wrld, yu cannt say a result with p >.05 is significant, even if the p- value is.051 (sme peple might call this marginally significant r say there was a trend in a certain directin.) 4

5 Basic Qualitative Analysis Techniques Qualitative data analysis is recgnizing patterns in yur data and turning patterns int meaningful ideas. It is essentially the develpment f schemes t rganize and manage yur data, which is dne thrugh cding, categrizing, classifying and identifying themes. Cding Qualitative Data Cding is ften the first step in qualitative data analysis. It is part f data reductin and is generally dne t make yur data mre manageable. It is als ften used with ther frms f data analysis such as a thematic analysis. Cding typically invlves a tw step prcess: 1. Develping cdes/categries Cdes can be generated frm data cllectin tl r data r bth. Generating cdes frm yur data (e.g., interview transcripts, dcuments, etc.) will typically invlve reading thrugh yur data and making ntes n what can be dne with different parts f the data. Cnsider bth reccurring regularities and deviant cases. If mre than ne persn is invlved in yur research it is useful t develp cdes independently and then cmpare and cmbine them. Develp a cding sheets/frm. 2. Cding data This is a mre systematic review f yur data. It invlves ging thrugh yur data and filling ut a cding sheet fr each case (e.g., each interview, dcument, etc.). Cding can be an iterative prcess. As yu are cding yur data yu may find that yu need t g back and update/revise yur cding sheet. It is ften helpful t identify qutes r examples t illustrate the idea the cde is meant t capture. Cding can be used t generate basic quantitative data by cunting the frequency f specific cdes. 5

6 Basic Qualitative Analysis Techniques Thematic Analysis A thematic analysis essentially invlves identifying brad themes r mre cmplex patterns in yur data. It will ften invlve: Recgnizing relatinships and linkages within yur data r between yur cdes/clusters f cdes Identifying prcesses and utcmes Determining substantive significance Interpretatin f meaning This is dne by lking acrss cases, whereas cding is typically dne n a case-by-case basis. Yur cded data can help identify themes but yu may als have t revisit yur data t fully develp yur themes. Themes are identified frm yur data but they may be influenced by yur evaluatin bjectives. When identifying themes, it is ften useful t use diagrams r matrices t help understand the linkages and relatinships in yur data. Useful display tls: Cncept maps (smetimes referred t as cgnitive maps) Prcess maps Matrix Taxnmies Similar t cding, it is helpful t identify qutes r examples that illustrate yur themes. 6

7 Techniques fr Data Quality Cntrl Expert Cnsultatin Check with peple wh are knwledgeable n yur tpic area (e.g., smene in yur department, a service prvider, smene with experience ding a similar evaluatin) Briefly describe what yu have dne and yur results see if it makes sense t them. Pilt Testing Cnduct a trial run with a small grup first t make sure everything is wrking the way yu expected (e.g., peple are interpreting the questins crrectly, instructins are clear, etc.) Mnitring Data Cllectin Onging watching t make sure data is being cllected prperly, checking t see if data cllectrs / participants have cncerns, ensuring everyne understands and remembers instructins. Checking Frequencies & Transcriptins Spt check t make sure data entry is crrect (e.g., there are nt big chunks missing, that the descriptive statistics are reasnable, etc.). Listen t audi recrdings while rereading transcripts t make sure they are accurate. Peer Review f Cding Schemes and Cdes Check with ther experts abut methdlgy / apprach. Data Cleaning Must set criteria fr data t be included r excluded (t much missing) and stick t them! Save the data file with all participants and make a new data file that nly includes the smaller sample. It s als imprtant t check that yur variables are in the crrect frmat be particularly careful abut dates! This prcess sunds easy, but can take a substantial amunt f time. Reliability Checks fr Cding If multiple peple are cding, have everyne cde a subsample (10 r 15) and make sure there is agreement n hw the items are cded. Prepared By Alberta Health Services Addictin and Mental Health Cmmunity Treatment and Supprt Knwledge and Strategy 7

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