Data Processing Flashcards

(48 cards)

1
Q

Systematic procedure to ensure that the information/data gathered are complete, _____and ______for analysis.

A

Data Processing
consistent
suitable

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2
Q

Stages of Data Analysis

A

Editing
Coding
Data Entry
Data Analysis

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3
Q

Purpose of the stages of data analysis

A

Error checking and verification

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4
Q

Data Preparation and Process

A
Prepare Preliminary Plan of Data Analysis
Check Questionnaire
Edit 
Code
Transcribe 
Clean Data
 Statistically Adjust Data
Select Data Analysis Strategiesa
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5
Q

Reasons for “Questionnaire Checking”

A
  • Parts of the questionnaire may be incomplete
  • The pattern of responses indicate that the respondent did not understand or follow the instructions
  • The responses show a little variance
  • One or more pages are missing
  • The questionnaire is received after the pre-established cutoff date
  • The questionnaire is answered by someone who does not qualify for participating
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6
Q

Transformingtheresearchdatacollectedinthefieldintocleantablesfordataanalysis.

A

Data Capture

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7
Q

In outline, the steps for Data Capture are?

A

-receipt of data by the study center (hardcopy forms or electronic files), followed by coding (if applicable), data entry/encoding(if applicable), and (computer) editing

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8
Q

Editing of responses include treatment of ______ ______

A

Treatment of Unsatisfactory Results

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9
Q

Under the treatment of Unsatisfactory Results

A
  • Returning to the field- interviewer re-contact the respondent
  • Assigning Missing Values- if returning is not feasible
  • Discarding Unsatisfactory Results
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10
Q

Reasons for Editing

A

Completeness
Consistency
Questions answered out of order

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11
Q

The process of checking and adjusting the Data

A

Data Editing

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12
Q

Data Editing is FOR??

A
  • omissions
  • legibility
  • consistency
  • -readying for coding and storage
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13
Q

is a process of examining the collected raw data to detect errors and omissions and to correct these when possible.
◉With regards to stages/Types of Editing::

A

Data Editing

  • Field Editing
  • Central Editing
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14
Q

Under Field Editing

A
  • Reviewing the accomplished data collection forms
  • Decoding of special symbols/abbreviations
  • Making callbacks/messages for verification/clarification of incomplete answers
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15
Q

Under Central Editing

A
  • Checking of inconsistencies and incorrect entries

- Checking of encoded data

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16
Q

Importance of Data Editing

A
  • Make corrections as early as possible
  • Reduce non-response or incomplete answers
  • Eliminate inconsistencies and incorrect info
  • Make the entries clear, legible, and comprehensive
  • Prepare data for analysis
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17
Q

What to check when editing data?

A

Check for duplicate entries
Check the total of each variable if the same as with the sample size
In qualitative data, check if categories are consistent specified in the coding manual
In quantitative data, check the maximum and minimum if they are logical given the possible values of variable

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18
Q

Conversion of verbal/written information into numbers which can be more easily encoded, counted, and tabulated.

A

Data Coding

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19
Q

The rules for interpreting,classifying,and recording data in the coding process

A

Codes

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20
Q

The actual numerical or other character symbols

21
Q

Types of Codes

A

Factual Code
Field Code
Bracket Code
Pattern Code

22
Q

actual value or information given by the respondent

23
Q

recorded as range of values rather than actual values

24
Q

codes are assigned to a list of categories of a given variable

25
applicable for questions with multiple response
Pattern Codes
26
Guidelines for coding unstructured questions
◉Number of rules must be kept to minimum(<8) ◉Codes should be exhaustive and mutually exclusive ◉Adopt coding convention for questions with similar answers
27
Coding Problems
◉No response | ◉Not applicable questions
28
A document which containsa record of all codes assigned to the responses to all questions in the data collection forms
Coding Manual
29
Minimum information that must be included in a coding manual
○Variable name ○Variable description ○Coding instructions
30
Under Data Preparation
Variable Codes | Missing Data
31
Under Variable Codes
- Each categorical variable must be exhaustive and mutually exclusive codes - These codes should be thoroughly documented in the codebook/coding manual - Where possible, use standard data codes to facilitate comparison results across the variables or even across the studies
32
Arise from many sources, such as refusal to answer,omission,missing by design,etc.
Missing Data
33
The coding of missing data often does not receive special____,which is often ____;indeed, cells left ___in Excel spreadsheets, will be interpreted as _____ ____ by most statistical programs.
attention benign empty missing data
34
MISSING DATA ◉When coded, missing values for categorical variables should be identified by a code ______different from non missing values. AND THIS IS UNDER THE?
Coding missing and inapplicable data | radically
35
a variable that would be coded as 0=‟not at all‟,1=‟a little bit‟,…,5=‟verymuch‟,a sensible choice would be to take a _____,e.g.-9.
negative value
36
◉**NOTE also that inapplicable(e.g.,pregnancy related data among males)data should be coded ______from missing data. ◉Codes for missing and inapplicable data can be entered in the ____ ____ ______ for ______variables.
differently variable label worksheet unformatted
37
Entering the data/responses in a spreadsheet
Data Encoding
38
Examples of Data Encoding
○MSExcel ○MSAccess ○EpiInfo
39
◉Checking data for errors
Cleaning Data
40
Cleaning data is "critical or NOT?" Explain your answer.
Critical- because “dirty” data can severely influence your results.
41
Three most commonly used in cleaning methods are (United Way of _____,19__):
United Way of America, 1996 1. Spot-checking 2. Eye-balling 3. Logic checks
42
Randomly select several participants’ completed paper surveys and compare them to the data on the electronic spreadsheet.
Spot checking
43
For qualitative data use this approach to check whether participants’ words were _____accurately and are ______to the right individual.
Spot checking transribed attributed
44
◉If there is an error in your first round of spot-checking you should ____check another round of the ___data.
randomly check | raw data
45
This technique involves reviewing the data for errors that may have resulted from a data-entry or coding mistake.
Eye balling
46
◉For example,question 5 from sample code book above reads:Did you participate in the summer program? Participants can only respond to this question with a“no”or“yes.” ◉“No”is assigned a value of 0, while “yes” responses are assigned a value of 1.Therefore, any number other than a 0 or 1 in the “Q5” column on the sample spreadsheet would be an obvious error. What cleaning method is this?
Eye balling
47
This technique involves a careful review of the electronically entered data to make sure that the answers to the different questions“make sense.”
Logic Check
48
For example, if participant 001 on the sample spreadsheet indicated that they did not attend the summer program in question 5, it would be illogical for this participant to have provided a satisfaction rating in the irresponse to question 6. ◉The only logical response for this participant would be “99” or “not applicable.” What cleaning method is this?
Logic Check