quantifying data. data entry define variables, enter case data, conduct runs coding and recoding...
TRANSCRIPT
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Quantifying Data
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Data EntryDefine variables, enter case data, conduct runs
Coding and Recoding
– If numeric values not pre-assigned, decide on coding system
– If there is open-ended data, would need to decide how to deal with responses
Defining your variables
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Data CleaningReread each set of responses back (immediately)
to confirm accuracy
“Possible-code cleaning”– easiest way to check is to run a frequency
distribution
Contingency cleaning– On the “if” questions
“Sort” by response – do you recycle… then check the “what do you
recycle” variable
Can also run cross tabs and make sure cells are empty
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Basic Analysis – Measures of Central TendencyBasic Analysis – Measures of Central Tendency Mean: sum of values divided by the
number of cases– simple average
Median: middle attribute in a list of observed attributes – extreme cases eliminated
Mode: most frequently occurring attribute– used with nominal variables, i.e.. sex
• most respondents were women• usually report with percentage, 60% were
women
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Cross Tabs
Used often with Bivariate data
Convention usually places
– “independent variables” across top in columns
– “dependent variables” in rows below
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Coding and data entry options
Transfer sheetsTransfer sheets are special forms ruled off in 80 columns
Edge codingEdge coding involves recording code #'s in margins of questionnaires
Direct data entryDirect data entry involves entering data directly into computer; eliminating transfer sheets
Data entry by interviewerData entry by interviewer (CATI)
Optical scan sheetsOptical scan sheets
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Coding
What is it? – It is the assignment of numerical values to
information or responses gathered by a research instrument
Codebook: describes the locations of variables and lists the codes assigned to the attributes of the variables
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Data Management Process
concerned with the process by which raw data gathered by some instrument are converted into numbers for analysis purposes
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Collect information with data gathering instrument
Use codebook to transfer this information to a transfer sheet or code sheet (optional)
Create data file from information on code sheet by entering data from a computer keyboard
Check/clean up data file for accuracy – Data cleaning done by– Computer edit programs – Examine distributions – Contingency cleaning
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What about open-ended items?
– Read through responses a create a preliminary code based on responses
– If more than 10% of responses fall into "other" category, code needs to be revised to include many of these responses
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Elementary Quantitative Analyses
To understand the meaning of univariate, bivariate, and multivariate analysis
To become familiar with the meaning of several univariate and bivariate statistics
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Analysis Strategies
Why do we have to have them?
– People who read our ‘research’ are interested in the highlights
– Should try to communicate findings in an understandable and ‘painless fashion’
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Three types of analysisUnivariate analysis
– the examination of the distribution of cases on only one variable at a time (e.g., college graduation)
Bivariate analysis – the examination of two variables
simultaneously (e.g., the relation between gender and college graduation)
Multivariate analysis – the examination of more than two variables
simultaneously (e.g., the relationship between gender, race, and college graduation)
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“Purpose”
Univariate analysis
– Purpose: description
Bivariate analysis
– Purpose: determining the empirical relationship between the two variables
Multivariate analysis
– Purpose: determining the empirical relationship among the variables
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Types of StatisticsTechniques that summarize and describe
characteristics of a group or make comparisons of characteristics between groups are knows as descriptive statistics.
Inferential statistics are used to make generalizations or inferences about a population based on findings from a sample.
The choice of a type of analysis is based on the evaluation questions, the type of data collected, and the audience who will receive the results.
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Univariate AnalysisInvolves examination of the distribution
of cases on only ONE variable at a time
Frequency distributionsFrequency distributions are listings of the number of cases in each attribute of a variable– Ungrouped frequency distribution– Grouped frequency distribution
ProportionsProportions express number of cases of the criterion variable as part of the total population; frequency of criterion variable divided by N
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Percentages Percentages are simple 100 X proportion – Or [100 X (frequency of criterion
variable divided by N)]
RatesRates make comparisons more meaningful by controlling for population differences
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Measures of Central Tendency
Measures of central tendencyMeasures of central tendency reflect the central tendencies of a distribution
– ModeMode reflects the attribute with the greatest frequency
– Median Median reflects the attribute that cuts the distribution in half
– MeanMean reflects the average; sum of attributes divided by # of cases
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Measures of Dispersion
Measures of dispersionMeasures of dispersion reflect the spread or distribution of the distribution
– RangeRange is the difference between largest & smallest scores; high – low
– VarianceVariance is the average of the squared differences between each observation and the mean
– Standard deviationStandard deviation is the square root of variance
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Types of Variables
Continuous:Continuous: increase steadily in tiny fractions
Discrete:Discrete: jumps from category to category
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Subgroup Comparisons
Somewhere between univariate & bivariate, are Subgroup Comparisons
Present descriptive univariate data for each of several subgroups– Ratios: compare the number of
cases in one category with the number in another
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Bivariate Analysis
Bivariate analysisBivariate analysis focus on the relationship between two variables
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Contingency TablesFormat: attributes of independent
variable are used as column headings and attributes of the dependent variable are used as row headings
Guidelines for presenting & interpreting contingency tables – Contents of table described in title – Attributes of each variable clearly described – Base on which percentages are computed
should be shown – Norm is to percentage down & compare across– Table should indicate # of cases omitted from
analysis
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Multivariate Analysis
Multivariate AnalysisMultivariate Analysis allow the separate and combined effects of the independent variable to be examined