Quantifying Data

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

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

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

Cross Tabs

Used often with Bivariate data

Convention usually places

– “independent variables” across top in columns

– “dependent variables” in rows below

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

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

Data Management Process

concerned with the process by which raw data gathered by some instrument are converted into numbers for analysis purposes

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

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

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

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’

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)

“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

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. 

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

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

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

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

Types of Variables

Continuous:Continuous: increase steadily in tiny fractions

Discrete:Discrete: jumps from category to category

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

Bivariate Analysis

Bivariate analysisBivariate analysis focus on the relationship between two variables

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

Multivariate Analysis

Multivariate AnalysisMultivariate Analysis allow the separate and combined effects of the independent variable to be examined