Quantifying Data Data Entry Define 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 Cleaning Reread 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 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 attribute frequently occurring used with nominal variables i e sex most respondents were women usually report with percentage women 60 were Cross Tabs Used often with Bivariate data Convention usually places independent variables top in columns across dependent variables in rows below Coding and data entry options Transfer sheets are special forms ruled off in 80 columns Edge coding involves recording code s in margins of questionnaires Direct data entry involves entering data directly into computer eliminating transfer sheets Data entry by interviewer CATI Optical 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 instrument with data gathering 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 preliminary code based on responses a 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 analysis Univariate 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 Statistics Techniques 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 Analysis Involves examination of the distribution of cases on only ONE variable at a time Frequency distributions are listings of the number of cases in each attribute of a variable Ungrouped frequency distribution Grouped frequency distribution Proportions express number of cases of the criterion variable as part of the total population frequency of criterion variable divided by N Percentages are simple 100 X proportion Or 100 X frequency of criterion variable divided by N Rates make comparisons more meaningful by controlling for population differences Measures of Central Tendency Measures of central tendency reflect the central tendencies of a distribution Mode reflects the attribute with the greatest frequency Median reflects the attribute that cuts the distribution in half Mean reflects the average sum of attributes divided by of cases Measures of Dispersion Measures of spread or distribution dispersion reflect distribution of the the Range is the difference between largest smallest scores high low Variance is the average of the squared differences between each observation and the mean Standard deviation is the square root of variance Types of Variables Continuous increase steadily in tiny fractions 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 analysis focus on the relationship between two variables Contingency Tables Format 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 Analysis allow the separate and combined effects of the independent variable to be examined