Data Processing, Fundamental Data Analysis, and the Statistical Testing of Differences Chapter Twelve

Develop an understanding of the importance and nature of quality control checks Understand the process of coding Understand the data entry process and data entry alternatives Learn how surveys are tabulated and cross tabulated Learn basic descriptive statistics Understand the concept of hypothesis development and how to test hypotheses Chapter Twelve Objectives Chapter Twelve

Data Analysis Overview Validation and Editing Coding Machine Cleaning of Data Tabulation and Statistical Analysis Data Entry Chapter Twelve

Step One: Validation: Confirming the interviews / surveys occurred Editing: Determining the questionnaires were completed correctly Step Two: Coding: Grouping and assigning numeric codes to the question responses Step Three: Data Entry: Process of converting data to an electronic form Scanning the questionnaire into a database Step Four: Clean the Data: Check for data entry errors or data entry inconsistencies Machine cleaning: Computerized check of the data Step Five: One-Way Frequency Tables, Cross Tabulations Data Analysis Overview Chapter Twelve

Editing and Skip Patterns Editing: The process of ascertaining that questionnaires were filled out properly and completely Skip Patterns: Sequence in which later questions are asked, based on a respondent’s answer to an earlier question Chapter Twelve

Coding Coding: Grouping and assigning numeric codes to every potential response to a question The Process: List responses Consolidate responses Set codes Enter codes Keep coding sheet Chapter Twelve

Data Entry Data Entry: Converting information to an electronic format Intelligent Data Entry: A form of data entry in which the information being entered into the data entry device is checked for internal logic Chapter Twelve

Tabulation Chapter Twelve The most basic tabulation is the one-way frequency table:

Bivariate cross-tabulation: Cross tabulation two items: “Business Category” and “Gender” Multivariate cross-tabulation: Additional filtering criteria—“Veteran Status”. Now filtering three items. Cross-Tabulation Data Chapter Twelve

Effective means of summarizing large data sets. Key measures include: mean, median, mode, standard deviation, skewness, and variance. Effective means of summarizing large data sets. Key measures include: mean, median, mode, standard deviation, skewness, and variance. Descriptive Statistics Chapter Twelve

Mean: The sum of the values for all observations of a variable divided by the number of observations Median: In an ordered set, the value below which 50 percent of the observations fall Mode: The value that occurs most frequently Mean: The sum of the values for all observations of a variable divided by the number of observations Median: In an ordered set, the value below which 50 percent of the observations fall Mode: The value that occurs most frequently Measure of Central Tendency Chapter Twelve

Variance: Sums of the squared deviations from the mean divided by the number of observations minus one Same formula as standard deviation Range: M aximum value for variable minus the minimum value for that variable Standard Deviation: Calculate by Subtracting the mean of a series from each value in a series Squaring each result then summing them Dividing the result by the number of items minus 1 Take the square root of this value Variance: Sums of the squared deviations from the mean divided by the number of observations minus one Same formula as standard deviation Range: M aximum value for variable minus the minimum value for that variable Standard Deviation: Calculate by Subtracting the mean of a series from each value in a series Squaring each result then summing them Dividing the result by the number of items minus 1 Take the square root of this value Measures of Dispersion Chapter Twelve

1.Mathematical differences 2.Statistical significance 3.Managerially important differences Statistical Significance Chapter Twelve

Step One: Stating the hypothesis Null Hypothesis: status quo proven to be true Alternative Hypotheses: another alternative proven to the true. Step Two: Choosing the appropriate test statistic Test of means, test or proportions, ANOVA, etc. Step Three: Developing a decision rule Determine the significance level Need to determine whether to reject or fail to reject the null hypothesis Hypothesis Testing: Key Steps Chapter Twelve

Step Four: Calculating the value of the test statistic Use the appropriate formula to calculate the value of the statistic. Step Five: Stating the conclusion Stated from the perspective of the original research question Hypothesis Testing: Key Steps Chapter Twelve

Rejection of the null hypothesis when, in fact, it is true Acceptance of the null hypothesis when, in fact, it is false Tests are either one- or two-tailed. This decision depends on the nature of the situation and what the researcher is demonstrating. One-Tailed Test: “If you take the medicine, you will get better” Two-Tailed Test: “If you take the medicine, you will get either better or worse.” Type I error: Type II error: Types of Errors in Hypothesis Testing Chapter Twelve One- and Two-Tailed Tests

Issues With Type I and II Errors Chapter Twelve

1.Independent samples 2.Related samples 3.Degrees of freedom 4.p Values and significance testing Commonly Used Statistical Hypothesis Tests Chapter Twelve