Data Processing, Fundamental Data Chapter Thirteen: Data Processing, Fundamental Data Analysis, and the Statistical Testing of Hypotheses

Understand the importance and nature of quality control checks Chapter Thirteen: Data Processing, Fundamental Data Analysis, and the Statistical Testing of Hypotheses Understand the importance and nature of quality control checks Describe the process of coding Understand the data entry process and data entry alternatives Explain how surveys are tabulated and cross tabulated Describe basic descriptive statistics Understand the concept of hypothesis development and testing

Data Analysis Overview The Key Steps: 1 2 3 4 5 Machine Cleaning of Data Tabulation and Statistical Analysis Validation and Editing Data Entry Coding Chapter Thirteen

Data Analysis Overview 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

Editing and Skip Patterns 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

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

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

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

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

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

Measure of Central Tendency 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

Measures of Dispersion Variance: Sums of the squared deviations from the mean divided by the number of observations minus one Same formula as standard deviation Range: Maximum 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

Statistical Significance Mathematical differences Statistical significance Managerially important differences

Hypothesis Testing: Key Steps 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 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

Types of Errors in Hypothesis Testing Type I error: 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.” One- and Two-Tailed Tests

Issues With Type I and II Errors Type I and Type II Errors

Commonly Used Statistical Hypothesis Tests Independent samples Related samples Degrees of freedom p Values and significance testing

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