logging in or signing up Burns_11 HumberPR Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT lite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 73 Category: Entertainment License: All Rights Reserved Like it (0) Dislike it (0) Added: April 21, 2009 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Slide 1: Basic Marketing Research: Using Microsoft Excel Data Analysis, 2nd Edition Alvin C. Burns Louisiana State University Ronald F. Bush University of West Florida Prentice Hall Publishers Slide 2: Collecting Data and Summarizing What You Found in Your Sample Slide 3: Nonsampling errors: are errors in the research process pertaining to anything except the sample size. Fieldworker (Interviewer) errors Intentional Unintentional Respondent Intentional Unintentional Errors Encountered in the Data Collection Stage Slide 4: Nonresponse: is defined as a failure on the part of a prospective respondent to take part in the survey or to fail to answer all questions on the questionnaire. Three types: Refusal: a prospective respondent declines to participate in the survey. Break-off: a respondent stops answering somewhere in the middle of the survey. Item omission: a respondent does not answer a particular question, but continues to answer following questions. Types of Nonresponse Errors Slide 5: Multiple submissions Bogus respondents and responses Population misrepresentation Data Collection Errors with Online Surveys Slide 6: 6 Data entry: refers to the creation of a computer file that holds the raw data taken from all of the completed questionnaires. Data coding: is defined as the identification of code values that pertain to the possible responses for each question on the questionnaire. Data codes are typically numerical. The data code book: identifies all of the variable names and code numbers associated with each possible response to each variable. Coding Data and the Data Code Book Slide 7: Introduction to Your XL Data Analyst Slide 8: Introduction to Your XL Data Analyst cont. Slide 9: How to get your data code book into XL Data Analyst Begin with the “define variables” worksheet Type in a variable label: a unique, short single-word description for a variable. The variable label should be placed in the first row on the Data worksheet. The row of variable labels should be linked (via Copy-Paste Special—Paste Link) into the Data Variables worksheet. Introduction to Your XL Data Analyst cont. Slide 10: A variable description: is a phrase or sentence that identifies the variable in more detail and refers to the question on the questionnaire. Value codes: are numerical values associated with responses. Value labels: are the names of the different responses for each data code number. Define Variables Worksheet Slide 11: A data set: is defined as a matrix of numbers and other representations that includes all of the relevant answers of all the respondents in a survey. Data analysis: is defined as the process of describing a data set by computing a small number of measures that characterize the data set in ways that are meaningful to the client. Types of Data Analyses Used in Marketing Research Slide 12: It summarizes the data. It generalizes sample findings to the population. It compares for meaningful differences. It relates underlying patterns. Four Functions of Data Analysis Slide 13: The Research Objective Determines the Appropriate Types of Data Analysis Slide 14: Two objectives of summarizing findings: Describing the typical response (“central tendency”) How typical are respondents (“variability”) Summarizing Your Sample Findings Slide 15: Research Objectives and Data Analyses Used in Marketing Research Slide 16: Appropriate Summarization Analyses by Type of Scale Slide 17: XL Data Analyst… Summarize… Percents… How to Summarize Categorical Variables with XL Data Analyst Slide 18: How to Summarize Categorical Variables with XL Data Analyst cont. Slide 19: How to Summarize Categorical Variables with XL Data Analyst cont. Slide 20: XL Data Analyst… Summarize… Averages… How to Summarize Metric Variables with XL Data Analyst Slide 21: How to Summarize Metric Variables with XL Data Analyst cont. Slide 22: Standard Deviation and Normal Curve Slide 23: The Six Step Approach to Data Analysis and Presentation Slide 24: The Six Step Approach to Data Analysis and Presentation cont. You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.
Burns_11 HumberPR Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT lite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 73 Category: Entertainment License: All Rights Reserved Like it (0) Dislike it (0) Added: April 21, 2009 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Slide 1: Basic Marketing Research: Using Microsoft Excel Data Analysis, 2nd Edition Alvin C. Burns Louisiana State University Ronald F. Bush University of West Florida Prentice Hall Publishers Slide 2: Collecting Data and Summarizing What You Found in Your Sample Slide 3: Nonsampling errors: are errors in the research process pertaining to anything except the sample size. Fieldworker (Interviewer) errors Intentional Unintentional Respondent Intentional Unintentional Errors Encountered in the Data Collection Stage Slide 4: Nonresponse: is defined as a failure on the part of a prospective respondent to take part in the survey or to fail to answer all questions on the questionnaire. Three types: Refusal: a prospective respondent declines to participate in the survey. Break-off: a respondent stops answering somewhere in the middle of the survey. Item omission: a respondent does not answer a particular question, but continues to answer following questions. Types of Nonresponse Errors Slide 5: Multiple submissions Bogus respondents and responses Population misrepresentation Data Collection Errors with Online Surveys Slide 6: 6 Data entry: refers to the creation of a computer file that holds the raw data taken from all of the completed questionnaires. Data coding: is defined as the identification of code values that pertain to the possible responses for each question on the questionnaire. Data codes are typically numerical. The data code book: identifies all of the variable names and code numbers associated with each possible response to each variable. Coding Data and the Data Code Book Slide 7: Introduction to Your XL Data Analyst Slide 8: Introduction to Your XL Data Analyst cont. Slide 9: How to get your data code book into XL Data Analyst Begin with the “define variables” worksheet Type in a variable label: a unique, short single-word description for a variable. The variable label should be placed in the first row on the Data worksheet. The row of variable labels should be linked (via Copy-Paste Special—Paste Link) into the Data Variables worksheet. Introduction to Your XL Data Analyst cont. Slide 10: A variable description: is a phrase or sentence that identifies the variable in more detail and refers to the question on the questionnaire. Value codes: are numerical values associated with responses. Value labels: are the names of the different responses for each data code number. Define Variables Worksheet Slide 11: A data set: is defined as a matrix of numbers and other representations that includes all of the relevant answers of all the respondents in a survey. Data analysis: is defined as the process of describing a data set by computing a small number of measures that characterize the data set in ways that are meaningful to the client. Types of Data Analyses Used in Marketing Research Slide 12: It summarizes the data. It generalizes sample findings to the population. It compares for meaningful differences. It relates underlying patterns. Four Functions of Data Analysis Slide 13: The Research Objective Determines the Appropriate Types of Data Analysis Slide 14: Two objectives of summarizing findings: Describing the typical response (“central tendency”) How typical are respondents (“variability”) Summarizing Your Sample Findings Slide 15: Research Objectives and Data Analyses Used in Marketing Research Slide 16: Appropriate Summarization Analyses by Type of Scale Slide 17: XL Data Analyst… Summarize… Percents… How to Summarize Categorical Variables with XL Data Analyst Slide 18: How to Summarize Categorical Variables with XL Data Analyst cont. Slide 19: How to Summarize Categorical Variables with XL Data Analyst cont. Slide 20: XL Data Analyst… Summarize… Averages… How to Summarize Metric Variables with XL Data Analyst Slide 21: How to Summarize Metric Variables with XL Data Analyst cont. Slide 22: Standard Deviation and Normal Curve Slide 23: The Six Step Approach to Data Analysis and Presentation Slide 24: The Six Step Approach to Data Analysis and Presentation cont.