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 ID: 418731
Download Presentation The PPT/PDF document "Chapter Thirteen:" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Chapter Thirteen:
Data Processing, Fundamental Data
Analysis, and the Statistical Testing of HypothesesSlide2
Understand the importance and nature of quality control checks
Describe the process of codingUnderstand the data entry process and data entry alternativesExplain how surveys are tabulated and cross tabulatedDescribe basic descriptive statisticsUnderstand the concept of hypothesis development and testing
Chapter
Thirteen: Data
Processing, Fundamental Data
Analysis
, and the Statistical Testing of HypothesesSlide3
Data Analysis Overview
The Key Steps:
1
2
3
4
5
Validation
and
Editing
Coding
Machine
Cleaning
of Data
Tabulation andStatisticalAnalysis
DataEntry
Chapter ThirteenSlide4
Data Analysis Overview
Step One:Validation: Confirming the interviews / surveys occurredEditing: Determining the questionnaires were completed correctlyStep Two:
Coding
: Grouping and assigning numeric codes to the question
responses
Step Three:Data Entry: Process of converting data to an electronic formScanning the questionnaire into a
databaseStep Four:Clean the Data: Check for data entry errors or data entry inconsistenciesMachine cleaning: Computerized check of the dataStep Five:
One-Way Frequency Tables, Cross TabulationsSlide5
Editing and Skip Patterns
Editing:The process of ascertaining that questionnaires were filled out properly and completelySkip Patterns:Sequence in which later questions are asked, based on a respondent’s answer to an earlier questionSlide6
Coding
Coding: Grouping and assigning numeric codes to every potential response to a question The Process:List
responses
Consolidate
responses
Set codesEnter codesKeep
coding sheetSlide7
Data Entry
Data Entry: Converting information to an electronic formatIntelligent Data Entry:A form of data entry in which the information being entered into the data entry device is checked for internal logicSlide8
Tabulation
The most basic tabulation is the one-way frequency table:Slide9
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.Slide10
Descriptive Statistics
Effective means of summarizing large data sets.
Key measures include: mean, median, mode, standard deviation, skewness, and variance.Slide11
Measure of Central Tendency
Mean:The sum of the values for all observations of a variable divided by the number of observationsMedian:In an ordered set, the value below which 50 percent of the observations fallMode:
The
value that occurs most frequentlySlide12
Measures of Dispersion
Variance:Sums of the squared deviations from the mean divided by the number of observations minus oneSame formula as standard deviationRange:Maximum value for variable minus the minimum value for that variableStandard Deviation: Calculate by
Subtracting
the mean of a series from each value in a series
Squaring
each result then summing themDividing the result by the number of items minus 1
Take the square root of this valueSlide13
Statistical Significance
Mathematical differencesStatistical significanceManagerially important differencesSlide14
Hypothesis Testing: Key Steps
Step One: Stating the hypothesis Null Hypothesis: status quo proven to be trueAlternative Hypotheses: another alternative proven to the true.Step Two: Choosing the appropriate test statisticTest
of means, test or proportions, ANOVA, etc
.
Step Three: Developing a decision rule
Determine the significance levelNeed to determine whether to reject or fail to reject the null hypothesisSlide15
Hypothesis Testing: Key Steps
Step Four: Calculating the value of the test statisticUse the appropriate formula to calculate the value of the statistic.Step Five: Stating the conclusionStated from the perspective of the original research questionSlide16
Types of Errors in Hypothesis Testing
Type I error:Rejection of the null hypothesis when, in fact, it is trueType I error: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 TestsSlide17
Issues With Type I and II Errors
Type I and Type II ErrorsSlide18
Commonly Used Statistical Hypothesis Tests
Independent samplesRelated samplesDegrees of freedomp Values and significance testingSlide19
Copyright © 2014 John Wiley & Sons Canada, Ltd. All rights reserved. Reproduction or translation of this work beyond that permitted by Access Copyright (the Canadian copyright licensing agency) is unlawful. Requests for further information should be addressed to the Permissions Department, John Wiley & Sons Canada, Ltd.
The purchaser may make back-up copies for his or her own use only and not for distribution or resale.
The author and the publisher assume no responsibility for errors, omissions, or damages caused by the use of these files or programs or from the use of the information contained herein.
Copyright