Major Sources Of Error In Research Design
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CASE MANAGEMENT VoC Consulting & Integrations market RESEARCH Customer Satisfaction Strategic Planning & Segmentation Research Product Development MARKETING & BRAND RESEARCH employee INSIGHTS employee engagement employee pulse surveys training surveys 360o employee feedback exit interviews Onboarding Surveys Platform Research Suite Vocalize Target Audience Site types of errors in research design Intercept Employee Engagement Qualtrics 360 Online Sample Professional Services Industries industrySOLUTIONS AIRLINES AUTOMOTIVE
Sources Of Error In Survey Research
BUSINESS TO BUSINESS (B2B) FINANCIAL SERVICES GOVERNMENT HIGHER EDUCATION K-12 MEDIA RETAIL TRAVEL & HOSPITALITY Customers Resources Support Online Help 1-800-340-9194 types of sampling errors in research Contact Support Login Request Demo Survey Tips Back to Blog 5 Common Errors in the Research Process AuthorQualtricsJune 21, 2010 Designing a research project takes time, skill and knowledge. With Qualtrics survey software, we make types of errors in data collection the survey creation process easier, but still you may feel overwhelmed with the scope of your research project. Here are 5 common errors in the research process. 1. Population Specification This type of error occurs when the researcher selects an inappropriate population or universe from which to obtain data. Example: Packaged goods manufacturers often conduct surveys of housewives, because they are easier to contact, and it is assumed they decide
Sources Of Error In Measurement In Research Methodology Ppt
what is to be purchased and also do the actual purchasing. In this situation there often is population specification error. The husband may purchase a significant share of the packaged goods, and have significant direct and indirect influence over what is bought. For this reason, excluding husbands from samples may yield results targeted to the wrong audience. 2. Sampling Sampling error occurs when a probability sampling method is used to select a sample, but the resulting sample is not representative of the population concern. Unfortunately, some element of sampling error is unavoidable. This is accounted for in confidence intervals, assuming a probability sampling method is used. Example: Suppose that we collected a random sample of 500 people from the general U.S. adult population to gauge their entertainment preferences. Then, upon analysis, found it to be composed of 70% females. This sample would not be representative of the general adult population and would influence the data. The entertainment preferences of females would hold more weight, preventing accurate extrapolation to the US general adult population. Sampling error is affected by the homogeneity of the population being studied and sampled from and by the size of the sample. 3. Selection Selection error is the sampling error for a sample
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Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use http://www.slideshare.net/abineshraja/errors-in-research of cookies on this website. See our Privacy Policy and User Agreement http://www.socialresearchmethods.net/kb/measerr.php for details. SlideShare Explore Search You Upload Login Signup Home Technology Education More Topics For Uploaders Get Started Tips & Tricks Tools Errors in research Upcoming SlideShare Loading in …5 × 1 1 of 13 Like this presentation? Why not share! Share Email SAMPLING AND SAMPLING ERRORS byrambhu21 of error 26971views Type i and type ii errors byp24ssp 7529views Sampling Errors byNeeraj Kumar 1405views Examples of Type of Errors in Surve... byLena Argosino 5643views Errors and Error Measurements byMilind Pelagade 1820views Type 1 and type 2 errors bysmulford 3340views Share SlideShare Facebook Twitter LinkedIn Google+ Email Email sent successfully! Embed Size (px) Start on Show related SlideShares at end WordPress Shortcode errors in research Link Errors in research 15,595 views Share Like Abinesh Raja M, Management Consultant Follow 0 0 2 Published on Feb 11, 2013 Published in: Business 1 Comment 12 Likes Statistics Notes Full Name Comment goes here. 12 hours ago Delete Reply Spam Block Are you sure you want to Yes No Your message goes here Post Simba Nyakudanga , Accountant at Government statistics Office gud 3 years ago Reply Are you sure you want to Yes No Your message goes here kottur2004 1 month ago Pudzianowski Bernard Oduor , Learner at life 2 months ago Divya Sharma 3 months ago manish gharte 5 months ago Sam Godson 10 months ago Show More No Downloads Views Total views 15,595 On SlideShare 0 From Embeds 0 Number of Embeds 7 Actions Shares 0 Downloads 0 Comments 1 Likes 12 Embeds 0 No embeds No notes for slide Errors in research 1. Errors In Research Presented By: Team 18 Abinesh Raja M Raghu Priya Rajeshwaran Sriram Kumar Vijayalakshmi S 2. Agenda1 Introduction To Research Errors 2 Common Errors In Research 3 Implications of Research Errors Research Errors Lea
assumes that any observation is composed of the true value plus some random error value. But is that reasonable? What if all error is not random? Isn't it possible that some errors are systematic, that they hold across most or all of the members of a group? One way to deal with this notion is to revise the simple true score model by dividing the error component into two subcomponents, random error and systematic error. here, we'll look at the differences between these two types of errors and try to diagnose their effects on our research. What is Random Error? Random error is caused by any factors that randomly affect measurement of the variable across the sample. For instance, each person's mood can inflate or deflate their performance on any occasion. In a particular testing, some children may be feeling in a good mood and others may be depressed. If mood affects their performance on the measure, it may artificially inflate the observed scores for some children and artificially deflate them for others. The important thing about random error is that it does not have any consistent effects across the entire sample. Instead, it pushes observed scores up or down randomly. This means that if we could see all of the random errors in a distribution they would have to sum to 0 -- there would be as many negative errors as positive ones. The important property of random error is that it adds variability to the data but does not affect average performance for the group. Because of this, random error is sometimes considered noise. What is Systematic Error? Systematic error is caused by any factors that systematically affect measurement of the variable across the sample. For instance, if there is loud traffic going by just outside of a classroom where students are taking a test, this noise is liable to affect all of the children's scores -- in this case, systematically lowering them. Unlike random error, systematic errors tend to be consistently either positive or negative -- because of this, systematic error is sometimes considered to be bias in measurement. Reducing Measurement Error So, how can we reduce measurement errors, random or systematic? One thing you can do is to pilot test your instruments, getting feedback from your respondents regarding how easy or hard the measure was and information about how the testing environment affected their performance. Second, if you are gathering measures using people to collect the data (as interviewers or observers) you should make sure