The course is about website survey sampling.
The terminology my target student would expect to find in my course is elementary technical statistical concepts like the mean, standard deviation, variance and standard error. The generic statistical business process model. The Statistics Canada Survey Methods and Practices.
The survey methodology part of the course is composed material from the Statistics Canada Survey Methods and Practices (2013 publication)-chapter one, chapter six: section 6.1, section 6.2.1, section 6.2.2, section 6.2.3, section 6.2.5, and section 6.2.6. The survey methodology part of the course is also composed of material from the United Nations Economic Commission for Europe (UNECE) Generic Statistical Business Process Model (Version 5) Word Document under the Creative Commons License. The R programming part of the course is composed of material from the Introduction to R manual. The website survey sampling software part of the course is composed of material from the KwikSurveys Free Software and Google Forms.
The course will take one hour and forty four minutes to complete.
The course gives students an introduction to website survey sampling. The course has a theoretical component composed of probability sampling, non-probability sampling, sample survey theory, website survey sampling software, introduction to R programming and statistical business process modeling. The applied component is composed of website survey sampling software.
Students have the option to register for the SAS OnDemand for Academics program to access free SAS software educational bundle. The SAS software educational bundle is closely related to the course material. Registration for the SAS OnDemand for Academics program is required in order to access the SAS software educational bundle.
Introduction to Udemy College. Introduction to Udemy Courses. Introduction to the Website Survey Sampling Course.
Overview of the Course. Layout of the Lectures. The prescribed readings: United Nations Economic Commission for Europe (UNECE) Generic Statistical Business Process Model (version 5).
Statistics Canada Survey Methods and Practice chapter one; chapter six: section: 6.1; section: 6.2.1; section: 6.2.2; section: 6.2.3; section: 6.2.5; and section: 6.2.6. Layout of the quiz assessments. The lecture also introduces the first prescribed reading before the first course lecture. Hariz Naam interaction.
The lecture introduces the students to the generic statistical business process model and the Statistics Canada Survey Methods and Practices.
These are the nine steps of the UNECE generic statistical business process model (visual component-process chart) and Statistics Canada Survey Methods and Practices text (eleven steps). The lecture covers specify needs and design activities of the UNECE generic statistical business process model. Covers formulate objectives and select survey frame from Statistics Canada Survey Methods and Practices.
The lecture also covers the determine sample design from Statistics Canada Survey Methods and Practices. Introduce the Website Survey Sampling Course intermission.
How many UNECE statistical business process model (Version 5) activities are there in total?
The lecture covers Probability Sampling methods from Statistics Canada Survey Methods and Practices. In this lecture probability sampling is introduced. The key concepts covered are sampling, probability sampling and the eight sampling methods described in Statistics Canada Survey Methods and Practices. These are specified to be: Simple random sampling , Systematic sampling, Cluster sampling, Stratified sampling, Probability-Proportional-to-Size (PPS) sampling, Multi-stage sampling, Multi-phase sampling and Replicated sampling.
The lecture covers Probability Sampling methods from Statistics Canada Survey Methods and Practices. In this lecture simple random probability sampling is introduced. The methodology; and the advantages and disadvantages of the method are considered. The method is illustrated with a visual example.
The lecture covers Probability Sampling methods from Statistics Canada Survey Methods and Practices. In this lecture systematic probability sampling is introduced. The methodology; and the advantages and disadvantages of the method are considered. The method is illustrated with a visual example.
The lecture covers Probability Sampling methods from Statistics Canada Survey Methods and Practices. In this lecture cluster probability sampling is introduced. The methodology; and the advantages and disadvantages of the method are considered. The method is illustrated with a visual example.
In this lecture stratified probability sampling is also introduced. The methodology; and the advantages and disadvantages of the method are considered. The method is illustrated with a visual example.
The other methods that are mentioned are Probability-Proportional-to-Size sampling, multi-stage sampling, multi-phase sampling and replicated sampling. The lecture also specifies the chapter on sampling designs of Statistics Canada Survey Methods and Practice additional reading when the students have completed the course.
Which probability sampling design is the foundation of all probability sampling designs?
The lecture covers Non-probability sampling methods from Statistics Canada Survey Methods and Practices. In this lecture the students are introduced to the definition of sampling and its uses. The key definitions explored are that of a census and a sample survey. The components of the sampling process are outlined as, sub-setting of populations, followed by collection and using the sample design to make inferences about the population.
The students are introduced to the common uses of non-probability sampling designs, namely, market research. This is explained as being due to its characteristic of being inexpensive and quick. The lecture then compares it to probability designs. Initially the advantages are outlined followed by the limitations. In each case the limitations are summarized to finding an alternative method for making inferences and using the advantages of non-probability sampling methods to enrich one's empirical analyses.
The lecture covers Non-Probability Sampling methods from Statistics Canada Survey Methods and Practices. In the lecture the students are introduced to the six different types of non-probability sampling schemes in the Statistics Canada Survey Methods and Practices.
The techniques are introduced as: haphazard sampling; volunteer sampling; Judgment sampling; quota sampling; modified probability sampling; and network or snowball sampling.
The first method introduced in the lecture is non-probability sampling method haphazard sampling. The lecture shows that the method has interesting properties based on the assumption of homogeneity, however, compared to probability sampling methods in the statistical inference setting the method cannot compare. The most interesting biases considered are those of the collector and the individuals that enter into the sample. In comparison to simple random sampling the biases are those of the frame. The other important difference is that of being able to make inference after both biases are corrected. It is interesting to note that both methods can control both biases to obtain a very interesting dataset for analysis. The collection can be structured and the collectors grouped according to homogenous characteristics for the biases in the haphazard samples. A similar approach can be followed for the individuals who enter the sample since one can choose the sample in any manner. The configuration of the collection for simple random sampling can be done by adjusting the frame structure to be in line with the survey population and the sample in the standard manner.
The lecture then introduces the students to volunteer sampling, judgment sampling; quota sampling; modified probability sampling; and network or snowball sampling.
What probability sampling method is similar Network or Snowball sampling?
UNECE generic statistical business process model (visual component-process chart). The following four activities of the Statistics Canada Survey Methods and Practices: questionnaire design: data collection (basic approaches): Website Survey Sampling Course focus on internet based methods; and data capture and coding. Nine activities of the UNECE generic statistical business process model and the following individual activities: design; build; collect; process; and quality management.
UNECE generic statistical business process model (visual component-process chart). The following two activities of the Statistics Canada Survey Methods and Practices: Data capture and coding; and edit and impute, estimation, estimation (sampling error), analyze data, disseminate data, disseminate data (delivery and presentation), documentation, and a summary of all the survey steps (including the sample survey life cycle and the statistical business process model). The lecture also considers: guidelines for imputation methods; similarity between video editing and imputation in statistics (image analysis concept).
The lecture provides an overview of all the lecture presentations for website survey sampling software. The three components of the three lectures are: A guided tour of KwikSurveys and Google Forms; an introduction to the R Language, and other materials. The guided tour of KwikSurveys involves the following steps: Where to download the software, namely, KwikSurveys.com and an illustrated guide of its key functionalities. The lecture highlights that the guided tour is that of the Free version. The lecture assumes that the student knows how to download and install the software. The tour gives students a recommendation to the Help section of the KwikSurveys Free version dashboard. The tour also highlights the limitation that the KwikSurveys free version software does not allow one to use open ended questions in the surveys that can be built. This is then followed by a concise explanation of how to construct questionnaire with the software. This is then followed by a brief recommendations on how where to obtain customized reports of the survey data (in real-time) from the KwikSurveys Report section. The tour ends with the different ways of publishing the survey, namely, embedding the survey (includes pop-up surveys and embedded surveys), sending to email-list, and a link from one's website. The guided tour of Google Forms, follows steps similar to those followed for KwikSurveys namely: Where to download the software.The lecture highlights Google Forms is free and has an advantage over KwikSurveys in that it allows one to ask open-ended questions. The lecture glosses over website sample survey construction with Google Forms. The tour concludes with an illustration of the survey publication options of Google Forms in the form of: Embedded link. The lecture also considers other software in the form of Google Consumer Surveys, and the opportunities available for website owners under the program.
The lecture begins with the components of the R language training. The complete framework is the Introduction to R manual, namely, the students should aim to apply the whole manual. The approach to the course is that a demonstration with an introductory session will show the students how to approach the R language. The undertone is that the students will explore all aspects of the manual analogously. The lecture simulates an interaction with R using the Introduction to R manual. The students are shown the section of the manual that explains where to find instructions to download and install R. The next is to consider the remainder of the instructions when invoking R from one's desktop. The screen output corresponding to the instruction manual instructions is shown and explained in the lecture.
The students are shown how to navigate in manual.
The help command of the R language is considered first. The R manual instructions about the resulting output are mentioned. The output is not shown.
The next part of the code considers the task of simulating the x and y co-ordinates of points on a plane using the standard normal distribution. The x and y coordinates are generated using the code and the result plotted. The resulting output is considered with the instructions from the manual about the expected output.
The second part of the lecture considers code to perform complex arithmetic operations in R. The coding system is used to guide the students to the generation of complex numbers on the unit circle representing the real and imaginary parts of a complex number. I have added code to calculate the mean and variance of the 50 non-random points that are equally spaced between -pi and pi as instructed by the manual.
The students are then given a walk-through the two methods suggested by the manual for generating a sample of points within the unit circle. The first method uses the standard normal distribution to simulate the real and imaginary real parts of a complex number. The manual provides guidance on how to make adjustments for points that happen to fall outside the unit circle from a sampling scheme using the standard normal distribution. The output from the method is shown.
The students shown how to repeat the process with the uniform distribution. The output generated is also shown and explained to the students.
What type of questions can you not ask on KwikSurveys free version?
What is the main advantage of creating website surveys with Google Forms over KwikSurveys free version?
What is the first section of the R Manual of the recommended reading for the Introduction to R programming lecture?
The lecture guides the students through a build up from the survey life cycle and statistical business process model foundation established at the end of Introduction to Survey Sampling Part Two lecture (in the summary). The Statistics Canada Survey Life Cycle as an add-on to the Statistics Canada Survey Methods and Practices eleven steps. The add-on steps are planning, design, develop, implement and evaluate. The add-on is essentially a process management like component of the Statistics Canada Survey Methods and Practices. In the lecture this is linked to the UNECE generic statistical business process model steps: Evaluate, quality management and metadata management. The UNECE generic business statistical business process model has a quality loop method of plan, run, evaluate and improve. In this course this is also linked to the parallel activities of the survey.
The students are introduced to the next step after planning, namely, designing. The key component of design is a broad methodological framework. The methodological framework allows for more detailed work to be done on the various survey steps. The main parameters of the design are to find the set of methods that best meets the balance of quality objectives; and to find the set of procedures that achieve the best balance of quality objectives.
The students are introduced to the basic assessment toolkits for the survey design. These include pretests or pilot surveys, these may include or are supplemented with independent with assessments of questionnaire adequacy. The two important bias source assessments in the form of survey frame suitability and operational requirements. Other assessments are recommended which are part of the prescribed reading after the course is completed.
The final stage of the design is to further configure the collection processes by producing all field materials for the data collection or implementation stage. The common key components for collection are interviewer training manuals, instruction manuals, and sample control documents. The software programs are developed, adapted or tested for the case of computer administered questionnaires. The students can begin to conceptualize the application of the theory in the probability and non-probability sampling methods lectures. This is the finalization of sample selection procedures in the form of specifications, and the finalization of the sample estimation procedures.
The configuration of the design continues with the preparations of the specifications for data coding, capture, editing, and imputation. The key design parameter of efficiency is included to the data processing step. The students are also introduced to the quality assurance and control (management) procedures of design. These include controlling, measuring the quality of the final results at each survey step; and measuring the quality of the final statistical products (or survey results).
The students are introduced to the implementation phase in the form of the launching of the survey. The key operational aspects are the production of all statistical business process control forms, all statistical business process control manuals, and all the questionnaires. The finer grained method of moving through the process steps is illustrated by the finer considerations of design pertaining to the configuration of the data collection in the form of the training of interviewers, and selecting the sample. The students are then introduced to the considerations of the information collection process. The parameter for ideal performance is the compliance of activities with the development phase.
The students are then introduced to the data processing activities of data capture, coding, editing, and imputation. The students are then introduced to the goal of the survey life cycle process, namely, a well-structured and complete data set from which it is possible to produce required tabulations, and analyze survey (business process) results. The dataset is not the complete process, however, in that the results that need to be checked for confidentiality against the disclosure control protocol.
The students next explore data dissemination and data quality. In the case of data quality the design correspondence of data quality to the survey design parameters is measured and monitored. The UNECE data quality loop parallel concepts of survey or business process evaluation being ongoing process throughout the survey. The lecture highlights to students how we at Udemy College and Udemy Studio also apply strict quality measures on our material.
This quality assessments involve the methods used, in terms of operational effectiveness and cost performance in parallel to the quality assessments during the design phase. The design parameter for the evaluations is the suitability of the technical practices. The students are introduced to the reasons for the assessments, namely, to improve implementation and guide implementation. The process can be further enhanced by being made more specific in terms of methodological and operational concepts. Similarly, for components. The additional requirement is that the quality assessments must achieve all these requirements both within and across surveys. This is similar to the concept of Hadoop.
In this lecture the students are introduced to the other key information from the evaluations in the form of measures and other information about the quality limitations of the program data.
The key insights include shortcomings or problems in the other steps of the survey or business process.
The survey life cycle then loops to the beginning in the form of post survey evaluations and comparisons for existing and new activities.
The key information sources identified are previous surveys and its pilot surveys.
The key formulations in the new planning phase are realistic survey objectives, expected data quality ideas, essential information for survey design and essential information for data processing.
The final course summary ends with the consideration of what a survey process is, what the key steps are, in the context of the course material covered.
How many phases are there in the Statistics Canada Survey Methods and Practices sample survey life cycle?
The students are congratulated for completing the course. The students are then introduced to the supplemental materials. The supplemental materials are:
Sampling methods for web and e-mail surveys
Published in 2008
Professor R. D. Fricker,
Naval Postgraduate School
Statistics, Biosurveillance, Survey methods, Military, Personnel
In N. Fielding, R. M. Lee, & G. Blank (Eds.), The SAGE handbook of online research methods (pp. 195−216). Los Angeles: Sage
Good Practices in Survey Design Step-by-Step
Published in 2012
Published by: Organisation for Economic Co-operation and Development
In Measuring Regulatory Performance: A Practitioner's Guide to Perception Surveys, OECD Publishing
I am from East London, South Africa. I am 36 years old. My hobbies are voice acting, screencasting, e-learning, statistical modeling, Fourier analysis, polynomial chaos, extreme value theory, data analysis, visual analytics, statistical innovation (Hadoop), reading and data journalism.
I obtained my Master of Science degree in Mathematical Statistics in 2011. I have eleven years experience working as a statistician in research.
My special statistical research fields include sample survey sampling, data mining and spatial statistics. I have a SAS programming III certificate and a SAS Macro Language certificate. I am a member of the Internet Society, SAS Analytics U community and the Hadoop360 community. I am very passionate about statistical technologies, blogging technologies, multi-media content creation and writing.