Data validation
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This document discusses data validation. It defines data validation as ensuring programs use clean, correct and useful data by using validation rules to check data entered is right, meaningful and secure. It describes methods for checking validity like manual reviews, auto checks and having providers review data. The document also distinguishes between validation, done by software to check data types and formats, and verification, done by users to check values are entered correctly without mistakes. It provides examples of validation types like format, presence, range and lookup checks.
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This document introduces the concept of data classification and levels of measurement in statistics. It explains that data can be either qualitative or quantitative. Qualitative data consists of attributes and labels while quantitative data involves numerical measurements. The document also outlines the four levels of measurement - nominal, ordinal, interval, and ratio - from lowest to highest. Each level allows for different types of statistical calculations, with the ratio level permitting the most complex calculations like ratios of two values.
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1. The document discusses various steps involved in data processing such as editing, coding, tabulation and cross-tabulation.
2. Editing involves detecting and correcting errors in raw data to ensure accuracy, completeness, consistency and uniformity. Coding assigns numerical codes to questionnaire responses to simplify analysis.
3. Tabulation organizes data into tables or lists to facilitate analysis and comparison. It can be univariate, tabulating one variable, or bivariate/multivariate, tabulating relationships between two or more variables.
4. Cross-tabulation summarizes the relationship between two or more categorical variables by classifying data into a contingency table. It provides a basic understanding of how variables are interrelated.
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This document discusses different types of data including:
- Qualitative data which describes attributes that can be observed but not computed, and quantitative data which can be measured numerically.
- Primary data is collected first-hand for a specific purpose, while secondary data has already been collected in the past.
- Discrete data takes only certain values, while continuous data can take any value in a range.
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A data model is a set of concepts that define the structure of data in a database. The three main types of data models are the hierarchical model, network model, and relational model. The hierarchical model uses a tree structure with parent-child relationships, while the network model allows many-to-many relationships but is more complex. The relational model - which underlies most modern databases - uses tables with rows and columns to represent data, and relationships are represented by values in columns.
Classification of data
The document discusses different types of data classification. It defines classification as arranging things into groups based on similarities. The key types of classification discussed are:
- Geographical classification, which groups data by location.
- Chronological classification, which groups data by time period.
- Qualitative classification, which groups non-numerical data by attributes.
- Quantitative classification, which groups measurable numerical data into ranges.
- Alphabetical classification, which lists data in alphabetical order.
Data Analysis
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Dear viewers Check Out my other piece of works at___ https://healthkura.com
Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Assessment of Qualitative Data, Qualitative & Quantitative Data, Data Processing
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- Graphical representation
If anyone is really interested about research related topics particularly on data collection, this presentation will be the best reference.
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- Research Design by J. W. Creswell
- Internet
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Determine optimal factor settings
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This document provides an overview of classifying and tabulating data. It discusses concepts like variables, frequency distributions, and principles of data classification. The objectives of classification are to condense large amounts of data to reveal patterns and enable statistical analysis. Different parts of tables and types of tables are also described. Frequency distributions summarize how observations are distributed across classes.
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Validation ensures data entered complies with rules by restricting values and checking data type, presence, length, references to other fields or tables, format, and more. Verification compares entered data to its source to check for errors, such as by proofreading, double data entry where two people enter the same data and it is automatically compared, or other methods.
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Validation ensures data is reasonable before being accepted into a system through various checks like range, length, type, presence, format, and check digits. Verification checks data is accurately copied or transferred by methods such as double entry where data is entered twice and compared, or visual screen checks where users confirm displayed data is correct. Together validation and verification help produce accurate data for computer systems.
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This document discusses the process of data analysis, which includes editing, coding, classification, and tabulation of raw data collected during research. It explains that after data collection, the researcher must process and analyze the data. Key steps include editing the data for accuracy and completeness, coding the data by assigning numeric or alphabetic values to response categories, classifying the data into groups based on common attributes, and tabulating the data by organizing it into tables for further analysis and interpretation. Computer software can facilitate large-scale data processing and tabulation.
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The document discusses different types of data that can be collected in statistics including categorical vs. quantitative data, discrete vs. continuous data, and different levels of measurement for data including nominal, ordinal, interval, and ratio scales. It also discusses key concepts such as parameters, statistics, populations, and samples. Potential pitfalls in statistical analysis are outlined such as misleading conclusions, nonresponse bias, and issues with survey question wording and order.
Editing, Coding & Tabulation
1. The document discusses various steps involved in data processing such as editing, coding, tabulation and cross-tabulation.
2. Editing involves detecting and correcting errors in raw data to ensure accuracy, completeness, consistency and uniformity. Coding assigns numerical codes to questionnaire responses to simplify analysis.
3. Tabulation organizes data into tables or lists to facilitate analysis and comparison. It can be univariate, tabulating one variable, or bivariate/multivariate, tabulating relationships between two or more variables.
4. Cross-tabulation summarizes the relationship between two or more categorical variables by classifying data into a contingency table. It provides a basic understanding of how variables are interrelated.
Data and its Types
This document discusses different types of data including:
- Qualitative data which describes attributes that can be observed but not computed, and quantitative data which can be measured numerically.
- Primary data is collected first-hand for a specific purpose, while secondary data has already been collected in the past.
- Discrete data takes only certain values, while continuous data can take any value in a range.
Data Models
A data model is a set of concepts that define the structure of data in a database. The three main types of data models are the hierarchical model, network model, and relational model. The hierarchical model uses a tree structure with parent-child relationships, while the network model allows many-to-many relationships but is more complex. The relational model - which underlies most modern databases - uses tables with rows and columns to represent data, and relationships are represented by values in columns.
Classification of data
The document discusses different types of data classification. It defines classification as arranging things into groups based on similarities. The key types of classification discussed are:
- Geographical classification, which groups data by location.
- Chronological classification, which groups data by time period.
- Qualitative classification, which groups non-numerical data by attributes.
- Quantitative classification, which groups measurable numerical data into ranges.
- Alphabetical classification, which lists data in alphabetical order.
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Data analysis is a process that involves gathering, modeling, and transforming data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. It describes several major techniques for data analysis, including correlation analysis, regression analysis, factor analysis, cluster analysis, correspondence analysis, conjoint analysis, CHAID analysis, discriminant/logistic regression analysis, multidimensional scaling, and structural equation modeling.
Data Quality Control
This document discusses steps for ensuring data quality control, including quality assurance and quality control. It describes performing pilot testing and pre-testing to refine data collection instruments and procedures before full data collection. It emphasizes standardizing procedures through training and manuals. It also discusses designing data collection tools, using a codebook to define variables, implementing an audit trail for data management, performing quality control checks during data collection, and inspecting data for errors after collection. The goal is to systematically implement procedures to ensure high quality data throughout the research process.
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This document discusses primary and secondary data sources. Primary data is originally collected by a researcher specifically for their study, through methods like interviews or surveys. Secondary data has already been collected by someone else for another purpose and includes sources like published books, government reports, media sources, and internal company records. When using secondary data, researchers must evaluate if the data is relevant to their research objectives, accurate, and collected through reliable methods. They should also consider if the data could have errors from the original study. Secondary data sources in Nepal include government organizations, research agencies, universities, and trade organizations.
The database applications
The document discusses databases and database applications. It defines a database as a collection of organized data that can be easily accessed and managed. A database management system (DBMS) is software that allows users to create, retrieve, update and manage this data. Examples of popular DBMS software include Microsoft SQL Server, MySQL, and Oracle. Database applications are computer programs designed to efficiently collect, manage and share information from a database. Common examples of database applications mentioned are library systems, airline reservation systems, and content management systems for websites.
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Dear viewers Check Out my other piece of works at___ https://healthkura.com
Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Assessment of Qualitative Data, Qualitative & Quantitative Data, Data Processing
Presentation Contents:
- Introduction to data
- Classification of data
- Collection of data
- Methods of data collection
- Assessment of qualitative data
- Processing of data
- Editing
- Coding
- Tabulation
- Graphical representation
If anyone is really interested about research related topics particularly on data collection, this presentation will be the best reference.
For Further Reading
- Biostatistics by Prem P. Panta
- Fundamentals of Research Methodology and Statistics by Yogesh k. Singh
- Research Design by J. W. Creswell
- Internet
Database Design
The document discusses database design and the design process. It explains that database design involves determining the logical structure of tables and relationships between data elements. The design process consists of steps like determining relationships between data, dividing information into tables, specifying primary keys, and applying normalization rules. The document also covers entity-relationship diagrams and designing inputs and outputs, including input controls and designing report formats.
Exploratory data analysis data visualization
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
Classification of data
Data classification is the process of organizing data into categories for effective use. There are several types of data: qualitative data like nominal and ordinal data; quantitative or interval data that are measurements; and data classified by chronological or temporal bases. Qualitative nominal data categorizes attributes without order, while ordinal data ranks attributes. Quantitative data includes discrete counts and continuous measurements. Chronological data classifies by location and temporal data by time occurrence. Classification can be one-way based on a single characteristic, two-way based on two characteristics, or multi-way based on multiple characteristics.
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This document discusses data mining and its applications. It defines data mining as using algorithms to discover patterns in large data sets beyond simple analysis. It then provides examples of data mining applications, including market basket analysis, education, manufacturing, customer relationship management, fraud detection, research analysis, criminal investigation, and bioinformatics. The document also outlines the typical stages of the data mining process: data understanding, data preparation, modeling, evaluation, and deployment.
Data mining techniques unit 1
This document provides an overview of data mining techniques and concepts. It defines data mining as the process of discovering interesting patterns and knowledge from large amounts of data. The key steps involved are data cleaning, integration, selection, transformation, mining, evaluation, and presentation. Common data mining techniques include classification, clustering, association rule mining, and anomaly detection. The document also discusses data sources, major applications of data mining, and challenges.
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presentation of data
This document discusses different types of data and methods for presenting data. It describes qualitative and quantitative data, discrete and continuous data, and primary and secondary data. It also covers nominal and ordinal data. Common methods for presenting data include tabulation and various charts or diagrams. Tabulation involves organizing data into tables, following specific rules. Charts allow visualization of data and include bar charts, histograms, frequency polygons, cumulative frequency diagrams, scatter diagrams, line diagrams, and pie charts. Each chart has specific purposes and guidelines for effective presentation of data.
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This document discusses various methods for presenting data, including text, tables, and graphs. It provides classifications of data types and outlines principles and criteria for selecting presentation methods. Text can convey brief quantitative information and interpretation, while tables and graphs are best for larger datasets. Tables arrange data in rows and columns for comparison and have strengths in precision. Graphs simplify complex information through visual patterns and are effective for exploring relationships. Selection of a presentation method depends on factors like the size, scope, and time constraints of a study.
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Data validation
- 2. What is Data Validation? It is the process of ensuring that a program operates on clean, correct and useful data. It uses routines, often called "validation rules" or "check routines", that check for correctness, meaningfulness, and security of data that are input to the system that is the computer checks thr data you enter and if it is correct and make sence.
- 3. Methods for checking for data validity •Visual/manual •Aggregation •Reviewers guide •Auto data checks •Record counts •Spell checks •Have data provider review
- 4. Differences bt validation and verification Types of validation
- 5. Data Type
- 6. How? List menu Create a validation rule Input mask
- 7. Validation types Format check – e.g. two letters followed by two numbers – PR15 would be valid but PRX71 would not. Presence check – checking that the field contains any data. Range check –checking that the data is between certain numbers or letters, e.g. A–E. Lookup check – checking that only values from a set list can be entered, e.g. M or F. Size check – checking data falls within a minimum or maximum number of characters.
- 9. Verification Is done by the user By entering the data twice and comparing the values entered is a form of verification. Verifying that there are no spelling mistakes Correct data is entered - ie correct name, address, postcode
- 10. Validation Is checked by software Is it the correct data type – date of birth in a currency field Would not pick up spelling mistakes Would not detect incorrect data of the same data type being entered