DATA PROCESSING EDITING^J CODING^Jclassification.pptx
- 1. DATA PROCESSING EDITING, CODING & Classification
- 2. Data Processing • Data processing involves the conversion of raw data into a format that is understandable and usable. • It encompasses various operations to manipulate data, enabling analysis and decision-making. • Essentially, data processing transforms information from its raw state into meaningful insights. Importance of Data Processing: • Data processing ensures accuracy, consistency, and reliability in derived information. • It converts raw data into a suitable format for analysis, aiding in informed decision-making
- 3. Steps in Data Processing The steps involved in data processing are as follows, and we will explore each of them in more detail in the upcoming slides. EDITING Data Cleaning Data Adjusting CODING CLASSIFICATION tabulation graphical representation Involves reviewing and correcting errors or inconsistencies in collected data to ensure its accuracy and reliability. Involves identifying and rectifying errors, duplicates, or missing values in a dataset to improve quality and reliability. Involves modifying or standardizing data to ensure consistency and comparability across different sources or periods. Assigns numerical or categorical labels to data for easier analysis facilitating organization and categorization. Categorizes data into groups or classes based on predefined criteria, enabling systematic organization and analysis. Presents data in a structured format, typically in tables, summarizing information for easy comparison and analysis. Enhancing understanding and interpretation through visual patterns and trends.
- 4. Editing of data Data editing is reviewing and correcting errors, inconsistencies, and inaccuracies in collected data. It is used to ensure the quality and reliability of the data before further analysis or processing. Data editing helps improve the accuracy of results and conclusions drawn from the data, reducing the risk of making incorrect decisions based on flawed information. Additionally, it enhances the integrity of datasets, making them more suitable for sharing and reuse by other researchers or stakeholders. Overall, data editing is critical to maintaining data quality and integrity throughout the research or analysis process.
- 5. Data Cleaning Data cleaning, also known as data cleansing, is detecting and correcting errors, inconsistencies, and inaccuracies in a dataset to improve its quality and reliability. Common Data Cleaning Tasks • Handling Missing Values: Identifying and dealing with missing values through techniques such as imputation or deletion. • Removing Duplicates: Detecting and removing duplicate records or entries to ensure data integrity. • Standardizing Formats: Converting data into a consistent format (e.g., date formats, units of measurement) for easier analysis. • Correcting Errors: Identifying and correcting errors such as typos, inconsistencies, or outliers that may affect the validity of the dataset.
- 6. Data Adjusting Data adjusting involves modifying or standardizing data to ensure consistency, comparability, and accuracy across different sources or periods. Common Data Adjusting Techniques: • Normalization: Scaling numerical data to a common scale to remove variations and allow for comparison. • Standardization: Converting data into a standard format or unit of measurement to ensure consistency across different sources. • Time Series Adjustment: Adjusting time series data for seasonal variations, trends, or other factors to enable accurate analysis and forecasting. • Comparative Analysis: Adjusting data to account for differences in demographic or population characteristics when comparing across regions or groups.
- 7. On field editing Review and correct errors directly within individual data fields or variables during data collection. Example: Checking for inaccuracies in age or income data in a survey dataset. Advantages • Real-time Error Detection: Enables immediate identification and correction of errors during data collection, improving data accuracy. • Cost Efficiency: Reduces the need for extensive post-processing corrections, saving time and resources. • Enhanced Data Quality: Ensures data integrity and reliability by addressing errors at the source. • Improved Data Collection Process: Streamlines data collection procedures, leading to more efficient and effective data management. Methods • Manual Inspection: Data analysts manually review each data field for errors or discrepancies, correcting them as necessary. • Automated Validation: Implementing automated validation rules or algorithms to flag and correct common errors, such as out-of- range values or invalid formats. • Double Data Entry: Having two independent data entry operators input the same data separately. • Use of Standardized Forms: Designing data collection forms with built-in validation checks and clear instructions to minimize errors during data entry.
- 8. Central Editing Review and correct errors in a centralized location after data collection from various sources. Example: Aggregating sales data from regional branches in a multinational corporation. Advantages • Consistency: Ensures consistent data quality standards across sources. • Comprehensive Review: Facilitates a thorough review of the entire dataset. • Timeliness: Allows for prompt identification and correction of errors, minimizing potential data inaccuracies. • Improved Decision-Making: Enhances the reliability of data, leading to more informed decision-making processes. • Enhanced Data Integrity: Boosts the overall integrity and trustworthiness of the dataset, supporting long-term data-driven initiatives. Methods • Automated Validation: Using software to automatically check for errors in the dataset and flag inconsistencies for review. • Expert Review: Involving subject matter experts to manually review the dataset for errors and inconsistencies based on their domain knowledge. • Data Cross-Referencing: Comparing data across different sources or databases to identify and resolve inconsistencies. • Feedback Loop: Establishing a mechanism for feedback between data collection sources and the editing team to address errors collaboratively.
- 9. CODING Coding involves assigning numerical or categorical values to qualitative data for analysis and interpretation. Significance Data Standardization Statistical Analysis Enhanced Interpretation Efficient Data Processing
- 10. SAMPLE SURVEY
- 11. SAMPLE SURVEY
- 12. Codebook for the sample survey S.NO Name Question Values/codes 1 Ex Rating How would you rate this exhibition? Excellent = 4; Good = 3; Fair = 2; Pbor = 1 2 Attend Did you attend today By yourself = 1; Family = 2; Friends = 3; F+F = 4 3 LearnOf How did you learn about today’s exhibition opening? Friends = 1; Family = 2; Social media = 3; Local media = 4; Other = 5 4 Return Are you likely to attend future events at the gallery? Yes Def = 4; Maybe Y = 3; Maybe N = 2; Def NO = 1 5 preTalk Did you find the pre-event talk by the artist informative? Yes Def = 4; TSE = 3; Not really 2; NAA = 3 6 Member Are you a member Of the gallery? Yes = 1; No = O 7 SocMed Do you follow gallery updates On social media ? Yes = 1; NO = O 8 Age As stated in years 9 Sex Gender Female = 1; Male 2 10 Employ Current employment Status FT = 4; PT = 3; C = 2; Retired = 1 Unem = O 11 Income Yearly personal income <$15 = 1; $15-30 = 2; $31-50 = 3; $51-80 = 4; >$80 = 5 12 Local Do you live locally? Yes = 1; No = O 13 WhichSM Which kind of social media? Facebook = 1; Instagram = 2; Twitter = 3 14 LearnOf How did you learn about today's exhibition opening "other" 1 = Public notice; 2 = Directly from the artist 15 Missing values 99
- 13. Classification of Data Data classification is the process of organizing and categorizing data based on predefined criteria or characteristics. Importance Facilitates Effective Analysis Simplifies Complex Data Aids Comparison Required by Research Analytics Methods
- 14. Types of Data Classification 1.Categorical Data: Categorical data consists of distinct categories or groups with no inherent order. Examples include gender, marital status, and product types. 2.Ordinal Data: Ordinal data represents categories with a specific order or ranking. Examples include ratings (e.g., 1 to 5 stars), education levels (e.g., high school, college, graduate), or income brackets. 3.Interval Data: Interval data represents numerical values where the intervals between values are meaningful but there is no true zero point. Examples include temperature measured in Celsius or Fahrenheit.
- 15. Thank You Done by Praduman sharma 22E2354 P Aadityaa 22E2345