Quantitative Analysis
We live in a data driven society where people are increasingly looking to numbers to describe, explain, and predict phenomena in our businesses, charities, sports, education, personal lives, and more. Quantitative analysis is the most comprehensive way to draw conclusions from direct observation and measurement. Our extensive quantitative analytic services empower you to collect and analyze data to answer your most pressing research needs.
Data Integrity
What is it? Data integrity refers to the quality and accuracy of data, which is critical to data being reliable, valid, reproducible, and transferrable. Reliable information obtained from data drives suitable choices and decision-making towards the optimal direction, which contributes to overall success. Because of this, data integrity is a top priority for most enterprises and researchers.
Data integrity includes various assessment steps, including error checking, data validation, and more. Of all processes, data security protects data against data corruption and unauthorized access, and it plays an important role in maintaining data integrity. If you have concerns on how to maintain data integrity, let our consultants help you.
How can we help you?
- Data preparation to ensure high quality of data
- Data validation to avoid duplicates and inaccurate values
- Reliability assessment
- Appropriate analysis plan development
- Appropriate complexity
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Learn MoreArticles and White Papers About Data Integrity
What Are Some Data Collection Challenges and How Do You Overcome Them? (Part 3 of 3)
Articles and White Papers About Considerations How do You Develop an Evaluation Plan? Read More How Do You Get Started With Your Program Evaluation? Read More What Do You Need to Consider About Program Evaluation? Read More How Does Your Organization Build Its Credibility? Read More Load More
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What Are Some Data Collection Challenges and How Do You Overcome Them? (Part 2 of 3)
Articles and White Papers About Considerations How do You Develop an Evaluation Plan? Read More How Do You Get Started With Your Program Evaluation? Read More What Do You Need to Consider About Program Evaluation? Read More How Does Your Organization Build Its Credibility? Read More Load More
Read More
What Are Some Data Collection Challenges and How Do You Overcome Them? (Part 1 of 3)
Articles and White Papers About Considerations How do You Develop an Evaluation Plan? Read More How Do You Get Started With Your Program Evaluation? Read More What Do You Need to Consider About Program Evaluation? Read More How Does Your Organization Build Its Credibility? Read More Load More
Read More
How Can We Reduce Bias in Our Research?
Articles and White Papers About Data Integrity What Are Some Data Collection Challenges and How Do You Overcome Them? (Part 3 of 3) Articles and White Papers About Considerations How do You Develop an Evaluation Plan? Read More How Do You Get Started With Your Program Evaluation? Read More What...
Read MoreFAQ About Data Integrity
Research design, research instrumentation,
Certain patterns in responses warrant cases to be removed to protect the integrity of data. Cases should be flagged where respondents answer too quickly or slowly. Additionally, responses may need to be removed from participants who drop off after certain points, answer with limited to no variance or answer antithetical questions inconsistently.
Non-normal data can be analyzed using non-parametric analyses or other analysis that require less strict assumptions. Additionally sometimes it is beneficial to transform non-normal quantitative data into categorical data which can be normally distributed by using ranges of values for categories.
Ideally there is less than 5% missing data, but the importance and impact of missing data depends upon what data is missing, what type of data is collected, the intended analysis and the relationship between variables that are missing large amounts of data.
The source of data collection should optimize utility, affordability, and integrity. This will depend on who is collecting what data and from where. The answer to these questions will direct a strategy toward optimal data source and collection.
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