· As we progress into this week , our Tappen (2015) text will begin to explore quantitative data management and basic quantitative data analysis. Research as you know is broken down into basically two types of data. The study methods are qualitative, quantitative, and their combination which is mixed methods. Each approach to research has their benefits as well as possible drawbacks.
Qualitative Data: Qualitative data is data that can be observed but cannot be measured. This type of data is often in contrast to quantitative data, which is data that can be measured. Qualitative data stems from the word quality and characterizes attributes or properties of an object. When comparing qualitative and quantitative data, a key difference is that quantitative data is measured in numbers. For example, qualitative data for a painting includes a description of the characteristics of the painting such as colors and the way it is painted. Quantitative data for the same painting includes measured data like its cost, which can be measured in dollars.
Quantitative Data: Quantitative data is any kind of data that can be measured numerically. For example, quantitative data is used to measure things precisely, such as the temperature, the amount of people in a crowd or the height of a structure. Although quantitative data usually involves numbers and equations, some data records actions, such as the frequency of human behavior.
Mixed Methods Research: Mixed methods is a research approach whereby researchers collect and analyze both quantitative and qualitative data within the same study. Growth of mixed methods research in nursing and healthcare has occurred at a time of internationally increasing complexity in healthcare delivery. Mixed methods research draws on potential strengths of both qualitative and quantitative methods, allowing researchers to explore diverse perspectives and uncover relationships that exist between the intricate layers of our multifaceted research questions. As providers and policy makers strive to ensure quality and safety for patients and families, researchers can use mixed methods to explore contemporary healthcare trends and practices across increasingly diverse practice settings.
As you progress as a research scholar you will soon discover how you may want to approach your research. However, for this week we will focus on numerical data that is associated with quantitative data. This is a part of the analysis and interpretation phase of your research endeavor.
Everyone knows as a research scholar; you will collect data as it applies to your study. Now what are we going to do with this data once it is collected? There are potential issues that you will encounter. If these issues are not managed correctly, they can quickly lead errors that will affect the outcomes of your research.
Quantitative research has an advantage over qualitative research as it is not subject to investigator bias. However, if one does not collect data correctly or there is an error in its execution, there are risks related to outcomes.
Data errors can include but are not limited to the following:
· Mathematical errors
· Missing items
· Incorrect scoring of rating scales
· Duplication of identification numbers assigned
These are just a few of the issues that could occur.
Chapter 18 (Basic Quantitative Data Analysis) will cover the following concepts:
· Managing the data
· Setting up a tracking system
· Developing a filing system
· Packet review and response
· Code creation for questions that are open-ended (requiring more than a yes or no response
· Selecting the software for the database
· Database creation
· Testing the program
· Codebook Development
· Data base creation
· Data input
Please review the following link related to our quantitative discussion.
Website Link: Quantitative Management
Video Link: Quantitative Data Collection
(Please note that part of this video also explores concepts related to qualitative data, this is to show are correlation between the two methods).
Video Link: Introduction to Quantitative Data Analysis and Statistics
Chapter 19 (Basic Descriptive Statistics) goes further into quantitative execution by covering basic data analysis:
· Data cleaning
· Missing data
· Replacing missing values
· Visual representations
· Stem and leaf
· Box plots
· Bar charts and pie charts
· Basic descriptive statistics
· Central tendency
· Bivariate Association
· Additional Measures of Association
The terms can initially seem overwhelming. However, as one continues to explore they will make sense to the new researcher.
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