RESPOND TO SARAH AND CORTNEE POSTS IN A 100 WORDS
Sarah
Qualitative is the measure of the data’s quality. It can not be measured using statistical analysis and often refers to personal preferences, labels, or anything that can be represented by words. Quantitative is the measure of the data’s numerical values. There’s typically a type of order that can be used to group the values. Although both have variables, the meaning and use of those variables are not the same and can’t be measured equally. The advantages found when using quantitative data are that the information may be represented clearly as a number (quantity), organizable, discreteness, and there’s meaning behind the differences in the numbers. The advantages for qualitative data are being able to categorize and utilize the word value of the data and doesn’t require specific order to properly represent meaning. When it comes to my work, there are many examples of quantitative data that I utilize to properly determine a line of credit amount and when to know to pull back on shipments. The type of numerical data that I use are credit ratings, current balances, dates, calculating how much of their line is remaining, how much is to be received and applied against the account, percentages, averages, so on and so forth. An example of qualitative would be the differences between my girls. My oldest has curly, blonde hair, blue eyes, likes the color blue, and likes to draw. My youngest has straight, blonde hair, blue eyes, likes the color purple, and like to ride her scooter.
Cortnee post
Quantitative is a numerical value that is measured and ordered. Qualitative is a category that has variables.
I’ve been a nurse for over 25 years in cardiology and now work in medical eduation. We study risk factors for men and women, age of the patient, in relation to cardiac issues. Those could include hypertension (high blood pressure), myocardial infarction (heart attack), myocarditis (inflammed heart muscle) and a few other more cardiac conditions.
The patient’s age has a numerical value and is quanitative. The others are qualitative data and would be the other catagories such as men and women, family history, smoking history etc. We have just added a COVID vaccine category and a history of COVID sickness. We are seeing an increase in both of those with myocarditis and cardiac arrest.
I would love to hear if you know anyone with any heart issues after getting the vaccine or getting the virus. They clearly have a link in the data we are studying. We are seeing myocarditis in children for the first time so that is new data.
In response to John and , explain whether you agree or disagree with their conclusions. Provide specifics reasons as to why or why not.
John post
One specific consequence of the assassination of Dr. Martin Luther King is the militarization of some civil rights groups over time. Dr. King himself was a vocal proponent of peaceful protest; spearheading many campaigns for justice using this method. Often at odds with more militant civil rights groups, these organizations became empowered after his death; becoming some of the loudest voices in the civil rights movement in Dr. King’s absence. The message of these political groups was further supported by the assassination itself; being used to argue that civil rights activism is dangerous and requires more radical action and that strictly peaceful protest is ineffectual.
Kelly post
The assassination of Dr. Martin Luther King Jr. assisted in the passing of the 1968 Fair Housing Act, as well as the Voting Rights Act. His assassination created such outrage that riots and violence were occurring, and I think that the Fair Housing Act was passed to pacify the angry masses.
“The loss of King as an eloquent advocate of nonviolent protest definitely hurt the movement. And perhaps more important, the wave of racial violence that convulsed many cities in the wake of King’s death shattered whatever fragile political consensus might have been forming behind the idea of comprehensive reforms to address the root causes of racial discrimination and African-American poverty (Garrow, 2004)” (Mindedge, 2022) . I think that these events would have taken place anyway, but I do believe that the assassination created anger, brought attention to the movement, and necessitated quicker movement than would have occurred if he was not killed.
Quantitative is a numerical value that is measured and ordered. Qualitative is a category that has variables.
Introduction
If you’re trying to understand quantitative and qualitative data, you may be wondering what the difference is between them. The answer lies in their nature: one is numerical and one is not.
Quantitative is a numerical value that is measured and ordered.
Quantitative is a numerical value that is measured and ordered.
Data is quantitative information that can be analyzed, interpreted and used to make decisions. Examples of quantitative data include temperature in Celsius or Fahrenheit (C/F), miles per hour (mph) or kilometers per hour (kmph), etcetera.
Qualitative is a category that has variables.
Qualitative research is often used in the social sciences and psychology. It can be ordered or unordered, with either ordinal or interval categories. An example of qualitative data might be “How do you feel about this?”
Qualitative data is often qualitative research because it is not quantitative and therefore cannot be measured without having a specific scale to measure it on (ie: numerical values).
Quantitative can be ordinal, interval or ratio.
A quantitative value can be ordinal, interval or ratio. An ordinal scale is one in which the distance between each pair of numbers is equal; this means that if you have two people and they’re both taller than you, then your friend is as tall as your brother.
An interval scale has equal distances between all pairs of numbers but does not have true zero (the number zero). For example: 2<4<4<6…
A ratio scale has all the properties of an interval scale but also includes an undefined number representing none of something—like “a” for “not a” or 0 for “no”. For example: 1/32
Ordinal is ranked data with no equal distances between them. Examples include a ranking of movies or books from best to worst.
Ordinal data is ranked data with no equal distances between them. Examples include a ranking of movies or books from best to worst.
A person’s age is ordinal because it cannot be measured in the same way as their weight and height, for example. However, you could use ordinal measurements for other things such as your income or how often you exercise each week (e.g., “I exercise three times a week”).
Interval have equal distances between them. There is no true zero because it doesn’t represent none of something. Temperature measures are an example of interval data because 0 represents the point at which water freezes but not the total absence of temperature.
Consider the case of temperature measurements. Zero degrees is the point at which water freezes, but it’s not the total absence of temperature; rather, it represents a point where things are neither too hot nor too cold. Zero degrees Celsius is another example of an interval measurement because it doesn’t represent anything in particular—it just marks off one extreme from another.
Interval data can be ordered by adding or subtracting values (or both) and then comparing them to see how they differ from each other given their ordering position within some succession or sequence (for example: “The tenth largest city in America has more people than any other city except for its capital city”). Intervals also come with true zeros because there’s no way to measure something without ending up somewhere along that line between 0°C and 100°C (or below zero).
Ratio has all the properties of interval and also has a true zero representing none of something. Examples include counts and ages as well as height and weight measurements.
The true zero is the absence of something. Examples include count, age and weight measurements.
In this section we will look at some examples of ratios that are used in everyday life:
Height and weight: You can measure someone’s height by measuring their head to toe length (HEIGHT), then adding up all those numbers together to get their TOTAL HEIGHT. If you want to know how tall they are compared to another person, just divide one by another (TOTAL HEAD TOE EXTENSION).
Temperature: You can measure temperature using Celsius or Fahrenheit scales depending on where you live; however, if we’re talking about Celsius degrees only then saying “32” means 32 degrees Celsius which is pretty close!
Qualitative involves categories instead of numbers but can be ordinal, interval or ratios.
Qualitative data is usually categorized. It involves categories instead of numbers, and can be ordinal, interval or ratio.
Ordinal data is ranked in order from lowest to highest (1st, 2nd…).
Interval data has equal distances between values (e.g., 1–10).
Ratio data has a true zero at one end and positive infinity at the other end (e.g., 3:2).
Quantitative data has numbers and qualitative does not but both can be ordinal, interval or ratio data.
There are three types of data: ordinal, interval and ratio.
Ordinal data is ranked but has no equal distances between them. For example, if you were to measure the height of a person who had been born in 1940, their height would be an ordinal value because it’s not equal to another person’s height.
Interval data has equal distances between them but no true zero (e.g., -10 inches). For example: You could measure your friend’s age from 18 up until his birthday as an interval value since there are two possible answers for each year he could have turned 18 during that time period (18 and 19). This type of data also makes sense when we consider how many years ago someone was born; however unlike ordinal variables which only allow one answer per person/variable combination at most times during their lifetime (for example: “male”/”female”), interval variables would allow infinitely many answers per individual throughout their entire lifespan due to being able to change from one hour on earth into a newer day without having any other changes made within your system itself – even though technically speaking those changes may still happen regardless!
Conclusion
In conclusion, data is any information that has been measured and quantified. Quantitative data includes numbers and qualitative does not but both can be ordinal, interval or ratio data.