What is the difference between numerical and qualitative identity

The boy I was ten years ago now has the property of being 31 years old, being married, having children, etc. The boy I was ten years ago is sitting in this chair typing right now; he has grown up.

Since he is me, he has all the same qualitative properties that I have and I all of the same qualitative properties that he does. That boy was shorter than you are now, and height is a property. No Glenn, I do have all the properties that the boy I was 20 years ago has. Where would you put it? Well, on me, of course. Where would you put that sticky note? If you want to know what properties the referent of that description has, again, you must look at me.

The subject is what the title indicates, but van Inwagen spends a lot of time talking about the issue of trans-temporal identity as well I borrowed the sticky-note thing from him. And when I read that article, a lot of confusions I had about the notion of numerical identity faded away. Of course, you may disagree that they did.

Therefore in order for anything to have numerical identity with me, something must be the present incarnation of me, and so must have all the qualities that I now have. Of course, everyone agrees that in order to be the current incarnation of you, a thing must have the qualities that you now have. I think that contained in what I have said already is adequate reason to continue to maintain that Kenny at t1 can have different qualities from Kenny at t2 where t2 is 20 year after t1 , and yet still have numerical identity with Kenny at t2.

Ergo in the sense that I am obviously talking about, I dogmatically affirm that numerical identity does not require qualitative identity. I agree that I can change qualities without ceasing to be numerically identical to the individual that I was.

But that does not entail that numerical identity does not require qualitative identity. If I start out having property F, then I am numerically identical to something that has F. If I subsequently lose property F, then I am numerically identical to something that lacks F. But it is never the case that I am numerically identical to something that has F and to something that lacks F. Again, this last part is just the indiscernibility of identicals, which, to my knowledge, is an almost entirely uncontroversial principle.

Let me put the following dilemma to you: Either 1 the boy I was 20 years ago presently exists or 2 the boy I was 20 years ago does not presently exist. So, if 1 , that description does presently refer to me. For example: face-to-face interviews, telephone interviews, remote interviews Longitudinal studies Website interceptors Online polls Systematic observations Experiments Qualitative vs quantitative data In terms of the actual data, here are some of the key differences: Qualitative data is not countable.

Turning qualitative data into quantitative data You can turn qualitative data into quantitative data, and vice versa. Analyzing quantitative data Because quantitative data is based on numbers, some form of mathematical analysis will be required.

Tools like Excel, SPSS, or R can be used to calculate: The mean scores of your data also known as the average The frequency of a particular answer The correlation or causation between two or more variables The validity or statistical significance of your results Analyzing qualitative data Because of its unstructured and somewhat ambiguous nature, analyzing qualitative data involves a more interpretive style of analysis. Ways to analyze qualitative data: Coding your data with tags and conducting a thematic analysis.

You can also use coding for a range of other similar analysis techniques like content analysis Analyze qualitative video, audio, and text with Dovetail, used by teams worldwide. How to create and analyze open-ended survey questions Nikki Anderson.

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If you have been called upon to fill in a document verbally or orally that asks you to rate on a scale of , you have participated in Quantitative research. Demographic information falls under this category: age, sex, occupation, area of residence, etc.

There is a tendency in the scientific community to give more credence to quantitative research, but this attitude is a mistake as both methodologies have their place in gathering research. You can examine any type of records involved if they pertain to the experiment, so the data is extensive.

The best practices of each help to look at the information under a broader lens to get a unique perspective. Using both methods is helpful because they collect rich and reliable data, which can be further tested and replicated. An interview is the most common qualitative research method.

This method involves personal interaction either in real life or virtually with a participant. Interviews are very popular methods for collecting data in product design. Data analysis by focus group is another method where participants are guided by a host to collect data. Within a group either in person or online , each member shares their opinion and. For the visual learner, here are some examples of both quantitative and qualitative data:.

The fundamental difference is that one type of data answers primal basics and one answers descriptively. What does this mean for data quality and analysis? You need both in order to truly learn from data—and truly learn from your customers. Learn how the best-of-the-best are connecting quantitative data and experience to accelerate growth. So how do you determine which type is better for data analysis?

Quantitative data is structured and accountable. This type of data is formatted in a way so it can be organized, arranged, and searchable. Think about this data as numbers and values found in spreadsheets—after all, you would trust an Excel formula. Qualitative data is considered unstructured. This type of data is formatted and known for being subjective, individualized, and personalized. Anything goes. But for complete statistical analysis, using both qualitative and quantitative yields the best results.

At FullStory, we understand the importance of data, which is why we created a Digital Experience Intelligence solution that analyzes customer data for better insights. Our DXI platform delivers a complete, retroactive view of how people interact with your site or app—and analyzes every point of user interaction, so you can scale. As mentioned throughout this guide, quantitative data are a critical part of improving your website.

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