Computer Science, asked by sukhchain8298, 9 months ago

Explain different data representation methods.

Answers

Answered by sriramsriran68
1

Answer:

Explanation:

There are many different ways of representing longitudinal data structures

Some depend on the nature of the data ...

...or on the nature of the analysis

Others are largely equivalent

Let's take pure panel information: discrete evenly spaced state observations

One file per wave, with identical structure, identified by PID

One file, one record per respondent, identified wave by variable name/position

One file, one record per respondent per wave, identified by PID and a wave-number index variable.

These are equivalent in their information content:

We can move between them relatively easily (especially between types 2 and 3, in Stata and later versions of SPSS)

But differ in their ease of use for different purposes.

For example, to cross-tabulate a variable for a given pair of waves, type 2 is clearly better.

However, if you want to cross-tabulate current status with last year's status, pooling across waves, type 3 is better.

Status history data is relatively simple in principle: there is an observation for each time unit per person

An easy way to represent is as a wide horizontal file: one variable per time unit

Broadly equivalent is a long vertical file: one record per person-time-unit

The practical complication is combining waves

If (as in ECHP design) the reference period is a calendar year, some respondents do not report their recent experience

If (as in BHPS) a variable length reference period is used there will be overlap

With overlap, a decision for the analyst: which report to accept?

In SPSS, handling wide `calendars' by VECTOR/LOOP is straightforward

In Stata, handling long vertical files is easy

Event history data is a little more complicated

An efficient representation is to record the dates and destinations of all transitions: this is a pure event history (the act of observation must be recorded as an event)

Closely related is spell or episode history: store start of spell, state and end-date (including `on-going at time of observation' or `censored')

However, for many purposes event/episode data can be transformed into state histories, with a variable per time unit

This can be wasteful, if the average spell length is much greater than one time unit: long strings of the same data

A bigger problem is that it loses information: for instance two successive jobs with the same characteristics look like one long job.

It's also harder to think in spell terms (how long, when did this spell end/start)

But if you need to relate status in many domains, it's very convenient (e.g., you want to know job status and marital status at a particular time)

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