Assignment:

1. What is data?

.Data is a collection of facts, such as values or measurements.

It can be numbers, words, measurements, observations or even just descriptions of things.

Qualitative vs Quantitative

Data can be qualitative or quantitative.

• Qualitative data is descriptive information (it describes something)

Quantitative data, is numerical information (numbers)

2.       Explain the term ‘Instrumentation’.

Instrumentation is a branch of physics which deals with the measuring and controlling variables in a process industry, the instruments related to the process variables and its calibration. Some of  the industrial process variables are Level, Pressure, Temperature, Humidity, Flow, pH, Force, Speed etc,. The technology which used to design and develop an instrument for the measurement and control is called instrumentation technology.

Definition: Instrumentation is defined as "the art and science of measurement and control".

3.The three different ways in which data can be collected are:

       a)observation

       b)questionnaire

       c)interview

4.       Explain ‘data-collection instrument’.

        Refers to the device used to collect data, such as a paper questionnaire or computer assisted interviewing system.

5.       List down the various types of instruments and describe them in detail.

The various types of instruments are researcher  complete instrument, subject complete instrument and informant instrument

Researcher complete instruments:

1.   Rating scale 2. Interview schedule 3. Tally sheet 4. Flow chart 5. Performance check list 6. Anecdotal records 7. Time-emotion logs

Subject complete instruments:

1.questionnaires 2. Self checklist 3. Attitude scale 4. Performance inventories 5. aptitude test 6. Achievement test 7. Performance test 8. Projective devices 9. Sociometric devices

6.       Explain what is meant by the term ‘unobtrusive measures’ 

     Unobtrusive measures are measures that don't require the researcher to intrude in the research context. Direct and participant observation require that the researcher be physically present. This can lead the respondents to alter their behavior in order to look good in the eyes of the researcher. A questionnaire is an interruption in the natural stream of behavior. Respondents can get tired of filling out a survey or resentful of the questions asked.

Unobtrusive measurement presumably reduces the biases that result from the intrusion of the researcher or measurement instrument. However, unobtrusive measures reduce the degree the researcher has control over the type of data collected. For some constructs there may simply not be any available unobtrusive measures.

7.       Name the types of measurement scales you have learnt and give examples of each.

      An easy way to have a paper rejected is to have used either an incorrect scale/statistic combination or to have used a low powered statistic on a high powered set of data.

• Nominal
• Ordinal
• Interval
• Ratio

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Nominal

The lowest measurement level you can use, from a statistical point of view, is a nominal scale.A nominal scale, as the name implies, is simply some placing of data into categories, without any order or structure.
A physical example of a nominal scale is the terms we use for colours. The underlying spectrum is ordered but the names are nominal.
In research activities a YES/NO scale is nominal. It has no order and there is no distance between YES and NO.


and statisticsThe statistics which can be used with nominal scales are in the non-parametric group. The most likely ones would be:


mode
crosstabulation - with chi-square
There are also highly sophisticated modelling techniques available for nominal data.

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Ordinal

An ordinal scale is next up the list in terms of power of measurement.The simplest ordinal scale is a ranking. When a market researcher asks you to rank 5 types of beer from most flavourful to least flavourful, he/she is asking you to create an ordinal scale of preference.
There is no objective distance between any two points on your subjective scale. For you the top beer may be far superior to the second prefered beer but, to another respondant with the same top and second beer, the distance may be subjectively small.
An ordinal scale only lets you interpret gross order and not the relative positional distances.


and statisticsOrdinal data would use non-parametric statistics. These would include:


Median and mode
rank order correlation
non-parametric analysis of variance
Modelling techniques can also be used with ordinal data.

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Interval

The standard survey rating scale is an interval scale.
When you are asked to rate your satisfaction with a piece of software on a 7 point scale, from Dissatisfied to Satisfied, you are using an interval scale.
It is an interval scale because it is assumed to have equidistant points between each of the scale elements. This means that we can interpret differences in the distance along the scale. We contrast this to an ordinal scale where we can only talk about differences in order, not differences in the degree of order.
Interval scales are also scales which are defined by metrics such as logarithms. In these cases, the distances are note equal but they are strictly definable based on the metric used.
and statistics
Interval scale data would use parametric statistical techniques:
Mean and standard deviation
Correlation - r
Regression
Analysis of variance
Factor analysis
Plus a whole range of advanced multivariate and modelling techniquesRemember that you can use non-parametric techniques with interval and ratio data. But non-parametric techniques are less powerful than the parametric ones.

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Ratio

A ratio scale is the top level of measurement and is not often available in social research.The factor which clearly defines a ratio scale is that it has a true zero point.
The simplest example of a ratio scale is the measurement of length (disregarding any philosophical points about defining how we can identify zero length).The best way to contrast interval and ratio scales is to look at temperature. The Centigrade scale has a zero point but it is an arbitrary one. The Fahrenheit scale has its equivalent point at -32o. (Physicists would probably argue that Absolute Zero is the zero point for temperature but this is a theoretical concept.) So, even though temperature looks as if it would be a ratio scale it is an interval scale. Currently, we cannot talk about no temperature - and this would be needed if it were a ration scale.