Quantitative data gathered are in raw format and it needs to refine and organize in systematic manner in order to help research document readable and clear to understand. Once data is gathered researcher converts collection into static data based on category and types of questions, example; if survey says 6 out of 10 traditional hinge survived for only 5 years from installation date, then it is survey of hinge life expectancy and such counts are recorded in numerical format.
Why quantitative analysis used? because all results will support or oppose the statement previously made as an idea or a proposition made as a basis for reasoning, without any assumption of its truth. example; training engineers in research and development project will definitely improve productivity then research needs to explain those answers and to be documented as theory. As most researchers are considering to develop data based on reasoning they tend to conduct experiment in there data gathering, example; building design concepts a researcher may form two groups, one with qualified engineers and another with non-qualification persons but with experience and see the result of both groups and measure the varieties based on personal feelings, tastes or opinions and thing aimed.
As data are gathered from various resources as interviews, questionnaires, observations, documentary research, transaction logs, etc. These data need to processed by organizing, summarizing and performing exploratory data analysis (EDA) in order to make data readable when presented, by polishing data with tables, graphical representations and providing statistic summary.
These data are analysed to measure similarities in responses example; 70% of the R&D projects are conducted in-house within the organization.
To study differences within research conducted, example; medium scale industries R&D projects are performed 60% in-house, for Large scale 70% and for Government sectors 85%.
Understand the relationship within study, example; relationship between the R&D project conducted within locality and their project conducted per annul.
Quantitative research do form issue as they are designed to find 4W (who, what, when, where), quantitative analysis are formed with questions and they need to be answered by justifiable theory. To be clear there are two hypothesis (a proposition made as a basis for reasoning, without any assumption of its truth) one is null hypothesis which defines no change and other experimental hypothesis as we saw in second paragraph. We have to note there are no decisions made from such data as they are just measurable either to support research or to contradict with research. The whole purpose of quantitative research is to concert concepts into numerical format, then using those numerical data form variables and set scales to quantify variables.
Relationship between cause and effect are essential to measure success of data gathered, which are understood by identifying variables in three ways independent, dependent & control. When the variables are manipulated by researcher they are known as independent variable as training engineers, etc. Then to measure the effectiveness of independent variable dependent variable is used such as measuring time. As a part of experimentation some variable needs to be controlled even thou they are independent as qualification, time, etc.
So when we train engineers on R&D project they take less time to complete project compared to persons with experience but no qualification and training, this is cause (training & qualification) and effect (project duration) comparison. Wikipedia states a necessary relationship between one event (called cause) and another event (called effect) which is the direct consequence (result) of the first _(_[_http://en.wikipedia.org/wiki/Causality)_](http://en.wikipedia.org/wiki/Causality))
Researches are generalized or applied to certain extent based on study, if study is based on R&D project in Australia conducted per annual where our study can be referring to other people study (which is population validity) and or settings (ecological validity) which produces result.
Research need validity which is repeating research test and trials in order to establish reliability of its findings (measurements).
Best tool for small data analysis will be excel which compute basic algorithms and when data becomes complex the most expert advice comes to use statistical analysis software as SPSS _(Statistical Package for the Social Sciences)_ and such. As quantitative data may be only useful as the original data, act as data collection tool, define operations, produce research question.
Its important to write down research findings very beginning and refine down to specifics, which helps to choose proper data collection and analysis tools. If researcher finds future possibilities then its good to use descriptive statistics, where tools like survey and such can be use to generalize results with wider population using inferential statistics _(use a random sample of data taken from a population to describe and make inferences about the population)_ some times SPSS is used.
As one cannot generalize whole population so there have to be random sample, where at sampling researcher needs to be known what to be achieved and availability of participants. Few approaches are random sampling, stratified sampling, cluster sampling, convenience sampling, accidental sampling. Any inferential statistical analysis is to be considered based on sample results not to the large population.
#### **Referencing Quantitative Readings**
- BAWDEN, D. (1990). User-oriented evaluation of information systems and services . England : Gower.
- BRYMAN, A. (2006). Integrating quantitative and qualitative research: how is it done? Qualitative Research, 6 (1), pp.97-113.[Online] Access this resource online
- BURNS, R., 2000. Introduction to Research Methods London : Sage.
- GRIFFITHS , J.R. , Hartley, R.J. and Willson, J.P. (2002). An improved method of studying user-system interaction by combining transaction log analysis and protocol analysis. Information Research , 7 (4). [Online] [http://informationr.net/ir/7-4/paper139.html](http://informationr.net/ir/7-4/paper139.html)
- KRUGER, D. J. (2003) 'Integrating quantitative and qualitative methods in community Research', The Community Psychologist, 36, pp.18-19.
- NEWTON , R.R. and RUDESTAM, K.E., 1999. Your statistical consultant: answers to your data analysis questions London : Sage
- O'NEILL, R. (2008) The advantages and disadvantages of qualitative and quantitative research methods. [Online] [http://www.roboneill.co.uk/papers/research_methods.htm](http://www.roboneill.co.uk/papers/research_methods.htm)
- ROBSON, C. (2002). Real world research: a resource for social scientists and practitioner-researchers. London : Blackwell.
- ROWNTREE, D., 1991. Statistics without tears: a primer for non-mathematicians London :Penguin.
- STEPHEN, P. and HORNBY, S., 1997. Simple statistics for Library & information professionals 2 nd ed. Library Association
- STRAUSS, A. and CORBIN. J. Basics of qualitative research ( 2nd ed). Sage, 1998.