Assignment Quantitative Data Analysis

Topics: Statistics, Scientific method, Quantitative research Pages: 8 (1368 words) Published: May 18, 2015
Data analysis is an attempt by the researcher to summarize collected data either quantitative or qualitative. Generally, quantitative analysis is simply a way of measuring things but more specifically it can be considered as a systematic approach to investigations. In this approach numerical data is collected or the researcher transforms collected or observed data into numerical data. It is ideal for finding out when and where, who and what and any relationships and patterns between variables. This is research which involves measuring or counting attributes (i.e. quantities). It can be defined as: “The numerical representation and manipulation of observations for the purpose of describing and explaining the phenomena that those observations reflect is called quantitative analysis” Quantitative analysis gives base to quantitative geography and considered as one of important parts of geographical research. As, subject matter of quantitative geography is comprehended by the following key issues: Collection of empirical data

Analysis of numerical spatial data
Development of spatial methods for measurements, theories and hypothesis Construction and testing of mathematical models of spatial theory Concisely, all above mentioned activities develop understanding of spatial processes. Quantitative geography is not bound by deep-routed philosophical stance as its most obvious, efficient and reliable mean of obtaining knowledge. Thus, it might be labeled all quantitative researchers as positivist or naturalist (Graham, 1997). Thus, its purpose is not to produce flawless data but rather is to maximize knowledge with minimum of error. Therefore, verification of quantitative research can be done by determining its significance in discipline. Sources of quantitative data:

We can gather quantitative data in a variety of ways and from a number of different sources. Many of these are similar to sources of qualitative data, for example: a) Questionnaires: these are series of questions and other prompts for the purpose of gathering information from respondents. b) Interviews: a conversation between two or more people (the interviewer and the interviewee) where questions are asked by the interviewer to obtain information from the interviewee. c) Observation - a group or single participants are manipulated by the researcher, for example, asked to perform a specific task or action. Observations are then made of their user behavior, user processes, workflows etc, either in a controlled situation (e.g. lab based) or in a real-world situation (e.g. the workplace). d) Transaction logs - recordings or logs of system or website activity. e) Documentary research - analysis of documents belonging to an organization. Level of measurements

Quantitative data is associated with any of following four levels of measurement:   Nominal: data has no logical and classified into separate groups which are not interlinked. For Example, Male or Female as mentioned below: There is no order associated with male nor female

Each category is assigned an arbitrary value (male = 0, female = 1) Ordinal: data has a logical order, but the differences between values are not constant   Example: T-shirt size (small-scale, large scale industries)

Example: economic activities (from Primary to quinary)
Interval: data is continuous and has a logical order. It has standardized differences between values, but no natural zero   Example: Fahrenheit degrees
Ratio (scale): data is continuous, ordered, has standardized differences between values, and a natural zero   Example: height, weight, age, length
Quantitative data analysis procedure:
Data tabulation (frequency distributions & percent distributions) Descriptive data
Data disaggregation
Moderate and advanced analytical methods
Data tabulation: The first thing researcher should do with his data is to tabulate results for the different variables in data set. This process will give a comprehensive...
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