Data analysis is the process of summarizing data that has been gathered, regardless of whether it is quantitative or qualitative. Quantitative analysis involves measuring and systematically studying numerical data to discover relationships and patterns between variables. It employs numerical representation and manipulation of observations to describe and explain phenomena. Quantitative analysis is fundamental in quantitative geography research, playing a critical role in gathering empirical data, examining numerical spatial data, and developing spatial methodologies for measurements, theories, and hypotheses.
Construction and testing of mathematical models of spatial theory aims to develop understanding of spatial processes. Quantitative geography, which is not bound by a deep-rooted philosophical stance, uses quantitative methods as its most obvious, efficient, and reliable means of obtaining knowledge. As a result, all quantitative researchers may be labeled as either positivist or naturalist according to Graham (1997). The purpose of quantitative geography is not to produce flawless data but rather to maximize knowledge while minimizing error. Consequently, verification of quantitative research can be achieved by assessing its significance within the discipline.
The sources of initiative data are diverse and can be collected in various ways and from multiple sources. These sources include questionnaires, which consist of a series of questions and prompts to gather information from respondents. Interviews, on the other hand, involve conversations between an interviewer and interviewee where questions are asked to obtain information. Lastly, observation involves manipulating a group or individual participants, such as instructing them to perform specific tasks or actions.
Observations are made of user behavior, user processes, and workflow in both controlled situations (such as a lab) or real-world situations (such as the workplace). Transaction logs are recordings or logs of system or website activity. Documentary research involves analyzing documents from an organization.
Quantitative data is associated with four levels of measurement. Nominal data consists of separate groups that are not interlinked, such as classifying individuals as male or female.
There is no order associated with male nor female. Each category is assigned an arbitrary value (male = O, female = 1).
Ordinal data has a logical order, but the differences between values are not constant. Examples include T-shirt size in both small-scale and large-scale industries, as well as economic activities ranging from primary to quandary.
Interval data is continuous and has a logical order. It also has standardized differences between values, but lacks a natural zero. An example of interval data is Fahrenheit degrees.
Ratio (scale) data is continuous, ordered, has standardized differences between values, and possesses a natural zero. Examples of ratio data include height, weight, age, and length.
The first step in the quantitative data analysis procedure is to tabulate the data. This involves creating frequency distributions and percent distributions for the variables in the data set. Tabulating the data provides a comprehensive view of the data and helps identify patterns. Frequency distribution organizes the number of individuals in each category.
It is referred to as inebriate analysis, which involves analyzing a single variable for descriptive purposes. An example of this is analyzing the gender variable, where the number of men and women in a sample or population is examined. Percent distribution shows the proportion of participants within each category (see below). The table reveals that 75% of students (n = 20) surveyed for the summer program reported satisfaction with the experience. Data descriptive refers to calculations that provide a description of the data set.
The most common descriptive measures used are:
– Mean – the numerical average of scores for a particular variable
– Standard deviation – square root of variance
– Minimum and maximum values – the highest and lowest value for a particular variable
– Median – the numerical middle point or score that cuts the distribution in half for a particular variable
– Mode – the most common number score or value for a particular variable
Fig. : showing the actual heights (at the shoulders) are: mom, mom, mom, mom and mom with its mean value
Fig. : showing standard deviation and variance
Descriptive statistics may not apply to all variables in the dataset, depending on their level of measurement. The mean can be calculated for interval and ratio data, while minimum and maximum values can be calculated for all levels of measurement. The median is calculable for ordinal, interval, and ratio data, whereas the mode can be calculated for all levels of measurement.
Data disaggregation involves tabulating and analyzing the data by breaking it down across different variables and subcategories of variables.
Crossbars allow for the segmentation of data into different categories and subcategories within a variable, making it easier to analyze individual units within each category. Correlation, also known as bipartite analysis, is a statistical computation that evaluates the connection between two variables (such as strength – strong or weak, nature – positive or negative) and its statistical significance.
When utilizing correlations, it is crucial to bear in mind that they do not provide an explanation for causation. Correlations solely indicate a relationship or pattern without suggesting the presence of causality between variables. A positive correlation arises when values increase in tandem, whereas a negative correlation occurs when one value decreases as the other increases. An analysis of variance (NOVA) assesses whether the disparity in means between two groups is statistically significant.
Regression is an expansion of correlation that determines whether one variable can predict another. It is a part of multivariate analysis, which examines relationships among multiple variables. Regression can evaluate the strength of the relationship between an intervention and outcome variables. It can also determine if a variable, such as program participation, significantly predicts an outcome variable like GAP or SAT scores. Variables can have positive or negative influences with varying degrees of strength. To conduct these analyses, computer software such as SPAS, ASS, STATS, and MAINTAIN are required along with a solid understanding of statistics. Quantitative Data Analysis finds wide application in fields like economics, sociology, psychology, market research, health development, and various branches of science.
Quantitative data, comprised of numbers and statistics, is generally considered more dependable compared to qualitative data. It proves highly valuable in identifying behavior patterns or dominant themes, thus making it more significant than qualitative research. This preference stems from the fact that qualitative research can often be ambiguous and reliant on collection methods employed. By utilizing quantitative data, researchers are able to comprehend the accessible information, summarize and prepare the data for distribution, identify trends, and enhance result accuracy by minimizing personal biases. Moreover, quantitative data can be extrapolated to accommodate larger sample sizes and processed into user-friendly displays like graphs and charts. Consequently, this empowers researchers to gather percentages and statistics while analyzing their findings with greater effectiveness.
