Editing Quantitative research (section)

==Use of statistics==
[[Statistics]] is the most widely used branch of mathematics in quantitative research outside of the physical sciences, and also finds applications within the physical sciences, such as in [[statistical mechanics]]. Statistical methods are used extensively within fields such as economics, social sciences and biology. Quantitative research using statistical methods starts with the collection of data, based on the hypothesis or theory. Usually a big sample of data is collected – this would require verification, validation and recording before the analysis can take place. Software packages such as [[SPSS]] and [[R (programming language)|R]] are typically used for this purpose. Causal relationships are studied by manipulating factors thought to influence the phenomena of interest while controlling other variables relevant to the experimental outcomes. In the field of health, for example, researchers might measure and study the relationship between dietary intake and measurable physiological effects such as weight loss, controlling for other key variables such as exercise. Quantitatively based [[Survey research|opinion surveys]] are widely used in the media, with statistics such as the proportion of respondents in favor of a position commonly reported. In opinion surveys, respondents are asked a set of structured questions and their responses are tabulated. In the field of climate science, researchers compile and compare statistics such as temperature or atmospheric concentrations of carbon dioxide.

Empirical relationships and associations are also frequently studied by using some form of [[general linear model]], non-linear model, or by using [[factor analysis]]. A fundamental principle in quantitative research is that [[correlation does not imply causation]], although some such as [[Clive Granger]] suggest that a series of correlations can imply a [[Granger causality|degree of causality]]. This principle follows from the fact that it is always possible a [[spurious relationship]] exists for variables between which [[covariance]] is found in some degree. Associations may be examined between any combination of continuous and categorical variables using methods of statistics. Other data analytical approaches for studying [[Causality|causal relations]] can be performed with [[Necessary Condition Analysis]] (NCA), which outlines must-have conditions for the studied outcome variable.