Search results
Results From The WOW.Com Content Network
Psychological statistics is application of formulas, theorems, numbers and laws to psychology. Statistical methods for psychology include development and application statistical theory and methods for modeling psychological data. These methods include psychometrics, factor analysis, experimental designs, and Bayesian statistics. The article ...
This categorization of information is an important step, for example, in preparing data for computer processing with statistical software. Prior to coding, an annotation scheme is defined. It consists of codes or tags. During coding, coders manually add codes into data where required features are identified.
Qualitative psychological research findings are not arrived at by statistical or other quantitative procedures. Quantitative psychological research findings result from mathematical modeling and statistical estimation or statistical inference. The two types of research differ in the methods employed, rather than the topics they focus on.
Early probability theory and statistics was systematized in the 19th century and statistical reasoning and probability models were used by social scientists to advance the new sciences of experimental psychology and sociology, and by physical scientists in thermodynamics and statistical mechanics.
Operations research (or operational research) is an interdisciplinary branch of applied mathematics and formal science that uses methods such as mathematical modeling, statistics, and algorithms to arrive at optimal or near optimal solutions to complex problems; Management science focuses on problems in the business world.
An example of a descriptive device used in psychological research is the diary, which is used to record observations. There is a history of use of diaries within clinical psychology . [ 20 ] Examples of psychologists that used them include B.F. Skinner (1904–1990) and Virginia Axline (1911–1988).
In the examples listed above, a nuisance variable is a variable that is not the primary focus of the study but can affect the outcomes of the experiment. [3] They are considered potential sources of variability that, if not controlled or accounted for, may confound the interpretation between the independent and dependent variables.
For example, when working with time series and other types of sequential data, it is common to difference the data to improve stationarity. If data generated by a random vector X are observed as vectors X i of observations with covariance matrix Σ, a linear transformation can be used to decorrelate the data.