Professional practice of behavior analysisThe professional practice of behavior analysis is a domain of behavior analysis, the others being radical behaviorism, experimental analysis of behavior and applied behavior analysis. The practice of behavior analysis is the delivery of interventions to consumers that are guided by the principles of radical behaviorism and the research of both experimental and applied behavior analysis. Professional practice seeks to change specific behavior through the implementation of these principles.
CorrelationIn statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve.
Behavior modificationBehavior modification is an early approach that used respondent and operant conditioning to change behavior. Based on methodological behaviorism, overt behavior was modified with consequences, including positive and negative reinforcement contingencies to increase desirable behavior, or administering positive and negative punishment and/or extinction to reduce problematic behavior. It also used Flooding desensitization to combat phobias.
Gender roleA gender role, also known as a sex role, is a social role encompassing a range of behaviors and attitudes that are generally considered acceptable, appropriate, or desirable for a person based on that person's sex. Gender roles are usually centered on conceptions of masculinity and femininity, although there are exceptions and variations. The specifics regarding these gendered expectations may vary among cultures, while other characteristics may be common throughout a range of cultures.
Pearson correlation coefficientIn statistics, the Pearson correlation coefficient (PCC) is a correlation coefficient that measures linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; thus, it is essentially a normalized measurement of the covariance, such that the result always has a value between −1 and 1. As with covariance itself, the measure can only reflect a linear correlation of variables, and ignores many other types of relationships or correlations.
Behavioral activationBehavioral activation (BA) is a third generation behavior therapy for treating depression. Behavioral activation primarily emphasizes engaging in positive and enjoyable activities to enhance one's mood. It is one form of functional analytic psychotherapy, which is based on a Skinnerian psychological model of behavior change, generally referred to as applied behavior analysis. This area is also a part of what is called clinical behavior analysis (CBA) and makes up one of the most effective practices in the professional practice of behavior analysis.
Cognitive behavioral therapyCognitive behavioral therapy (CBT) is a psycho-social intervention that aims to reduce symptoms of various mental health conditions, primarily depression and anxiety disorders. Cognitive behavioral therapy is one of the most effective means of treatment for substance abuse and co-occurring mental health disorders. CBT focuses on challenging and changing cognitive distortions (such as thoughts, beliefs, and attitudes) and their associated behaviors to improve emotional regulation and develop personal coping strategies that target solving current problems.
Logistic regressionIn statistics, the logistic model (or logit model) is a statistical model that models the probability of an event taking place by having the log-odds for the event be a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients in the linear combination).
Cross-correlationIn signal processing, cross-correlation is a measure of similarity of two series as a function of the displacement of one relative to the other. This is also known as a sliding dot product or sliding inner-product. It is commonly used for searching a long signal for a shorter, known feature. It has applications in pattern recognition, single particle analysis, electron tomography, averaging, cryptanalysis, and neurophysiology. The cross-correlation is similar in nature to the convolution of two functions.
Intraclass correlationIn statistics, the intraclass correlation, or the intraclass correlation coefficient (ICC), is a descriptive statistic that can be used when quantitative measurements are made on units that are organized into groups. It describes how strongly units in the same group resemble each other. While it is viewed as a type of correlation, unlike most other correlation measures, it operates on data structured as groups rather than data structured as paired observations.
Job characteristic theoryJob characteristics theory is a theory of work design. It provides “a set of implementing principles for enriching jobs in organizational settings”. The original version of job characteristics theory proposed a model of five “core” job characteristics (i.e. skill variety, task identity, task significance, autonomy, and feedback) that affect five work-related outcomes (i.e. motivation, satisfaction, performance, and absenteeism and turnover) through three psychological states (i.e.
Gender pay gapThe gender pay gap or gender wage gap is the average difference between the remuneration for men and women who are working. Women are generally found to be paid less than men. In the United States, for example, the average annual salary of a woman is 83% that of a man. However, this figure changes when controlled for confounding factors such as differences in hours worked, occupations chosen, education, job experience, and level of danger at work. Attempts to control for these factors arrive at adjusted figures from 95% to 99%.
Correlation coefficientA correlation coefficient is a numerical measure of some type of correlation, meaning a statistical relationship between two variables. The variables may be two columns of a given data set of observations, often called a sample, or two components of a multivariate random variable with a known distribution. Several types of correlation coefficient exist, each with their own definition and own range of usability and characteristics. They all assume values in the range from −1 to +1, where ±1 indicates the strongest possible agreement and 0 the strongest possible disagreement.
Job performanceJob performance assesses whether a person performs a job well. Job performance, studied academically as part of industrial and organizational psychology, also forms a part of human resources management. Performance is an important criterion for organizational outcomes and success. John P. Campbell describes job performance as an individual-level variable, or something a single person does. This differentiates it from more encompassing constructs such as organizational performance or national performance, which are higher-level variables.
Job satisfactionJob satisfaction, employee satisfaction or work satisfaction is a measure of workers' contentedness with their job, whether they like the job or individual aspects or facets of jobs, such as nature of work or supervision. Job satisfaction can be measured in cognitive (evaluative), affective (or emotional), and behavioral components. Researchers have also noted that job satisfaction measures vary in the extent to which they measure feelings about the job (affective job satisfaction).
Organizational behaviorOrganizational behavior or organisational behaviour (see spelling differences) is the: "study of human behavior in organizational settings, the interface between human behavior and the organization, and the organization itself". Organizational behavioral research can be categorized in at least three ways: individuals in organizations (micro-level) work groups (meso-level) how organizations behave (macro-level) Chester Barnard recognized that individuals behave differently when acting in their organizational role than when acting separately from the organization.
Scaled correlationIn statistics, scaled correlation is a form of a coefficient of correlation applicable to data that have a temporal component such as time series. It is the average short-term correlation. If the signals have multiple components (slow and fast), scaled coefficient of correlation can be computed only for the fast components of the signals, ignoring the contributions of the slow components. This filtering-like operation has the advantages of not having to make assumptions about the sinusoidal nature of the signals.
Gender varianceGender variance or gender nonconformity is behavior or gender expression by an individual that does not match masculine or feminine gender norms. A gender-nonconforming person may be variant in their gender identity, being transgender or non-binary, or they may be cisgender. In the case of transgender people, they may be perceived, or perceive themselves as, gender-nonconforming before transitioning, but might not be perceived as such after transitioning.
Gender neutralityGender neutrality (adjective form: gender-neutral), also known as gender-neutralism or the gender neutrality movement, is the idea that policies, language, and other social institutions (social structures or gender roles) should avoid distinguishing roles according to people's sex or gender. This is in order to avoid discrimination arising from the impression that there are social roles for which one gender is more suited than another.
Ordinary least squaresIn statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values of the variable being observed) in the input dataset and the output of the (linear) function of the independent variable.
