This glossary defines selected research, cyberpsychology, cybersecurity, technology-adoption, and quantitative-methods terms used across CyberPsy.us.

Disclaimer. This glossary is provided for informational and educational purposes only. It is not an exhaustive or authoritative list of all research, cybersecurity, cyberpsychology, or statistical terms, and it does not replace formal methods texts, professional guidance, institutional policies, or legal, technical, or academic consultation.

Research and Dissertation Terms

Academic Review Board

An internal university committee that reviews and approves academic projects before Institutional Review Board submission.

Adoption

The process of accepting and implementing a new system, practice, or technology. In cybersecurity contexts, adoption may refer to integrating a cyber incident response plan, tool, policy, or practice into organizational operations.

Assumptions

Conditions accepted as true for the purposes of a study. Assumptions provide a foundation for the research, such as the assumption that participants will answer survey questions honestly.

Behavioral Intention

A person’s perceived likelihood or subjective probability that they will engage in a given behavior. In technology-adoption research, behavioral intention often refers to whether a person expects or intends to use, adopt, or support a particular system, practice, or technology.

Conceptual Framework

A visual or written representation of how concepts in a study are related, based on the researcher’s understanding of the problem, literature, and theory.

Construct

An abstract concept or variable that is defined and measured within a study, such as performance expectancy, effort expectancy, or social influence.

Construct Validity

The extent to which a measurement tool accurately measures the theoretical construct it is intended to measure.

Convergent Validity

A measure of how closely related two measures of the same construct are. In structural equation modeling, convergent validity is often assessed using average variance extracted.

Correlational Research

A type of non-experimental research that examines statistical relationships between two or more variables without manipulating them.

Cross-Sectional Study

A research design that analyzes data collected from a population or sample at a specific point in time.

Delimitations

Boundaries intentionally set by the researcher to narrow the scope of the study, such as limiting the population, setting, industry, or geographic area.

Dependent Variable

The outcome variable measured in a study. The dependent variable is expected to change based on one or more independent or predictor variables.

Explanatory Research

Research conducted to explain observed patterns or relationships among variables. Explanatory research often examines why or how certain relationships occur.

Independent Variable

A variable used to explain, predict, or influence changes in a dependent variable.

Institutional Review Board

A governing body that reviews research involving human participants to ensure ethical standards and participant protections are met.

Latent Variable

A variable that is not directly observable but is inferred from multiple observed indicators. Latent variables are commonly used to represent abstract concepts such as attitudes, perceptions, beliefs, or intentions.

Limitations

Potential weaknesses or factors beyond the researcher’s control that may affect the interpretation, reliability, or generalizability of study findings.

Mediating Variable

A variable that explains the mechanism through which an independent variable affects a dependent variable.

Moderating Variable

A variable that affects the strength or direction of the relationship between an independent variable and a dependent variable.

Non-Experimental Research

Research in which variables are observed as they naturally occur, without intervention or manipulation by the researcher.

Positivist Paradigm

A philosophical approach that assumes reality can be objectively measured using observation, numerical data, and hypothesis testing.

Predictor Variable

A variable used to forecast or explain changes in another variable. In quantitative research, predictor variable is often used similarly to independent variable.

Quantitative Research

A systematic investigation that uses numerical data and statistical analysis to understand phenomena, test hypotheses, examine relationships, or make predictions.

Reliability

The consistency of a measure. A reliable instrument produces stable and consistent results under similar conditions.

Theoretical Framework

A structure of concepts and theories that guides a study by explaining why the research problem exists and how key variables are expected to relate to one another.

Cybersecurity and Cyber Incident Response Terms

Confidentiality

The principle of ensuring that information is accessible only to those authorized to access it.

Cyber Incident Response Plan

A documented and structured plan that defines how an organization prepares for, responds to, manages, communicates about, and recovers from a cyber incident.

Cybersecurity Incident

An event that potentially jeopardizes the confidentiality, integrity, or availability of an information system. Examples include unauthorized access, malicious code, denial of service, misuse of authorized access, or other activity that may compromise systems or data.

Incident Response

A structured and documented approach to responding to an incident, limiting or preventing damage, remediating the cause, and reducing the likelihood or impact of future incidents.

Incident Response Plan

A formal strategic document that outlines responsibilities, procedures, communication protocols, and recovery steps for managing cybersecurity incidents.

Integrity

The principle of safeguarding the accuracy and completeness of information and processing methods.

Post-Adoption Usage Patterns

The ways a cyber incident response plan, system, or technology is used and sustained after initial adoption. This may include frequency of use, consistency of application, compliance with procedures, ongoing improvement, and integration into routine organizational practice.

Ransomware

A type of malicious software that encrypts or restricts access to data or systems, often with the attacker demanding payment for restoration or decryption.

Security Breach

A cybersecurity incident that results in the confirmed disclosure of sensitive data to an unauthorized party.

Threat Actor

An individual, group, or organization responsible for conducting or supporting malicious cyber activity.

Technology Adoption Theories and Models

Combined Technology Acceptance Model and Theory of Planned Behavior

The Combined Technology Acceptance Model and Theory of Planned Behavior integrates elements of the Technology Acceptance Model and the Theory of Planned Behavior. The C-TAM-TPB combines perceived usefulness, perceived ease of use, attitudes, subjective norms, and perceived behavioral control to help explain technology adoption.

Diffusion of Innovations Theory

The Diffusion of Innovations Theory explains how new ideas, technologies, and practices spread through populations over time. The DOI emphasizes adopter categories and innovation characteristics such as relative advantage, compatibility, complexity, trialability, and observability.

Model of PC Utilization

The Model of PC Utilization explains personal computer usage behavior through factors such as job fit, complexity, long-term consequences, affect toward use, social factors, and facilitating conditions. The MPCU was one of the models synthesized into the UTAUT.

Motivational Model

The Motivational Model emphasizes the role of intrinsic and extrinsic motivation in technology adoption and use. The MM includes concepts such as enjoyment, perceived usefulness, and reward-related motivation.

Social Cognitive Theory

Social Cognitive Theory explains behavior as a dynamic interaction among personal factors, behavior, and environmental influences. The SCT emphasizes concepts such as self-efficacy, outcome expectations, and observational learning.

Technology Acceptance Model

The Technology Acceptance Model explains technology acceptance through perceived usefulness and perceived ease of use. The TAM is one of the most widely used models in technology-adoption research.

Theory of Planned Behavior

The Theory of Planned Behavior explains behavioral intention as a function of attitudes toward the behavior, subjective norms, and perceived behavioral control. The TPB extends the Theory of Reasoned Action by accounting for perceived control over behavior.

Theory of Reasoned Action

The Theory of Reasoned Action explains behavior as driven by behavioral intention, which is influenced by attitudes toward the behavior and subjective norms. The TRA is a foundational model in behavioral-intention research.

Unified Theory of Acceptance and Use of Technology

The Unified Theory of Acceptance and Use of Technology integrates multiple prior technology-adoption models to explain technology usage through four core constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions. The UTAUT is an important foundation for later technology-adoption research.

Unified Theory of Acceptance and Use of Technology 2

The Unified Theory of Acceptance and Use of Technology 2 extends the UTAUT by adding hedonic motivation, price value, and habit as predictors of behavioral intention and use. The UTAUT2 is often used to study individual-level behavioral intentions related to technology adoption.

UTAUT2 Constructs

Effort Expectancy

The degree of ease associated with using a technology or system. In cyber incident response contexts, effort expectancy may refer to whether cybersecurity professionals believe a cyber incident response plan is easy or difficult to use.

Facilitating Conditions

The degree to which individuals believe organizational and technical infrastructures exist to support the use of a technology. In cyber incident response contexts, facilitating conditions may include resources, training, leadership support, technical infrastructure, and clear procedures.

Habit

The extent to which people tend to perform behaviors automatically because of prior learning, repetition, or routine practice. In cyber incident response contexts, habit may refer to whether professionals routinely use or follow cyber incident response plans.

Hedonic Motivation

The enjoyment, satisfaction, or positive experience derived from using a technology. In cybersecurity contexts, hedonic motivation may be less obvious than in consumer technology but can still relate to satisfaction, confidence, or professional engagement with effective tools and procedures.

Performance Expectancy

The belief that using a system, technology, or process will help achieve job-related gains or desired outcomes. In cyber incident response contexts, performance expectancy may refer to whether professionals believe a cyber incident response plan improves organizational readiness, response effectiveness, or protection of confidentiality, integrity, and availability.

Price Value

A user’s assessment of the tradeoff between the benefits of using a technology and the associated financial, time, or effort-related costs. In organizational cybersecurity contexts, price value may involve whether the perceived benefits of a cyber incident response plan outweigh the costs of implementation and maintenance.

Social Influence

The degree to which individuals perceive that important others believe they should use a particular system, technology, or practice. In cyber incident response contexts, social influence may include expectations from supervisors, peers, executives, regulators, clients, or professional communities.

PLS-SEM and Statistical Terms

Alternative Hypothesis

A statement proposing that a relationship, difference, or effect exists between variables.

ANOVA

Analysis of variance is a statistical procedure used to test whether mean differences exist across groups.

Average Variance Extracted

Average variance extracted evaluates convergent validity by estimating how much variance in a construct’s indicators is explained by the latent variable. In many research contexts, an AVE value of .50 or higher is considered acceptable.

Bootstrapping

A resampling method used in PLS-SEM to estimate the statistical significance of path coefficients, weights, and loadings. Bootstrapping produces values such as t-values, p-values, and confidence intervals.

Boxplot

A visual representation of a distribution showing the median, quartiles, and potential outliers.

Chi-Square

A statistic used to test relationships between categorical variables or to assess model fit in covariance-based structural equation modeling. It is not typically used as a primary fit index in PLS-SEM.

Collinearity

Collinearity occurs when two predictor variables are highly correlated. In PLS-SEM, high collinearity can inflate standard errors and distort path coefficients.

Composite Reliability

A measure of internal consistency reliability used in PLS-SEM. Composite reliability is often preferred over Cronbach’s alpha for reflective measurement models because it uses item loadings rather than assuming equal item weights.

Confidence Interval

A range of values within which the true population parameter is expected to fall with a specified level of confidence, often 95%.

Correlation

A standardized measure of the relationship between two variables, ranging from -1 to +1. Correlation indicates strength and direction but does not establish causation.

Covariance

A measure of how two variables change together. Positive covariance means variables tend to move in the same direction, while negative covariance means they tend to move in opposite directions.

Cronbach’s Alpha

A classic measure of internal consistency reliability for a set of scale or test items. Higher values indicate greater reliability, with .70 commonly used as a general benchmark.

Degrees of Freedom

The number of independent pieces of information available to estimate a parameter or test statistic.

Descriptive Statistics

Statistics that summarize data, such as mean, standard deviation, minimum, maximum, frequency, and percentage.

Discriminant Validity

The extent to which constructs are distinct from one another. Common approaches include the Fornell–Larcker criterion, cross-loadings, and the heterotrait–monotrait ratio.

Effect Size

A measure of the magnitude of an observed effect, independent of sample size. Common effect sizes include Cohen’s d, f², and R².

Effect Size f²

In PLS-SEM, f² measures the impact of a predictor on an endogenous construct by evaluating the change in R² when the predictor is removed.

F-Statistic

A ratio of explained variance to unexplained variance used in ANOVA and regression models.

Heteroscedasticity

Unequal variance in residuals across levels of predicted values. Heteroscedasticity can affect the accuracy of coefficients and significance testing.

Heterotrait–Monotrait Ratio

A discriminant validity metric used to assess whether constructs are empirically distinct. HTMT values below commonly accepted thresholds suggest adequate discriminant validity.

Homoscedasticity

The assumption that residuals have equal variance across levels of predicted values.

Indicator

An observed variable or survey item used to measure a latent variable.

Inferential Statistics

Statistical methods used to make conclusions or predictions about a population based on sample data.

Linearity

The assumption that the relationship between variables is linear. Linearity is often assessed using scatterplots or residual plots.

Listwise Deletion

A missing-data procedure that excludes a participant from an analysis if they have missing data for any variable used in that analysis.

Measurement Model

The part of a structural equation model that specifies how latent variables are measured through indicators. Measurement models may be reflective or formative.

Multicollinearity

High correlations among three or more predictor variables. Severe multicollinearity can make it difficult to isolate the effect of individual predictors.

Non-Normal Data

Data that do not follow a normal distribution. Non-normality may result from skewness, kurtosis, outliers, or other distributional patterns. PLS-SEM is often used because it does not require multivariate normality.

Nonparametric Test

A statistical test that does not assume a specific population distribution. Examples include the Mann–Whitney U test and Kruskal–Wallis test.

Normality

The assumption that data follow a normal distribution. Many classical inferential tests assume normality, although PLS-SEM does not require multivariate normality.

Null Hypothesis

A statement asserting that there is no relationship, difference, or effect between variables. Hypothesis testing often begins by assuming the null hypothesis is true.

Outer Loading

The relationship between an indicator and its latent construct in a reflective measurement model. Higher outer loadings indicate stronger relationships between indicators and the construct.

Outlier

A data point that is substantially distant from other values in a distribution. Outliers may influence correlations, regression coefficients, and other statistical results.

Pairwise Deletion

A missing-data procedure that uses all available data for each calculation and excludes cases only when they are missing data relevant to that specific calculation.

Parametric Test

A statistical test that assumes certain conditions about the population distribution, such as normality. Examples include t-tests and ANOVA.

Partial Least Squares Structural Equation Modeling

Partial least squares structural equation modeling is a statistical modeling technique used to analyze complex relationships between observed and latent variables. PLS-SEM is often used in predictive, exploratory, and technology-adoption research.

Path Coefficient

A standardized coefficient representing the strength and direction of the relationship between constructs in a structural model.

p-value

The probability of observing a result as extreme as, or more extreme than, the result obtained if the null hypothesis were true. A p-value below the selected significance level is typically considered statistically significant.

Predictive Relevance

A measure of whether a model has predictive capability for a construct. In PLS-SEM, Q² values greater than zero generally suggest predictive relevance.

R²

The coefficient of determination. R² indicates how much variance in an endogenous construct is explained by its predictors.

Regression Coefficient

A value representing the strength and direction of the relationship between a predictor and an outcome variable.

Residual

The difference between an observed value and a predicted value.

Scatterplot

A visual tool used to inspect relationships between variables, linearity, and potential outliers.

Shapiro–Wilk Test

A statistical test for normality. A nonsignificant result suggests the data are not significantly different from a normal distribution.

Significance Level

The threshold for rejecting the null hypothesis, commonly set at .05.

Standard Deviation

A measure of how much individual scores vary around the mean.

Standard Error

The standard deviation of a sampling distribution. Standard error is used in confidence intervals and significance testing.

Structural Equation Modeling

A multivariate statistical technique used to analyze relationships among observed variables and latent constructs.

Structural Model

The part of a structural equation model that specifies relationships among latent variables.

t-Test

A statistical test used to compare means or determine whether an observed effect differs significantly from zero. In PLS-SEM, t-tests are often produced through bootstrapping.

t-value

A statistic used to determine whether a path coefficient, loading, or other estimate is statistically significant.

Type I Error

Incorrectly rejecting a true null hypothesis. This is also known as a false positive.

Type II Error

Failing to reject a false null hypothesis. This is also known as a false negative.

Variance

The average squared deviation from the mean. Variance is the foundation for standard deviation and many statistical procedures.

Variance Inflation Factor

A statistic used to assess collinearity. Higher VIF values indicate greater collinearity among predictors.

Greek Symbols and Notation

α — Alpha

Alpha is commonly used to represent Cronbach’s alpha, a measure of internal consistency reliability. Alpha is also used to represent the significance level in hypothesis testing.

β — Beta

Beta is commonly used to represent a standardized path coefficient or regression coefficient. In PLS-SEM, beta indicates the strength and direction of a relationship between constructs.

δ — Delta

Delta may represent measurement error in reflective indicators. In simplified notation, measurement error can be expressed as one minus the squared loading.

ε — Epsilon

Epsilon commonly represents an error term or residual, including unexplained variance in a dependent or endogenous variable.

λ — Lambda

Lambda represents an outer loading or indicator loading in PLS-SEM. It reflects the strength of the relationship between an indicator and its latent construct.

ρA — Rho_A

Rho_A is a reliability measure used in PLS-SEM. It is often considered a useful reliability coefficient because it accounts for actual outer loadings.

ρC — Composite Reliability

Rho_C represents composite reliability, a measure of internal consistency reliability in PLS-SEM.

φ — Phi

Phi may represent the correlation between latent variables, particularly in measurement or model diagnostics.

SPSS Terms

Data View

The spreadsheet-like interface in SPSS where each row represents a case and each column represents a variable.

Explore Function

An SPSS tool used to generate descriptive statistics, boxplots, and normality tests.

Frequencies

Tables showing the count and percentage of values in categorical or ordinal variables.

Histogram

A bar graph used to visually assess distribution shape, skewness, and normality.

Item-Total Statistics

An SPSS reliability-analysis table showing how each item correlates with the total scale and how Cronbach’s alpha changes if an item is removed.

Levene’s Test

A test for homogeneity of variance, often used with t-tests and ANOVA.

Measurement Levels

SPSS measurement levels include nominal, ordinal, scale, and string. Scale variables are commonly used for correlations, regression, and many inferential tests.

Missing Values

Values that are absent from a dataset. SPSS distinguishes among system-missing values, user-defined missing values, and values excluded during analysis.

Normal Q-Q Plot

A diagnostic plot comparing observed data to a theoretical normal distribution. Points close to the diagonal line suggest approximate normality.

Pearson Correlation Table

A matrix showing correlations, significance levels, and sample sizes for relationships among variables.

Regression Output

SPSS regression output typically includes coefficients, t-values, p-values, confidence intervals, and collinearity statistics.

Reliability Analysis

An SPSS procedure used to compute Cronbach’s alpha, item-total correlations, and related scale-reliability statistics.

Sig. 2-tailed

The p-value associated with a two-tailed test. Values below the selected significance level are typically considered statistically significant.

Standardized Residuals

Residuals converted into standard deviation units. They are useful for identifying outliers and evaluating model assumptions.

Variable View

The SPSS interface where variables are defined, including names, labels, types, values, missing-data codes, and measurement levels.