132 Sentences With "dependent variables" | Random Sentence Generator

We've also used different dependent variables and different measures of gun attitudes.

When we read political reporting—perhaps when we read much of the news in general—we are looking to an independent variable for announcements about dependent variables.

Using a simulation-based analysis, we estimate that social intolerance increases the odds of an anti-democratic orientation by an average of about 10 percent across the three dependent variables.

Probit models offer an alternative to logistic regression for modeling categorical dependent variables.

In log-linear analysis there is no clear distinction between what variables are the independent or dependent variables. The variables are treated the same. However, often the theoretical background of the variables will lead the variables to be interpreted as either the independent or dependent variables.

MANOVA's power is affected by the correlations of the dependent variables and by the effect sizes associated with those variables. For example, when there are two groups and two dependent variables, MANOVA's power is lowest when the correlation equals the ratio of the smaller to the larger standardized effect size.

Such 'reductions', expressing the moments in terms of finitely many dependent variables, are described by the Gibbons- Tsarev equation.

In statistics, multivariate analysis of variance (MANOVA) is a procedure for comparing multivariate sample means. As a multivariate procedure, it is used when there are two or more dependent variables, and is often followed by significance tests involving individual dependent variables separately.Stevens, J. P. (2002). Applied multivariate statistics for the social sciences.

Typically, path models consist of independent and dependent variables depicted graphically by boxes or rectangles. Variables that are independent variables, and not dependent variables, are called 'exogenous'. Graphically, these exogenous variable boxes lie at outside edges of the model and have only single-headed arrows exiting from them. No single-headed arrows point at exogenous variables.

The methodology of the research also varies by the type of subliminal stimulus (auditory or visual) and the dependent variables they measure.

The data source for IVA is usually tabular data where the data is represented in columns and rows. The data variables can be divided into two different categories: independent and dependent variables. The independent variables represent the domain of the observed values, such as for instance time and space. The dependent variables represent the data being observed, for instance temperature, pressure or height.

Washington, DC US: American Psychological Association. This type of design is particularly useful because it can help to outline a functional relationship between the independent and dependent variables.

Anton, Howard, Irl C. Bivens, and Stephen Davis. Calculus Single Variable. John Wiley & Sons, 2012. Section 0.1 It is possible to have multiple independent variables or multiple dependent variables.

Edina, MN, US: Interaction Book Company. which contained a series of meta-analyses (currently being revised with updates)on many of the dependent variables relevant to Social Interdependence Theory.

Variables that are solely dependent variables, or are both independent and dependent variables, are termed 'endogenous'. Graphically, endogenous variables have at least one single-headed arrow pointing at them. In the model below, the two exogenous variables (Ex1 and Ex2) are modeled as being correlated as depicted by the double-headed arrow. Both of these variables have direct and indirect (through En1) effects on En2 (the two dependent or 'endogenous' variables/factors).

The governing equations of a mathematical model describe how the values of the unknown variables (i.e. the dependent variables) change when one or more of the known (i.e. independent) variables change.

Limited dependent variables, which are response variables that are categorical variables or are variables constrained to fall only in a certain range, often arise in econometrics. The response variable may be non-continuous ("limited" to lie on some subset of the real line). For binary (zero or one) variables, if analysis proceeds with least-squares linear regression, the model is called the linear probability model. Nonlinear models for binary dependent variables include the probit and logit model.

Brushing dependent variables and watching the connection to other dependent variables is called multivariate analysis. This could for example be used to find out if high temperatures are correlated with pressure by brushing high temperatures and watching a linked view of pressure distributions. Since each of the linked views usually has two or more dimensions, multivariate analysis can implicitly uncover higher-dimensional features of the data which would not be readily apparent from e.g. a simple scatterplot.

Because a true carpet plot represents two independent variables and two dependent variables simultaneously, there is no corresponding way to show the information on a conventional contour plot or 3D surface plot.

Ni et al. 2009. pp. 383, 387, 390. This study "explored the effects of spacing after the period on on- screen reading tasks through two dependent variables, reading time and reading comprehension".

The multivariate aspect of the MANCOVA allows the characterisation of differences in group means in regards to a linear combination of multiple dependent variables, while simultaneously controlling for covariates. Example situation where MANCOVA is appropriate: Suppose a scientist is interested in testing two new drugs for their effects on depression and anxiety scores. Also suppose that the scientist has information pertaining to the overall responsivity to drugs for each patient; accounting for this covariate will grant the test higher sensitivity in determining the effects of each drug on both dependent variables.

In a chemical process, independent variables that can be adjusted by the controller are often either the setpoints of regulatory PID controllers (pressure, flow, temperature, etc.) or the final control element (valves, dampers, etc.). Independent variables that cannot be adjusted by the controller are used as disturbances. Dependent variables in these processes are other measurements that represent either control objectives or process constraints. MPC uses the current plant measurements, the current dynamic state of the process, the MPC models, and the process variable targets and limits to calculate future changes in the dependent variables.

In such cases, a truncated or censored version of the normal distribution may formally be preferable (although there would be alternatives); there would be very little change in results from the more complicated analysis. However, software is readily available for maximum-likelihood estimation of even moderately complicated models, such as regression models, for truncated data. In econometrics, truncated dependent variables are variables for which observations cannot be made for certain values in some range. Regression models with such dependent variables require special care that properly recognizes the truncated nature of the variable.

Simultaneous equations models are a type of statistical model in which the dependent variables are functions of other dependent variables, rather than just independent variables. This means some of the explanatory variables are jointly determined with the dependent variable, which in economics usually is the consequence of some underlying equilibrium mechanism. For instance, in the simple model of supply and demand, price and quantity are jointly determined. Simultaneity poses challenges for the estimation of the statistical parameters of interest, because the Gauss–Markov assumption of strict exogeneity of the regressors is violated.

The following outline is provided as an overview of and topical guide to regression analysis: Regression analysis - use of statistical techniques for learning about the relationship between one or more dependent variables (Y) and one or more independent variables (X).

These changes are calculated to hold the dependent variables close to target while honoring constraints on both independent and dependent variables. The MPC typically sends out only the first change in each independent variable to be implemented, and repeats the calculation when the next change is required. While many real processes are not linear, they can often be considered to be approximately linear over a small operating range. Linear MPC approaches are used in the majority of applications with the feedback mechanism of the MPC compensating for prediction errors due to structural mismatch between the model and the process.

Vector field reconstruction is a method of creating a vector field from experimental or computer generated data, usually with the goal of finding a differential equation model of the system. A differential equation model is one that describes the value of dependent variables as they evolve in time or space by giving equations involving those variables and their derivatives with respect to some independent variables, usually time and/or space. An ordinary differential equation is one in which the system's dependent variables are functions of only one independent variable. Many physical, chemical, biological and electrical systems are well described by ordinary differential equations.

Factor analysis is an interdependence technique. The complete set of interdependent relationships is examined. There is no specification of dependent variables, independent variables, or causality. Factor analysis assumes that all the rating data on different attributes can be reduced down to a few important dimensions.

Spatial regression methods capture spatial dependency in regression analysis, avoiding statistical problems such as unstable parameters and unreliable significance tests, as well as providing information on spatial relationships among the variables involved. Depending on the specific technique, spatial dependency can enter the regression model as relationships between the independent variables and the dependent, between the dependent variables and a spatial lag of itself, or in the error terms. Geographically weighted regression (GWR) is a local version of spatial regression that generates parameters disaggregated by the spatial units of analysis. This allows assessment of the spatial heterogeneity in the estimated relationships between the independent and dependent variables.

If the modeled variables have not been standardized, an additional rule allows the expected covariances to be calculated as long as no paths exist connecting dependent variables to other dependent variables. The simplest case obtains where all residual variances are modeled explicitly. In this case, in addition to the three rules above, calculate expected covariances by: # Compute the product of coefficients in each route between the variables of interest, tracing backwards, changing direction at a two- headed arrow, then tracing forwards. # Sum over all distinct routes, where pathways are considered distinct if they contain different coefficients, or encounter those coefficients in a different order.

McLaughlin, M., 2009. University of Southern Carolina. # Homogeneity of covariances: The intercorrelation matrix between dependent variables must be equal across all levels of the independent variable. Violation of this assumption may lead to an increase in Type I error rates as well as decreased statistical power.

Key assumption: There are unique attributes of individuals that do not vary across time. These attributes may or may not be correlated with the individual dependent variables. To test whether fixed effects, rather than random effects, is needed, the (Durbin-Wu-)Hausman test can be used.

Brushing data points from the set of dependent variables (e.g. temperature) and seeing where among the independent variables (e.g. space or time) these data points show up, is called "feature localization". With feature localization, the user can easily identify the location of features in the dataset.

The basic setup is the same as in logistic regression, the only difference being that the dependent variables are categorical rather than binary, i.e. there are K possible outcomes rather than just two. The following description is somewhat shortened; for more details, consult the logistic regression article.

In statistics, the ordered logit model (also ordered logistic regression or proportional odds model) is an ordinal regression model—that is, a regression model for ordinal dependent variables—first considered by Peter McCullagh. For example, if one question on a survey is to be answered by a choice among "poor", "fair", "good", and "excellent", and the purpose of the analysis is to see how well that response can be predicted by the responses to other questions, some of which may be quantitative, then ordered logistic regression may be used. It can be thought of as an extension of the logistic regression model that applies to dichotomous dependent variables, allowing for more than two (ordered) response categories.

In this case, a new hypothesis will arise to challenge the old, and to the extent that the new hypothesis makes more accurate predictions than the old, the new will supplant it. Researchers can also use a null hypothesis, which states no relationship or difference between the independent or dependent variables.

Certain assumptions must be met for the MANCOVA to be used appropriately: # Normality: For each group, each dependent variable must represent a normal distribution of scores. Furthermore, any linear combination of dependent variables must be normally distributed. Transformation or removal of outliers can help ensure this assumption is met. French, A. et al.

John G. Gragg (1971) Some Statistical Models for Limited Dependent Variables with Application to the Demand for Durable Goods Econometrica Vol. 39, No. 5 (Sep., 1971), pp. 829-844 , where the non-zero values of x were modelled using a normal model, and a probit model was used to model the zeros.

PPT Download; Essentially, a control variable is what is kept the same throughout the experiment, and it is not of primary concern in the experimental outcome. Any change in a control variable in an experiment would invalidate the correlation of dependent variables (DV) to the independent variable (IV), thus skewing the results.

In calculus, a parametric derivative is a derivative of a dependent variable with respect to another dependent variable that is taken when both variables depend on an independent third variable, usually thought of as "time" (that is, when the dependent variables are x and y and are given by parametric equations in t ).

After the all- powerful assumption of mass media was disproved by empirical evidence, the indirect path of the media's effect on audiences has been widely accepted. An indirect effect indicates that an independent variable (e.g., media use) affecting the dependent variables (e.g., outcomes of media use) via one or more intervening (mediating) variables.

Vargas has written that, "What B. F. Skinner began is not an 'approach', 'view', 'discipline', 'field', or 'theory'. It was, and is, a science, differing from psychology in its dependent variables, its measurement system, its procedures, and its analytic framework".Julie S. Vargas, (2004). "Contingencies over B. F. Skinner’s Discovery of Contingencies".

The Heckman correction is a statistical technique to correct bias from non- randomly selected samples or otherwise incidentally truncated dependent variables, a pervasive issue in quantitative social sciences when using observational data. Conceptually, this is achieved by explicitly modelling the individual sampling probability of each observation (the so-called selection equation) together with the conditional expectation of the dependent variable (the so-called outcome equation). The resulting likelihood function is mathematically similar to the Tobit model for censored dependent variables, a connection first drawn by James Heckman in 1976. Heckman also developed a two- step control function approach to estimate this model, which avoids the computional burden of having to estimate both equations jointly, albeit at the cost of inefficiency.

A cheater plot A four-variable carpet plot showing interpolation A cheater plot with filled contours A lattice Plot A carpet plot is any of a few different specific types of plot. The more common plot referred to as a carpet plot is one that illustrates the interaction between two or more independent variables and one or more dependent variables in a two-dimensional plot. Besides the ability to incorporate more variables, another feature that distinguishes a carpet plot from an equivalent contour plot or 3D surface plot is that a carpet plot can be used to more accurately interpolate data points. A conventional carpet plot can capture the interaction of up to three independent variables and three dependent variables and still be easily read and interpolated.

The last approach a few scholars used in conducting Spiral of Silence researches is to use changed scores as dependent variables. However, as intuitive as this approach may be, it "leads to well-documented difficulties with respect to statistical properties, such as regression to the mean or the negative correlation of the change score with the time one state".

Ocean profile, plankton data, and metadata are available in the World Ocean Database for 29 depth-dependent variables (physical and biochemical) and 11 instruments types: Ocean Station Data (OSD), Mechanical Bathythermograph (MBT), Expendable Bathythermograph (XBT), Conductivity, Temperature, Depth (CTD), Undulating Oceanographic Recorder (UOR), Profiling Float (PFL), Moored Buoy (MRB), Drifting Buoy (DRB), Gliders (GLD), Autonomous Pinniped Bathythermograph (APB).

Ongoing design space verification should be dependent upon the results of an assessment of risk involved with scale up activities. More specifically, how scaling up production affects scale- dependent variables. Design space verification is much more focused in scope than overall process validation. Design space verification specifically aims to confirm output quality within a given operating range.

WR Ashby (1960), "Design for a Brain, p229" A system has good control if and only if the dependent variables remain the same even when the independent variables or the state function have changed. In a real system this implies that the state function is a composition of two functions, such that the second is the inverse of (the possible changes of) the first: y = F(G(x)) where F = controller system's function of state G = controlled system's function of state x = inputs, or independent variables y = outputs, or dependent variables. Later, in 1970, Conant working with Ashby produced the good regulator theorem Conant 1970 which required autonomous systems to acquire an internal model of their environment to persist and achieve stability (e.g. Nyquist stability criterion) or dynamic equilibrium.

Ni et al. 2009. pp. 383, 387, 390. This study "explored the effects of spacing after the period on on-screen reading tasks through two dependent variables, reading time and reading comprehension". A 2018 study of 60 students found that those who used two-word spaces between sentences read the same text 3 percent faster with a monospaced font (Courier New).

Tolman started and continued this research project until 1932, where, after coming back from Europe on a sabbatical leave, his interest started to decrease. Tolman's theoretical model was described in his paper "The Determiners of Behavior at a Choice Point" (1938).History of Psychology 4ed, Hothersall. p. 494 The three different variables that influence behavior are: independent, intervening, and dependent variables.

Categorical variables represent a qualitative method of scoring data (i.e. represents categories or group membership). These can be included as independent variables in a regression analysis or as dependent variables in logistic regression or probit regression, but must be converted to quantitative data in order to be able to analyze the data. One does so through the use of coding systems.

One of the most common applications is in logistic regression, which is used for modeling categorical dependent variables (e.g., yes-no choices or a choice of 3 or 4 possibilities), much as standard linear regression is used for modeling continuous variables (e.g., income or population). Specifically, logistic regression models can be phrased as latent variable models with error variables following a logistic distribution.

In research on the effectiveness of psychotherapy, experimenters often compare a given treatment with placebo treatments, or compare different treatments against each other. Treatment type is the independent variable. The dependent variables are outcomes, ideally assessed in several ways by different professionals.Evelyn S. Behar & Thomas D. Borkovec, "Psychotherapy Outcome Research", in Weiner (ed.), Handbook of Psychology (2003), Volume 2: Research Methods in Psychology.

In a somewhat obsolete variant usage, the abscissa of a point may also refer to any number that describes the point's location along some path, e.g. the parameter of a parametric equation. Used in this way, the abscissa can be thought of as a coordinate-geometry analog to the independent variable in a mathematical model or experiment (with any ordinates filling a role analogous to dependent variables).

Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP. If the independent variable is referred to as an "explanatory variable" then the term "response variable" is preferred by some authors for the dependent variable. "Explained variable" is preferred by some authors over "dependent variable" when the quantities treated as "dependent variables" may not be statistically dependent.Ash Narayan Sah (2009) Data Analysis Using Microsoft Excel, New Delhi.

In data mining, cluster-weighted modeling (CWM) is an algorithm-based approach to non-linear prediction of outputs (dependent variables) from inputs (independent variables) based on density estimation using a set of models (clusters) that are each notionally appropriate in a sub-region of the input space. The overall approach works in jointly input-output space and an initial version was proposed by Neil Gershenfeld.

It is different from an ANOVA or MANOVA, which is used to predict one (ANOVA) or multiple (MANOVA) continuous dependent variables by one or more independent categorical variables. Discriminant function analysis is useful in determining whether a set of variables is effective in predicting category membership.Green, S.B. Salkind, N. J. & Akey, T. M. (2008). Using SPSS for Windows and Macintosh: Analyzing and understanding data.

Selection bias may result in a non-representative population of test subjects in spite of best efforts to obtain a representative sample. Even a double-blind study may be subject to biased selection of dependent variables, population (via inclusion and exclusion criteria), sample size, statistical methods, or inappropriate comparators, any of which can bias the outcome of a study to favor a particular conclusion.

A true experiment with random allocation of subjects to conditions allows researchers to make strong inferences about causal relationships. In an experiment, the researcher alters parameters of influence, called independent variables, and measures resulting changes of interest, called dependent variables. Prototypical experimental research is conducted in a laboratory with a carefully controlled environment. Repeated- measures experiments are those which take place through intervention on multiple occasions.

The views are usually among the common tools of information visualization, such as histograms, scatterplots or parallel coordinates, but using volume rendered views is also possible if this is appropriate for the data. Typically, one view will display the independent variables of the dataset (e.g. time or spatial location), while the others display the dependent variables (e.g. temperature, pressure or population density) in relation to each other.

The wide concept of hybridization covers a broad variety of changes and gives a rough frame for different processes. However, still questions remain and further research is needed in several areas. As most important gap “a lack of clearly specified and standardized independent and dependent variables, and a lack of explanatory hypothesis-based analyses with large aggregated data sets”Esser & Strömbäck, 2012, p.304. can be identified.

The vertical coordinate is handled in various ways. Lewis Fry Richardson's 1922 model used geometric height (z) as the vertical coordinate. Later models substituted the geometric z coordinate with a pressure coordinate system, in which the geopotential heights of constant-pressure surfaces become dependent variables, greatly simplifying the primitive equations. This correlation between coordinate systems can be made since pressure decreases with height through the Earth's atmosphere.

A 2013 study found that funds with performance fees offer better risk-adjusted returns. The introduction of a performance fee increases the funds’ ex post four-factor alpha by on average 83 basis points per quarter. The results hold also when using Sharpe ratio and the raw quarterly return as dependent variables. The use of performance fees does not increase funds’ volatility levels relative to funds without such fees.

An attribute-value system is a basic knowledge representation framework comprising a table with columns designating "attributes" (also known as "properties", "predicates", "features", "dimensions", "characteristics", "fields", "headers" or "independent variables" depending on the context) and "rows" designating "objects" (also known as "entities", "instances", "exemplars", "elements", "records" or "dependent variables"). Each table cell therefore designates the value (also known as "state") of a particular attribute of a particular object.

Ideally, controlled experiments introduce only one independent variable at a time, in order to ascertain its unique effects upon dependent variables. These conditions are approximated best in laboratory settings. In contrast, human environments and genetic backgrounds vary so widely, and depend upon so many factors, that it is difficult to control important variables for human subjects. There are pitfalls in generalizing findings from animal studies to humans through animal models.

Perkins, H. W. & Craig, D. W. (2006). "A successful social norms campaign to reduce alcohol misuse among college student-athletes". Journal of Studies on Alcohol, 67, 868-879. When critiquing this study, one should ask how many dependent variables were assessed, as this group of researchers often assesses as many as 20 or more outcome variables and finds change in 2 or 3 and calls the program successful.

Censored regression models are used for data where only the value for the dependent variable is unknown while the values of the independent variables are still available. Censored dependent variables frequently arise in econometrics. A common example is labor supply. Data are frequently available on the hours worked by employees, and a labor supply model estimates the relationship between hours worked and characteristics of employees such as age, education and family status.

Quantitative genetics focuses on genetic variance due to genetic interactions. Any two locus interactions at a particular gene frequency can be decomposed into eight independent genetic effects using a weighted regression. In this regression, the observed two locus genetic effects are treated as dependent variables and the "pure" genetic effects are used as the independent variables. Because the regression is weighted, the partitioning among the variance components will change as a function of gene frequency.

Lie groups and hence their infinitesimal generators can be naturally "extended" to act on the space of independent variables, state variables (dependent variables) and derivatives of the state variables up to any finite order. There are many other kinds of symmetries. For example, contact transformations let coefficients of the transformations infinitesimal generator depend also on first derivatives of the coordinates. Lie-Bäcklund transformations let them involve derivatives up to an arbitrary order.

Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Second, in some situations regression analysis can be used to infer causal relationships between the independent and dependent variables. Importantly, regressions by themselves only reveal relationships between a dependent variable and a collection of independent variables in a fixed dataset.

The" panel" as a new tool for measuring opinion. The Public Opinion Quarterly, 2(4), 596–612. This longitudinal sampling-method allows estimates of changes in the population, for example with regard to chronic illness to job stress to weekly food expenditures. Panel sampling can also be used to inform researchers about within-person health changes due to age or to help explain changes in continuous dependent variables such as spousal interaction.

The classical measure of dependence, the Pearson correlation coefficient, is mainly sensitive to a linear relationship between two variables. Distance correlation was introduced in 2005 by Gábor J. Székely in several lectures to address this deficiency of Pearson's correlation, namely that it can easily be zero for dependent variables. Correlation = 0 (uncorrelatedness) does not imply independence while distance correlation = 0 does imply independence. The first results on distance correlation were published in 2007 and 2009.

These techniques may be used in the identification of flood dynamics, storm characterization, and groundwater flow in karst systems. Regression analysis is used in hydrology to determine whether a relationship may exist between independent and dependent variables. Bivariate diagrams are the most commonly used statistical regression model in the physical sciences, but there are a variety of models available from simplistic to complex. In a bivariate diagram, a linear or higher-order model may be fitted to the data.

The dependent variables (e.g., speed, number of errors) allows the psychologist to measure the strength of the intervening variables. Although Tolman was firmly behaviorist in his methodology, he was not a radical behaviorist like B. F. Skinner. In his studies of learning in rats, Tolman sought to demonstrate that animals could learn facts about the world that they could subsequently use in a flexible manner, rather than simply learning automatic responses that were triggered off by environmental stimuli.

Parametric estimating uses mathematic models in order to make reliable and logical predictions between a cost objective and its resultant costs. Parametric estimating process. Image in modified form according to NASA (2015) In the mathematic model, the dependent variables (cost) are being regressed on the independent variables (cost drivers), which are physical, operational or performance characteristics associated with the project to be estimated. Pursuing decent results, the dependent cost variable needs to be regressed on multiple cost drivers.

Construct validity refers to the extent to which the independent and dependent variables in a study represent the abstract hypothetical variables of interest. In other words, it has to do with whether the manipulated and/or measured variables in a study accurately reflect the variables the researcher hoped to manipulate. Construct validity also reflects the quality of one's operational definitions. If a researcher has done a good job of converting the abstract to the observable, construct validity is high.

EDA has also been studied as a method of pain assessment in premature born infants. Oftentimes, EDA monitoring is combined with the recording of heart rate, respiratory rate, and blood pressure, because they are all autonomically dependent variables. EDA measurement is one component of modern polygraph devices, which are often used as lie detectors. The E-meter used by the Church of Scientology as part of its practice of "auditing" and "security checking", is a custom EDA measurement device.

Dependent and independent variables are variables in mathematical modeling, statistical modeling and experimental sciences. Dependent variables receive this name because, in an experiment, their values are studied under the supposition or hypothesis that they depend, by some law or rule (e.g., by a mathematical function), on the values of other variables. Independent variables, in turn, are not seen as depending on any other variable in the scope of the experiment in question; thus, even if the existing dependency is invertible (e.g.

In an experiment, any variable that the experimenter manipulates can be called an independent variable. Models and experiments test the effects that the independent variables have on the dependent variables. Sometimes, even if their influence is not of direct interest, independent variables may be included for other reasons, such as to account for their potential confounding effect. In single variable calculus, a function is typically graphed with the horizontal axis representing the independent variable and the vertical axis representing the dependent variable.

In statistics, errors-in-variables models or measurement error models are regression models that account for measurement errors in the independent variables. In contrast, standard regression models assume that those regressors have been measured exactly, or observed without error; as such, those models account only for errors in the dependent variables, or responses. Illustration of regression dilution (or attenuation bias) by a range of regression estimates in errors-in-variables models. Two regression lines (red) bound the range of linear regression possibilities.

It is possible that something similar to your scale will already exist, so including those scale(s) and possible dependent variables in your survey may increase validity of your scale. #Begin by generating at least ten items to represent each of the scales. Administer the survey; the more representative and larger your sample, the more confidence you will have in your scales. #Review the means and standard deviations for your items, dropping any items with skewed means or very low variance.

This makes them able to observe all variables. Traditional data analysis may not be able to observe some variables, but sometimes experimenters cannot directly elicit certain information from subjects either. Without directly knowing a certain independent variable, good experimental design can create measures that to a large extent reflects the unobservable independent variable and the problem is therefore avoided. Unobservable dependent variables: In traditional data studies, extracting the cause for the dependent variable to change may prove to be difficult.

Psychometrika, 55, 107–122. represent repeated measures of dependent variables as a function of time and other measures. Such longitudinal data share the features that the same subjects are observed repeatedly over time, and on the same tests (or parallel versions), and at known times. In latent growth modeling, the relative standing of an individual at each time is modeled as a function of an underlying growth process, with the best parameter values for that growth process being fitted to each individual.

With censored dependent variables there is a violation of the Gauss–Markov assumption of zero correlation between independent variables and the error term. James Heckman proposed a two-stage estimation procedure using the inverse Mills ratio to correct for the selection bias. In a first step, a regression for observing a positive outcome of the dependent variable is modeled with a probit model. The inverse Mills ratio must be generated from the estimation of a probit model, a logit cannot be used.

The assumptions of MLM that hold for clustered data also apply to repeated measures: :(1) Random components are assumed to have a normal distribution with a mean of zero :(2) The dependent variable is assumed to be normally distributed. However, binary and discrete dependent variables may be examined in MLM using specialized procedures (i.e. employ different link functions). One of the assumptions of using MLM for growth curve modeling is that all subjects show the same relationship over time (e.g.

Derivatives may be generalized to functions of several real variables. In this generalization, the derivative is reinterpreted as a linear transformation whose graph is (after an appropriate translation) the best linear approximation to the graph of the original function. The Jacobian matrix is the matrix that represents this linear transformation with respect to the basis given by the choice of independent and dependent variables. It can be calculated in terms of the partial derivatives with respect to the independent variables.

Dependent variables (variables that are monitored for any change by the experimenter) for younger subjects have included self reporting on a 7-point smiley face scale and filmed facial reactions. Paper-based indices involve one or more of a variety of methods of responding. In some experiments, subjects are required to watch video scenarios (either staged or authentic) and to make written responses which are then assessed for their levels of empathy;e.g. scenarios are sometimes also depicted in printed form.e.g.

Although ideal flow reactors are seldom found in practice, they are useful tools for modeling non-ideal flow reactors. Any flow regime can be achieved by modeling a reactor as a combination of ideal CSTRs and plug flow reactors (PFRs) either in series or in parallel. For examples, an infinite series of ideal CSTRs is hydraulically equivalent to an ideal PFR. To model systems that do not obey the assumptions of constant temperature and a single reaction, additional dependent variables must be considered.

Mediation model with two covariates In experimental studies, there is a special concern about aspects of the experimental manipulation or setting that may account for study effects, rather than the motivating theoretical factor. Any of these problems may produce spurious relationships between the independent and dependent variables as measured. Ignoring a confounding variable may bias empirical estimates of the causal effect of the independent variable. (2) Suppression: :A suppressor variable increases the predictive validity of another variable when included in a regression equation.

In study of partial differential equations, particularly fluid dynamics, a self-similar solution is a form of solution which is similar to itself if the independent and dependent variables are appropriately scaled. The self-similar solution appears whenever the problem lacks a characteristic length or time scale (for example, self-similar solution describes Blasius boundary layer of an infinite plate, but not the finite-length plate). These include, for example, the Blasius boundary layer or the Sedov-Taylor shell.Gratton, J. (1991).

Ordinal data can be considered as a quantitative variable. In logistic regression, the equation : logit[P(Y=1)] = \alpha + \beta_1 c + \beta_2 x is the model and c takes on the assigned levels of the categorical scale. In regression analysis, outcomes (dependent variables) that are ordinal variables can be predicted using a variant of ordinal regression, such as ordered logit or ordered probit. In multiple regression/correlation analysis, ordinal data can be accommodated using power polynomials and through normalization of scores and ranks.

A data point may consist of more than one independent variable. For example, when fitting a plane to a set of height measurements, the plane is a function of two independent variables, x and z, say. In the most general case there may be one or more independent variables and one or more dependent variables at each data point. To the right is a residual plot illustrating random fluctuations about r_i=0, indicating that a linear model(Y_i=\alpha+\beta x_i+U_i) is appropriate.

The models used in MPC are generally intended to represent the behavior of complex dynamical systems. The additional complexity of the MPC control algorithm is not generally needed to provide adequate control of simple systems, which are often controlled well by generic PID controllers. Common dynamic characteristics that are difficult for PID controllers include large time delays and high-order dynamics. MPC models predict the change in the dependent variables of the modeled system that will be caused by changes in the independent variables.

The multivariate probit model is a standard method of estimating a joint relationship between several binary dependent variables and some independent variables. For categorical variables with more than two values there is the multinomial logit. For ordinal variables with more than two values, there are the ordered logit and ordered probit models. Censored regression models may be used when the dependent variable is only sometimes observed, and Heckman correction type models may be used when the sample is not randomly selected from the population of interest.

Related statistics such as Yule's Y and Yule's Q normalize this to the correlation-like range . The odds ratio is generalized by the logistic model to model cases where the dependent variables are discrete and there may be one or more independent variables. The correlation ratio, entropy-based mutual information, total correlation, dual total correlation and polychoric correlation are all also capable of detecting more general dependencies, as is consideration of the copula between them, while the coefficient of determination generalizes the correlation coefficient to multiple regression.

Multiple regression (above) is generally used when the response variable is continuous and has an unbounded range. Often the response variable may not be continuous but rather discrete. While mathematically it is feasible to apply multiple regression to discrete ordered dependent variables, some of the assumptions behind the theory of multiple linear regression no longer hold, and there are other techniques such as discrete choice models which are better suited for this type of analysis. If the dependent variable is discrete, some of those superior methods are logistic regression, multinomial logit and probit models.

Sensitivity analysis has important applications in model calibration. One application of sensitivity analysis addresses the question of "What's important to model or system development?" One can seek to identify important connections between observations, model inputs, and predictions or forecasts. That is, one can seek to understand what observations (measurements of dependent variables) are most and least important to model inputs (parameters representing system characteristics or excitation), what model inputs are most and least important to predictions or forecasts, and what observations are most and least important to the predictions and forecasts.

For example, it might be logical to use ESM in order to answer research questions which involve dependent variables with a great deal of variation throughout the day. Thus, variables such as change in mood, change in stress level, or the immediate impact of particular events may be best studied using ESM methodology. However, it is not likely that utilizing ESM will yield meaningful predictions when measuring someone performing a repetitive task throughout the day, when outcomes are long-term in nature (e.g., coronary heart problems), or inherently stable variables.

A vignette in psychological and sociological experiments presents a hypothetical situation, to which research participants respond thereby revealing their perceptions, values, social norms or impressions of events. Peter Rossi and colleaguesRossi, Peter H., and Steven L. Nock, Eds. Measuring Social Judgments: The Factorial Survey Approach (Sage, 1982); Rossi, P. H., and Richard A. Berk (1985). "Varieties of normative consensus" American Sociological Review 50: 333-347 developed a framework for creating vignettes by systematically combining predictor variables in order to dissect the effects of the variables on dependent variables.

James Tobin (March 5, 1918 – March 11, 2002) was an American economist who served on the Council of Economic Advisers and consulted with the Board of Governors of the Federal Reserve System, and taught at Harvard and Yale Universities. He developed the ideas of Keynesian economics, and advocated government intervention to stabilize output and avoid recessions. His academic work included pioneering contributions to the study of investment, monetary and fiscal policy and financial markets. He also proposed an econometric model for censored dependent variables, the well-known Tobit model.

Ole Siggaard-Andersen, author of the textbook, the Acid-Base Status of the Blood, wrote, "the Stewart approach is absurd and anachronistic." This is because Stewart began by characterising [SID], ATOT and PCO₂ as independent variables, and [H+] as the dependent variable of interest. He wrote down the equations for equilibrium concentrations derived from the law of mass action, and eliminated all other "dependent" variables. This naturally yielded an equation that phrased [H+] in terms of [SID], ATOT and PCO₂, but people take it as support for the characterisation of variables as dependent and independent.

In model predictive controllers that consist only of linear models, the superposition principle of linear algebra enables the effect of changes in multiple independent variables to be added together to predict the response of the dependent variables. This simplifies the control problem to a series of direct matrix algebra calculations that are fast and robust. When linear models are not sufficiently accurate to represent the real process nonlinearities, several approaches can be used. In some cases, the process variables can be transformed before and/or after the linear MPC model to reduce the nonlinearity.

Here the count variable would be treated as a dependent variable. Statistical methods such as least squares and analysis of variance are designed to deal with continuous dependent variables. These can be adapted to deal with count data by using data transformations such as the square root transformation, but such methods have several drawbacks; they are approximate at best and estimate parameters that are often hard to interpret. The Poisson distribution can form the basis for some analyses of count data and in this case Poisson regression may be used.

Variables are also divided into independent variables (data) that influences the dependent variables (which scientists are trying to explain). For example, in a study of how different dosages of a drug are related to the severity of symptoms of a disease, a measure of the severity of the symptoms of the disease is a dependent variable and the administration of the drug in specified doses is the independent variable. Researchers will compare the different values of the dependent variable (severity of the symptoms) and attempt to draw conclusions.

Therefore, the theory has been used to explore an array of dependent variables including disordered eating, mental health, depression, motor performance, body image, idealized body type, stereotype formation, sexual perception and sexual typing. Body shame is a byproduct of the concept of an idealized body type adopted by most Western cultures that depicts a thin, model-type figure. Thus, women will engage in actions meant to change their body such as dieting, exercise, eating disorders, cosmetic surgery, etc. Effects of objectification theory are identified on both the individual and societal levels.

This involves observing, creating, and understanding a distribution of data based upon the studies subject matter. Researchers use particular variables to interpret their data distributions from their research and employ statistics as a way of creating data tables and analyzing their data. Psychology has moved from the "common sense" reputations initially posed by Thomas Reid to the methodology approach comparing independent and dependent variables through natural observation, experiments, or combinations of the two. Though results are still, with statistical methods, objectively true based upon significance variables or p- values.

Uncertainty and errors within LAMs are introduced by the global model used for the boundary conditions of the edge of the regional model, as well as within the creation of the boundary conditions for the LAMs itself. The vertical coordinate is handled in various ways. Some models, such as Richardson's 1922 model, use geometric height (z) as the vertical coordinate. Later models substituted the geometric z coordinate with a pressure coordinate system, in which the geopotential heights of constant-pressure surfaces become dependent variables, greatly simplifying the primitive equations.

LDA is also closely related to principal component analysis (PCA) and factor analysis in that they both look for linear combinations of variables which best explain the data. LDA explicitly attempts to model the difference between the classes of data. PCA, in contrast, does not take into account any difference in class, and factor analysis builds the feature combinations based on differences rather than similarities. Discriminant analysis is also different from factor analysis in that it is not an interdependence technique: a distinction between independent variables and dependent variables (also called criterion variables) must be made.

Treml and Alexeev studied the relationships between per capita legal money income and such income-dependent variables as per capita savings and purchases of various goods and services. The study indicated that the disparity between legal income and legal spending gradually grew during 1965–1989 and by the end of the period the correlation between the two almost disappeared, indicating the rapid growth of the second economy. The proliferation of the second economy was impossible without widespread corruption.M. Alexeev, "The Russian Underground Economy in Transition" (PDF), The National Council for Soviet and East European Research, Title VIII Program, November 20, 1995.

In statistics, a covariate represents a source of variation that has not been controlled in the experiment and is believed to affect the dependent variable. The aim of such techniques as ANCOVA is to remove the effects of such uncontrolled variation, in order to increase statistical power and to ensure an accurate measurement of the true relationship between independent and dependent variables. An example is provided by the analysis of trend in sea-level by Woodworth (1987). Here the dependent variable (and variable of most interest) was the annual mean sea level at a given location for which a series of yearly values were available.

The basic technique for plotting a field is to first construct a model of the independent variables, then to use voltage meter probes to measure the dependent variables. Typically this means applying known voltages at certain points, then measuring voltages and currents within the model. The two basic approaches are to either applying electrodes and a voltage at known points within a large sheet of Teledeltos (modelling an infinite field) or else to cut a shape from Teledeltos and then apply voltages to its edges (modelling a bounded field). There is a common practical association that electrical field models are usually infinite and thermal models are usually bounded.

Variable changes for differentiation and integration are taught in elementary calculus and the steps are rarely carried out in full. The very broad use of variable changes is apparent when considering differential equations, where the independent variables may be changed using the chain rule or the dependent variables are changed resulting in some differentiation to be carried out. Exotic changes, such as the mingling of dependent and independent variables in point and contact transformations, can be very complicated but allow much freedom. Very often, a general form for a change is substituted into a problem and parameters picked along the way to best simplify the problem.

To nondimensionalize a system of equations, one must do the following: #Identify all the independent and dependent variables; #Replace each of them with a quantity scaled relative to a characteristic unit of measure to be determined; #Divide through by the coefficient of the highest order polynomial or derivative term; #Choose judiciously the definition of the characteristic unit for each variable so that the coefficients of as many terms as possible become 1; #Rewrite the system of equations in terms of their new dimensionless quantities. The last three steps are usually specific to the problem where nondimensionalization is applied. However, almost all systems require the first two steps to be performed.

The problem above is a simple example because it is a single equation with only one dependent variable, and there is one boundary layer in the solution. Harder problems may contain several co-dependent variables in a system of several equations, and/or with several boundary and/or interior layers in the solution. It is often desirable to find more terms in the asymptotic expansions of both the outer and the inner solutions. The appropriate form of these expansions is not always clear: while a power-series expansion in \varepsilon may work, sometimes the appropriate form involves fractional powers of \varepsilon, functions such as \varepsilon \log \varepsilon, et cetera.

Experiments attempt to assess the effect of manipulating one or more independent variables on one or more dependent variables. To ensure the measured effect is not influenced by external factors, other variables must be held constant. The variables made to remain constant during an experiment are referred to as control variables. For example, if an outdoor experiment were to be conducted to compare how different wing designs of a paper airplane (the independent variable) affect how far it can fly (the dependent variable), one would want to ensure that the experiment is conducted at times when the weather is the same, because one would not want weather to affect the experiment.

Relational Power, Marital Schema, and Decisions to Withhold Complaints: An Investigation of the Chilling Effect on Confrontation in Marriage [PDF] Communication Studies, 55(1), 146-167 This is determined through interpersonal power, or the degree of influence one exerts over the other in a relationship through the ability to sway the costs and rewards the partner undergoes. Marital schemas are cognitive structures that contain organized knowledge about marriage relationships. This research was conducted by having communication students present a questionnaire to a married individual studying six different types of power as independent variables. The dependent variables were the conflicts that were not brought to the spouses attention.

Symbolic circuit analysis is a formal technique of circuit analysis to calculate the behaviour or characteristic of an electric/electronic circuit with the independent variables (time or frequency), the dependent variables (voltages and currents), and (some or all of) the circuit elements represented by symbols.G. Gielen and W. Sansen, Symbolic Analysis for Automated Design of Analog Integrated Circuits. Boston: Kluwer Academic Publishers, 1991.Labrèche P., presentation: Linear Electrical Circuits:Symbolic Network Analysis, 1977 When analysing electric/electronic circuits, we may ask two types of questions: What is the value of certain circuit variable (voltage, current, resistance, gain, etc.) or what is the relationship between some circuit variables or between a circuit variable and circuit components and frequency (or time).

In statistics, the Breusch–Godfrey test is used to assess the validity of some of the modelling assumptions inherent in applying regression-like models to observed data series. In particular, it tests for the presence of serial correlation that has not been included in a proposed model structure and which, if present, would mean that incorrect conclusions would be drawn from other tests or that sub-optimal estimates of model parameters would be obtained. The regression models to which the test can be applied include cases where lagged values of the dependent variables are used as independent variables in the model's representation for later observations. This type of structure is common in econometric models.

Data transformation may be used as a remedial measure to make data suitable for modeling with linear regression if the original data violates one or more assumptions of linear regression. For example, the simplest linear regression models assume a linear relationship between the expected value of Y (the response variable to be predicted) and each independent variable (when the other independent variables are held fixed). If linearity fails to hold, even approximately, it is sometimes possible to transform either the independent or dependent variables in the regression model to improve the linearity. For example, addition of quadratic functions of the original independent variables may lead to a linear relationship with expected value of Y, resulting in a polynomial regression model, a special case of linear regression.

Incidentally, this problem of hidden variables forms the foundation for the scientific method -- which is a solution to this problem of hidden variables. Only via the scientific method can one be absolutely sure that some true antecedent causes a conclusion to also be true. We say a cause causes an effect if and only if there exists a 100% perfect correlation (positive or negative) between the cause and the effect when and only when all other possible variables are controlled for (a 100% degree of confidence). In these cases these effects are called dependent variables, and causes are called independent variables (so named because the dependent variable(s) depend on the independent variable(s), and the independent variable(s) do not depend on any other variable).

It is used internally by the implementation, and does not typically need to be displayed to the user. Capturing dependencies across all possible values can be avoided by identifying subset of important values (e.g., aggregation results) across which dependencies can be tracked, and incrementally recomputing other dependent variables, hence balancing the amount of dependency information to be tracked with the amount of recomputation to be performed upon input change. Partial evaluation can be seen as a method for automating the simplest possible case of incremental computing, in which an attempt is made to divide program data into two categories: that which can vary based on the program's input, and that which cannot (and the smallest unit of change is simply "all the data that can vary").

''''' or ''''' is a Latin phrase meaning "other things equal"; English translations of the phrase include "all other things being equal" or "other things held constant" or "all else unchanged". A prediction or a statement about a causal, empirical, or logical relation between two states of affairs is ceteris paribus if it is acknowledged that the prediction, although usually accurate in expected conditions, can fail or the relation can be abolished by intervening factors. chapter 2 A ceteris paribus assumption is often key to scientific inquiry, as scientists seek to screen out factors that perturb a relation of interest. Thus epidemiologists, for example, may seek to control independent variables as factors that may influence dependent variables—the outcomes or effects of interest.

From a statistical perspective, Mendelian randomization (MR) is an application of the technique of instrumental variables with genotype acting as an instrument for the exposure of interest. The method has also been used in economic research studying the effects of obesity on earnings, and other labor market outcomes. Accuracy of MR depends on a number of assumptions: That there is no direct relationship between the instrumental variable and the dependent variables, and that there are no direct relations between the instrumental variable and any possible confounding variables. In addition to being misled by direct effects of the instrument on the disease, the analyst may also be misled by linkage disequilibrium with unmeasured directly-causal variants, genetic heterogeneity, pleiotropy (often detected as a genetic correlation), or population stratification.

True to the barnacle's physiology, the Morris–Lecar model replaces the voltage-gated sodium current of the Hodgkin–Huxley model with a voltage- dependent calcium current. There is no inactivation (no h variable) and the calcium current equilibrates instantaneously, so that again, there are only two time-dependent variables: the transmembrane voltage V and the potassium gate probability n. The bursting, entrainment and other mathematical properties of this model have been studied in detail. The simplest models of the action potential are the "flush and fill" models (also called "integrate- and-fire" models), in which the input signal is summed (the "fill" phase) until it reaches a threshold, firing a pulse and resetting the summation to zero (the "flush" phase).

Mediated moderation is a variant of both moderation and mediation. This is where there is initially overall moderation and the direct effect of the moderator variable on the outcome is mediated. The main difference between mediated moderation and moderated mediation is that for the former there is initial (overall) moderation and this effect is mediated and for the latter there is no moderation but the effect of either the treatment on the mediator (path A) is moderated or the effect of the mediator on the outcome (path B) is moderated. In order to establish mediated moderation, one must first establish moderation, meaning that the direction and/or the strength of the relationship between the independent and dependent variables (path C) differs depending on the level of a third variable (the moderator variable).

Study of saddle-node, transcritical, pitch-fork, period doubling, Hopf, secondary Hopf (Neimark) bifurcations of stable solutions allows for a theoretical discussion of the circumstances and occurrences which arise at the critical points. Parameter continuation also gives a more dependable system to analyze a dynamical system as it is more stable than more interactive, time-stepped numerical solutions. Especially in cases where the dynamical system is prone to blow-up at certain parameter values (or combination of values for multiple parameters). It is extremely insightful as to the presence of stable solutions (attracting or repelling) in the study of Nonlinear Partial Differential Equations where times stepping in the form of the Crank Nicolson algorithm is extremely time consuming as well as unstable in cases of nonlinear growth of the dependent variables in the system.

Dionne and colleagues (2002) argued that significant effects of substitutes found in prior studies may be a statistical artifact due to common-source bias, or bias occurring when independent and dependent variables are collected from the same person or group of people. In a study sampling 49 organizations, Dionne and colleagues controlled for the effect of common- source bias and found no moderating or mediating effects of substitutes on the relationship between leader behavior and group effectiveness. In a study by Podsakoff and Mackenzie (1995), the predictor variables, as well as the job attitude and role perception variables, were both taken from individual employees, while the performance measures were taken from supervisors. They found that their predictors accounted for a higher proportion of variance in job attitudes and role perceptions than in employee performance.

Each of the matrices Y−i is in fact an ni-columned submatrix of this Y. The m×m matrix Γ, which describes the relation between the dependent variables, has a complicated structure. It has ones on the diagonal, and all other elements of each column i are either the components of the vector −γi or zeros, depending on which columns of Y were included in the matrix Y−i. The T×k matrix X contains all exogenous regressors from all equations, but without repetitions (that is, matrix X should be of full rank). Thus, each Xi is a ki- columned submatrix of X. Matrix Β has size k×m, and each of its columns consists of the components of vectors βi and zeros, depending on which of the regressors from X were included or excluded from Xi. Finally, is a T×m matrix of the error terms.

The Peterson Institute for International Economics (PIIE) claims that ISDS provisions are necessary, as they boost investment: "empirical evidence has shown that treaties including these provisions have a positive effect on foreign direct investment (FDI) flows between signatory countries.". On the other hand, Hallward-Driemeier (2003) analyzed the impact of Bilateral Investment Treaties (BITs) and, after conducting several tests with different dependent variables – absolute amount of FDI, the ratio of FDI to host country’s GDP and the share of host country’s FDI in total FDI outflows of a home country – concluded that BITs do not serve to attract additional FDI. Additionally, Emma Aisbett (2007) found "no evidence for the claim that BITs signal a safe investment climate". Yackee also concluded that "the apparently positive effect of BITs on FDI largely (and in some cases entirely) falls from significance", which is consistent with the empirical findings that "potential investors seem to have little awareness or appreciation of specific BITs".

An example of the leaf plastochron index in action is Philip R. Larson and J. G. Isebrands’ article The Plastochron Index as Applied to Developmental Studies of Cottonwood. A cottonwood leaf was the organ used for this research as this plant has uniform rates of growth, meaning it falls under the requirements needed for the formula’s verifiability. Two tests were conducted; the first test showed the results of the plastochron intervals ranging between 2-2.76 days but the second test was to calculate the statistical models of the leaf plastochron indexes for all 5 size classes. The five size classes were directly related to the five dependent variables of their study; leaf length, leaf area, vessels per internode, vessels per petiole and leaf weight (dry). This study shows how the leaf plastochron index can be used within scientific research to explore and predict certain developmental events of a leaf or other plant organ’s life cycle through a non destructive method of morphological feature identification.

For example, if a consumer spends one-half of his or her income on bread alone, a fifty-percent decrease in the price of bread will increase the free money available to him or her by the same amount which he or she can spend in buying more bread or something else. The consumer's preferences, monetary income and prices play an important role in solving the consumer's optimization problem (choosing how much of various goods to consume so as to maximize their utility subject to a budget constraint). The comparative statics of consumer behavior investigates the effects of changes in the exogenous or independent variables (especially prices and money incomes of the consumers) on the chosen values of the endogenous or dependent variables (the consumer's demands for the goods). When the income of the consumer rises with the prices held constant, the optimal bundle chosen by the consumer changes as the feasible set available to them changes.

The idea of imagination is where narrative inquiry and storytelling converge within narrative methodologies. Within narrative inquiry, storytelling seeks to better understand the “why” behind human action.Nona Lyons and Vicki Kubler LaBoskey, Narrative Inquiry in Practice: Advancing the Knowledge of Teaching (New York: Teachers College Press, 2002), 163. Story collecting as a form of narrative inquiry allows the research participants to put the data into their own words and reveal the latent “why” behind their assertions. “Interpretive research” is a form of field research methodology that also searches for the subjective "why." Interpretive research, using methods such as those termed “storytelling” or “narrative inquiry,” does not attempt to predefine independent variables and dependent variables, but acknowledges context and seeks to “understand phenomena through the meanings that people assign to them.”Heinz K. Klein and Michael D. Myers, “A Set of Principles for Conducting and Evaluating Interpretive Field Studies in Information Systems,” MIS Quarterly 23, no. 1 (March 1999): 69.