Qualitative inquiry in psychotherapy: Research on the common factors.

1999 ◽  
pp. 57-88 ◽  
Author(s):  
Paul V. Maione ◽  
Ronald J. Chenail
Keyword(s):  
Common Factors ◽  
Psychotherapy Research ◽  
Qualitative Inquiry ◽  
The Common

Patient Information on Evidence and Clinical Effectiveness of Psychotherapy

2020 ◽  
Author(s):  
Charlotte Blease ◽  
John M. Kelley ◽  
Manuel Trachsel
Keyword(s):  
Research Evidence ◽  
Treatment Options ◽  
Common Factors ◽  
Psychotherapy Research ◽  
Clinical Effectiveness ◽  
Evidence Base ◽  
Relative Importance ◽  
Considerable Controversy ◽  
Specific Factors ◽  
The Common

This chapter focuses on what information should be provided to patients about the evidence base supporting the clinical effectiveness of psychotherapy. In particular, the authors consider whether research on the relative efficacy of different forms of psychotherapy should be provided to patients, as well as whether patients should be provided with information on the relative importance of common factors versus specific factors as the causal agents of clinical improvement. After a critical review and discussion of the relatively few scholarly papers that have previously addressed this question, the authors conclude that patients should be provided with an honest, transparent, and impartial summary of the evidence related to their treatment options including information about the common factors. The authors offer this conclusion even while acknowledging that considerable controversy persists about how to interpret the psychotherapy research evidence base. Finally, the authors strongly support continued research into these questions, especially given the relatively limited scholarly attention they have received to date.

scholarly journals Integrating psychotherapy with the Hierarchical Taxonomy of Psychopathology (HiTOP)

2018 ◽  
Author(s):  
Christopher James Hopwood ◽  
Michael Bagby ◽  
Tara M. Gralnick ◽  
Eunyoe Ro ◽  
Camilo Ruggero ◽  
...  
Keyword(s):  
Common Factors ◽  
Psychotherapy Research ◽  
Cognitive Behavioral ◽  
Diagnostic Classification ◽  
Evidence Based ◽  
Integrative Psychotherapy ◽  
Categorical Approach ◽  
Research And Practice ◽  
Relational Psychotherapy ◽  
The Common

In this paper we present the Hierarchical Taxonomy of Psychopathology (HiTOP), an evidence-based alternative to the categorical approach to diagnostic classification with considerable promise for integrative psychotherapy research and practice. We first review issues associated with the categorical approach that may have constrained advances in psychotherapy. We next describe how the HiTOP model addresses some of these issues. We then offer suggestions regarding potentially mutual benefits of integrating HiTOP with treatment principles from the common factors literature as well as the cognitive-behavioral and relational psychotherapy traditions. We conclude by enumerating principles for psychotherapy research and practice based on the HiTOP model, which are illustrated with a case example.

scholarly journals N-soliton solutions and the Hirota conditions in (1 + 1)-dimensions

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Wen-Xiu Ma
Keyword(s):  
Common Factors ◽  
Soliton Solutions ◽  
Higher Order ◽  
Kdv Equations ◽  
The Common ◽  
Bilinear Formulation ◽  
Generalized Kdv Equations ◽  
Hirota Bilinear

Abstract We analyze N-soliton solutions and explore the Hirota N-soliton conditions for scalar (1 + 1)-dimensional equations, within the Hirota bilinear formulation. An algorithm to verify the Hirota conditions is proposed by factoring out common factors out of the Hirota function in N wave vectors and comparing degrees of the involved polynomials containing the common factors. Applications to a class of generalized KdV equations and a class of generalized higher-order KdV equations are made, together with all proofs of the existence of N-soliton solutions to all equations in two classes.

scholarly journals A Spatial Panel Structural Vector Autoregressive Model with Interactive Effects and Its Simulation

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 883
Author(s):  
Yaqing Liu ◽  
Hongbing Ouyang ◽  
Xiaolu Wei
Keyword(s):  
Autoregressive Model ◽  
Common Factors ◽  
Interactive Effects ◽  
Vector Autoregressive Model ◽  
Finite Sample ◽  
Vector Autoregressive ◽  
Spatial Panel ◽  
The Common ◽  
Structural Vector Autoregressive Model ◽  
Structural Vector

The existing spatial panel structural vector auto-regressive model can effectively capture the time and spatial dynamic dependence of endogenous variables. However, the hypothesis that the common factors have the same effect for all spatial units is unreasonable. Therefore, incorporating time effects, spatial effects, and time-individual effects, this paper develops a more general spatial panel structural vector autoregressive model with interactive effects (ISpSVAR) that can reflect the different effects of common factors on different spatial units. Additionally, based on whether or not the common factors can be observed, this paper proposes procedures to estimate ISpSVAR separately and studies the finite sample properties of estimators by Monte Carlo simulation. The simulation results show the effectiveness of the proposed ISpSVAR model and its estimation procedures.

Determinants of total factor productivity in Visegrad Group Nuts-2 regions

2018 ◽  
Vol 68 (1) ◽  
pp. 31-50 ◽  
Author(s):  
Barbara Danska-Borsiak
Keyword(s):  
Panel Data ◽  
Total Factor Productivity ◽  
Latent Variables ◽  
Fixed Effects ◽  
Orthogonal Transformation ◽  
Common Factors ◽  
Dynamic Panel Data ◽  
Factor Productivity ◽  
The Common ◽  
Visegrad Group

This article attempts to estimate the total factor productivity (TFP) for 35 NUTS-2 regions of the Visegrad Group countries and to identify its determinants. The TFP values are estimated on the basis of the Cobb-Douglas production function, with the assumption of regional differences in productivity. The parameters of the productivity function were analysed with panel data, using a fixed effects model. There are many economic variables that influence the TFP level. Some of them are highly correlated, and therefore the factor analysis was applied to extract the common factors – the latent variables that capture the common variance among those observed variables that have similar patterns of responses. This statistical procedure uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. Each component is interpreted using the contributions of variables to the respective component. I estimated a dynamic panel data model describing TFP formation by regions. An attempt was made to incorporate the common factors among the model’s explanatory variables. One of them, representing the effects of research activity, proved to be significant.

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