Antismoking campaigns can have unintended effects on smokers and non-smokers. The author analyzed survey data from 1,687 respondents to examine how antismoking messages influence behavioral intentions, attitudes, and perceptions of influence. A structural equation model with four latent factors and direct effects between them found that exposure to antismoking messages was positively associated with intention to smoke, though the effect was small. Exposure also had a positive impact on attitudes toward smoking. The model explained a higher proportion of variance in intention for smokers than non-smokers.
A Bifactor and Itam Response Theory Analysis of the Eating Disorder Inventory-3David Garner
The Eating Disorder Inventory-3 (EDI-3; Garner, 2004) is a 91-item, self-report measure scored on 12 scales (three Eating
Disorder Risk scales, nine Psychological scales) and six composites. A sample of 1206 female eating disorder patients was
divided randomly into calibration (n = 607) and cross-validation (n = 599) samples for confirmatory factor analyses. A bifactor
model best fit the data in both samples, but a model with second-order factors corresponding to the risk and psychological scales
approached the fit of the bifactor model.
A Bifactor and Itam Response Theory Analysis of the Eating Disorder Inventory-3David Garner
The Eating Disorder Inventory-3 (EDI-3; Garner, 2004) is a 91-item, self-report measure scored on 12 scales (three Eating
Disorder Risk scales, nine Psychological scales) and six composites. A sample of 1206 female eating disorder patients was
divided randomly into calibration (n = 607) and cross-validation (n = 599) samples for confirmatory factor analyses. A bifactor
model best fit the data in both samples, but a model with second-order factors corresponding to the risk and psychological scales
approached the fit of the bifactor model.
This article chronicles the evolution of PCOMS from a simple way to discuss the benefit of services with clients to its emergence as an evidenced based practice to improve outcomes.
The research supporting the psychometrics of the measures and the PCOMS intervention is
presented and the clinical process summarized. Examples of successful transportation to public behavioral health are offered and an implementation process that values consumer involvement, recovery, social justice, and the needs of the front-line clinician is discussed. With now nine RCTs and American Psychological Association endorsements to support it, it is argued that client-based outcome feedback offers a pragmatic way to transport research to practice.
Article
Sex Offender Recidivism Revisited: Review of
Recent Meta-analyses on the Effects of Sex
Offender Treatment
Bitna Kim
1
, Peter J. Benekos
2
, and Alida V. Merlo
1
Abstract
The effectiveness of sex offender treatment programs continues to generate misinformation and disagreement. Some literature
reviews conclude that treatment does not reduce recidivism while others suggest that specific types of treatment may warrant
optimism. The principal purpose of this study is to update the most recent meta-analyses of sex offender treatments and to com-
pare the findings with an earlier study that reviewed the meta-analytic studies published from 1995 to 2002. More importantly,
this study examines effect sizes across different age populations and effect sizes across various sex offender treatments. Results of
this review of meta-analyses suggest that sex offender treatments can be considered as ‘‘proven’’ or at least ‘‘promising,’’ while age
of participants and intervention type may influence the success of treatment for sex offenders. The implications of these findings
include achieving a broader understanding of intervention moderators, applying such interventions to juvenile and adult offenders,
and outlining future areas of research.
Keywords
offenders, sexual assault, recidivism, intervention
Introduction
The topic of sex offenders generally elicits fear and anxiety
from the public and contributes to punitive policies aimed at
harsh, exclusionary punishments. The perspective that commu-
nities need to be protected from sex offenders through incar-
ceration and surveillance often overshadows the prospects
that treatment can also provide public safety. In their study,
Kernsmith, Craun, and Foster (2009) found that citizen respon-
dents who reported higher levels of fear of sex offenders were
more supportive of registration requirements for sex offenders.
Levenson, Brannon, Fortney, and Baker (2007) also reported
that public perceptions of sex offenders reflect public anxiety
and support for community protection.
Although negative attitudes toward sex offenders are not
reflective of all countries, cultural differences and historical
context can account for less punitive public responses. For
example, McAlinden (2012) found that therapeutic interven-
tions for sex offenders were more prevalent in European coun-
tries than in England and Wales. She attributes this to a more
scientific and medical approach to sex offending across Europe
and less emphasis on ‘‘sexual abuse as a moral, legal, and social
problem’’ (p. 170). Nevertheless, the sex offender problem has
become more serious across Europe and policies reflect a shift
toward more punitive attitudes and sanctions (McAlinden,
2012). Not only in European countries but also in the United
States, one of the misgivings about how to respond to sex
offenders concerns the effectiveness of treatment.
In this article, the authors address the treatment issue by
updat.
Article
Sex Offender Recidivism Revisited: Review of
Recent Meta-analyses on the Effects of Sex
Offender Treatment
Bitna Kim
1
, Peter J. Benekos
2
, and Alida V. Merlo
1
Abstract
The effectiveness of sex offender treatment programs continues to generate misinformation and disagreement. Some literature
reviews conclude that treatment does not reduce recidivism while others suggest that specific types of treatment may warrant
optimism. The principal purpose of this study is to update the most recent meta-analyses of sex offender treatments and to com-
pare the findings with an earlier study that reviewed the meta-analytic studies published from 1995 to 2002. More importantly,
this study examines effect sizes across different age populations and effect sizes across various sex offender treatments. Results of
this review of meta-analyses suggest that sex offender treatments can be considered as ‘‘proven’’ or at least ‘‘promising,’’ while age
of participants and intervention type may influence the success of treatment for sex offenders. The implications of these findings
include achieving a broader understanding of intervention moderators, applying such interventions to juvenile and adult offenders,
and outlining future areas of research.
Keywords
offenders, sexual assault, recidivism, intervention
Introduction
The topic of sex offenders generally elicits fear and anxiety
from the public and contributes to punitive policies aimed at
harsh, exclusionary punishments. The perspective that commu-
nities need to be protected from sex offenders through incar-
ceration and surveillance often overshadows the prospects
that treatment can also provide public safety. In their study,
Kernsmith, Craun, and Foster (2009) found that citizen respon-
dents who reported higher levels of fear of sex offenders were
more supportive of registration requirements for sex offenders.
Levenson, Brannon, Fortney, and Baker (2007) also reported
that public perceptions of sex offenders reflect public anxiety
and support for community protection.
Although negative attitudes toward sex offenders are not
reflective of all countries, cultural differences and historical
context can account for less punitive public responses. For
example, McAlinden (2012) found that therapeutic interven-
tions for sex offenders were more prevalent in European coun-
tries than in England and Wales. She attributes this to a more
scientific and medical approach to sex offending across Europe
and less emphasis on ‘‘sexual abuse as a moral, legal, and social
problem’’ (p. 170). Nevertheless, the sex offender problem has
become more serious across Europe and policies reflect a shift
toward more punitive attitudes and sanctions (McAlinden,
2012). Not only in European countries but also in the United
States, one of the misgivings about how to respond to sex
offenders concerns the effectiveness of treatment.
In this article, the authors address the treatment issue by
updat.
Article
Sex Offender Recidivism Revisited: Review of
Recent Meta-analyses on the Effects of Sex
Offender Treatment
Bitna Kim
1
, Peter J. Benekos
2
, and Alida V. Merlo
1
Abstract
The effectiveness of sex offender treatment programs continues to generate misinformation and disagreement. Some literature
reviews conclude that treatment does not reduce recidivism while others suggest that specific types of treatment may warrant
optimism. The principal purpose of this study is to update the most recent meta-analyses of sex offender treatments and to com-
pare the findings with an earlier study that reviewed the meta-analytic studies published from 1995 to 2002. More importantly,
this study examines effect sizes across different age populations and effect sizes across various sex offender treatments. Results of
this review of meta-analyses suggest that sex offender treatments can be considered as ‘‘proven’’ or at least ‘‘promising,’’ while age
of participants and intervention type may influence the success of treatment for sex offenders. The implications of these findings
include achieving a broader understanding of intervention moderators, applying such interventions to juvenile and adult offenders,
and outlining future areas of research.
Keywords
offenders, sexual assault, recidivism, intervention
Introduction
The topic of sex offenders generally elicits fear and anxiety
from the public and contributes to punitive policies aimed at
harsh, exclusionary punishments. The perspective that commu-
nities need to be protected from sex offenders through incar-
ceration and surveillance often overshadows the prospects
that treatment can also provide public safety. In their study,
Kernsmith, Craun, and Foster (2009) found that citizen respon-
dents who reported higher levels of fear of sex offenders were
more supportive of registration requirements for sex offenders.
Levenson, Brannon, Fortney, and Baker (2007) also reported
that public perceptions of sex offenders reflect public anxiety
and support for community protection.
Although negative attitudes toward sex offenders are not
reflective of all countries, cultural differences and historical
context can account for less punitive public responses. For
example, McAlinden (2012) found that therapeutic interven-
tions for sex offenders were more prevalent in European coun-
tries than in England and Wales. She attributes this to a more
scientific and medical approach to sex offending across Europe
and less emphasis on ‘‘sexual abuse as a moral, legal, and social
problem’’ (p. 170). Nevertheless, the sex offender problem has
become more serious across Europe and policies reflect a shift
toward more punitive attitudes and sanctions (McAlinden,
2012). Not only in European countries but also in the United
States, one of the misgivings about how to respond to sex
offenders concerns the effectiveness of treatment.
In this article, the authors address the treatment issue by
updat ...
No evidence for demand characteristics or social desirability with the Session Rating Scale.
Reese, R. J., Gillaspy, J. A., Owen, J. J., Flora, K. L., Cunningham, L. E., Archie, D., & Marsden, T. (2013). The influence of demand characteristics and social desirability on clients’ ratings of the therapeutic alliance. Journal of Clinical Psychology, 69, 696-709.
A Method for Meta-Analytic Confirmatory Factor AnalysisKamden Strunk
Research presentation by Kamden Strunk on A Method for Meta-Analytic Confirmatory Factor Analysis. Originally presented at the Southwestern Psychological Association in 2013.
Running head Final Project Data Analysis1Final Project Data A.docxjeanettehully
Running head: Final Project Data Analysis 1
Final Project Data Analysis 2
Final Project Data Analysis:
Luz Rodriguez
Southern New Hampshire University
Process and calculations
In completing the research on the influence that gender (male/female) has over the length of the hospital stay. We can use several types of statistical tests in analysis a more accurate analysis of the research question. This involves a dot plot and a histogram. In responding to this question, we can place gender in one category but studying it under two separate samples, male and female and the effects of length of stay after a myocardial infarction. We can compute this by resolving quantitative data and the relationship between the two factors s dot plot and a histogram would be effective in achieving this analysis.
Research question
To what extent does gender influence length of hospital stay for MI patients?
Response and predictor variables
Response: Length of hospital stay (LOS)
-Predictor: Gender (female and male)
Type of variable for predictor variable
Predictor: gender (female or male)
Type of diagram for analysis
Dot plot
Histogram
Data analysis
As shown the data tries to compare the differences between gender (male and Female) and the length of stay in hospitals with respect to each other. It’s clear that the length of hospital stay which is represented by 0 is shorter as compared to that of the female which is represented by 1. If there is a larger differences between the two genders, then there is a meaning which would reduce the standard deviation (Gerstman, 2015).
gender
n
mean
variance
Std. dev
Std. err.
median
range
min
max
Q1
Q3
0
65
0
0
0
0
0
0
0
0
0
0
1
35
1
0
0
0
1
0
1
1
1
1
Hypothesis test results:
Difference
Sample Diff.
Std. Err.
DF
T-Stat
P-value
μ1 - μ2
6.49
0.59375453
198
10.930443
<0.0001
References
Gerstman, B. B. (2015). Basic Biostatistics Statistics for Public Health (2nd ed.). Burlington, MA: Jones & Bartlett Learning.
gender 1 0.0 10.0 20.0 30.0 40.0 50.0 60.0 30.0 4.0 0.0 0.0 0.0 0.0 1.0
gender 0 2.0 17.0 34.0 3.0 6.0 1.0 3.0 gender 1 30.0 4.0 0.0 0.0 0.0 0.0 1.0
gender 0 0.0 2.5 5.0 7.5 10.0 12.5 15.0 2.0 17.0 34.0 3.0 6.0 1.0 3.0
Course ProjectCriteriaPointsDescribes the patient that is the subject of the project including diagnoses, medications, and history OR describes the community, its strengths and problems and the mental health issue that will be the subject of the paper.4Includes any substance abuse or violence issues for the patient or community 2Discusses attempted interventions, what has been successful and what has not.4Describes own personal thoughts about the patient's or community's mental health issues. 4Describes any cognitive concerns and possible interventions.2Writes a nursing care plan including three priority nursing diagnoses with r/t and AEB factors.4Includes outcomes in Nursing Outcomes Classification language and interventions in Nursing Intervention Classificati ...
This article chronicles the evolution of PCOMS from a simple way to discuss the benefit of services with clients to its emergence as an evidenced based practice to improve outcomes.
The research supporting the psychometrics of the measures and the PCOMS intervention is
presented and the clinical process summarized. Examples of successful transportation to public behavioral health are offered and an implementation process that values consumer involvement, recovery, social justice, and the needs of the front-line clinician is discussed. With now nine RCTs and American Psychological Association endorsements to support it, it is argued that client-based outcome feedback offers a pragmatic way to transport research to practice.
Article
Sex Offender Recidivism Revisited: Review of
Recent Meta-analyses on the Effects of Sex
Offender Treatment
Bitna Kim
1
, Peter J. Benekos
2
, and Alida V. Merlo
1
Abstract
The effectiveness of sex offender treatment programs continues to generate misinformation and disagreement. Some literature
reviews conclude that treatment does not reduce recidivism while others suggest that specific types of treatment may warrant
optimism. The principal purpose of this study is to update the most recent meta-analyses of sex offender treatments and to com-
pare the findings with an earlier study that reviewed the meta-analytic studies published from 1995 to 2002. More importantly,
this study examines effect sizes across different age populations and effect sizes across various sex offender treatments. Results of
this review of meta-analyses suggest that sex offender treatments can be considered as ‘‘proven’’ or at least ‘‘promising,’’ while age
of participants and intervention type may influence the success of treatment for sex offenders. The implications of these findings
include achieving a broader understanding of intervention moderators, applying such interventions to juvenile and adult offenders,
and outlining future areas of research.
Keywords
offenders, sexual assault, recidivism, intervention
Introduction
The topic of sex offenders generally elicits fear and anxiety
from the public and contributes to punitive policies aimed at
harsh, exclusionary punishments. The perspective that commu-
nities need to be protected from sex offenders through incar-
ceration and surveillance often overshadows the prospects
that treatment can also provide public safety. In their study,
Kernsmith, Craun, and Foster (2009) found that citizen respon-
dents who reported higher levels of fear of sex offenders were
more supportive of registration requirements for sex offenders.
Levenson, Brannon, Fortney, and Baker (2007) also reported
that public perceptions of sex offenders reflect public anxiety
and support for community protection.
Although negative attitudes toward sex offenders are not
reflective of all countries, cultural differences and historical
context can account for less punitive public responses. For
example, McAlinden (2012) found that therapeutic interven-
tions for sex offenders were more prevalent in European coun-
tries than in England and Wales. She attributes this to a more
scientific and medical approach to sex offending across Europe
and less emphasis on ‘‘sexual abuse as a moral, legal, and social
problem’’ (p. 170). Nevertheless, the sex offender problem has
become more serious across Europe and policies reflect a shift
toward more punitive attitudes and sanctions (McAlinden,
2012). Not only in European countries but also in the United
States, one of the misgivings about how to respond to sex
offenders concerns the effectiveness of treatment.
In this article, the authors address the treatment issue by
updat.
Article
Sex Offender Recidivism Revisited: Review of
Recent Meta-analyses on the Effects of Sex
Offender Treatment
Bitna Kim
1
, Peter J. Benekos
2
, and Alida V. Merlo
1
Abstract
The effectiveness of sex offender treatment programs continues to generate misinformation and disagreement. Some literature
reviews conclude that treatment does not reduce recidivism while others suggest that specific types of treatment may warrant
optimism. The principal purpose of this study is to update the most recent meta-analyses of sex offender treatments and to com-
pare the findings with an earlier study that reviewed the meta-analytic studies published from 1995 to 2002. More importantly,
this study examines effect sizes across different age populations and effect sizes across various sex offender treatments. Results of
this review of meta-analyses suggest that sex offender treatments can be considered as ‘‘proven’’ or at least ‘‘promising,’’ while age
of participants and intervention type may influence the success of treatment for sex offenders. The implications of these findings
include achieving a broader understanding of intervention moderators, applying such interventions to juvenile and adult offenders,
and outlining future areas of research.
Keywords
offenders, sexual assault, recidivism, intervention
Introduction
The topic of sex offenders generally elicits fear and anxiety
from the public and contributes to punitive policies aimed at
harsh, exclusionary punishments. The perspective that commu-
nities need to be protected from sex offenders through incar-
ceration and surveillance often overshadows the prospects
that treatment can also provide public safety. In their study,
Kernsmith, Craun, and Foster (2009) found that citizen respon-
dents who reported higher levels of fear of sex offenders were
more supportive of registration requirements for sex offenders.
Levenson, Brannon, Fortney, and Baker (2007) also reported
that public perceptions of sex offenders reflect public anxiety
and support for community protection.
Although negative attitudes toward sex offenders are not
reflective of all countries, cultural differences and historical
context can account for less punitive public responses. For
example, McAlinden (2012) found that therapeutic interven-
tions for sex offenders were more prevalent in European coun-
tries than in England and Wales. She attributes this to a more
scientific and medical approach to sex offending across Europe
and less emphasis on ‘‘sexual abuse as a moral, legal, and social
problem’’ (p. 170). Nevertheless, the sex offender problem has
become more serious across Europe and policies reflect a shift
toward more punitive attitudes and sanctions (McAlinden,
2012). Not only in European countries but also in the United
States, one of the misgivings about how to respond to sex
offenders concerns the effectiveness of treatment.
In this article, the authors address the treatment issue by
updat.
Article
Sex Offender Recidivism Revisited: Review of
Recent Meta-analyses on the Effects of Sex
Offender Treatment
Bitna Kim
1
, Peter J. Benekos
2
, and Alida V. Merlo
1
Abstract
The effectiveness of sex offender treatment programs continues to generate misinformation and disagreement. Some literature
reviews conclude that treatment does not reduce recidivism while others suggest that specific types of treatment may warrant
optimism. The principal purpose of this study is to update the most recent meta-analyses of sex offender treatments and to com-
pare the findings with an earlier study that reviewed the meta-analytic studies published from 1995 to 2002. More importantly,
this study examines effect sizes across different age populations and effect sizes across various sex offender treatments. Results of
this review of meta-analyses suggest that sex offender treatments can be considered as ‘‘proven’’ or at least ‘‘promising,’’ while age
of participants and intervention type may influence the success of treatment for sex offenders. The implications of these findings
include achieving a broader understanding of intervention moderators, applying such interventions to juvenile and adult offenders,
and outlining future areas of research.
Keywords
offenders, sexual assault, recidivism, intervention
Introduction
The topic of sex offenders generally elicits fear and anxiety
from the public and contributes to punitive policies aimed at
harsh, exclusionary punishments. The perspective that commu-
nities need to be protected from sex offenders through incar-
ceration and surveillance often overshadows the prospects
that treatment can also provide public safety. In their study,
Kernsmith, Craun, and Foster (2009) found that citizen respon-
dents who reported higher levels of fear of sex offenders were
more supportive of registration requirements for sex offenders.
Levenson, Brannon, Fortney, and Baker (2007) also reported
that public perceptions of sex offenders reflect public anxiety
and support for community protection.
Although negative attitudes toward sex offenders are not
reflective of all countries, cultural differences and historical
context can account for less punitive public responses. For
example, McAlinden (2012) found that therapeutic interven-
tions for sex offenders were more prevalent in European coun-
tries than in England and Wales. She attributes this to a more
scientific and medical approach to sex offending across Europe
and less emphasis on ‘‘sexual abuse as a moral, legal, and social
problem’’ (p. 170). Nevertheless, the sex offender problem has
become more serious across Europe and policies reflect a shift
toward more punitive attitudes and sanctions (McAlinden,
2012). Not only in European countries but also in the United
States, one of the misgivings about how to respond to sex
offenders concerns the effectiveness of treatment.
In this article, the authors address the treatment issue by
updat ...
No evidence for demand characteristics or social desirability with the Session Rating Scale.
Reese, R. J., Gillaspy, J. A., Owen, J. J., Flora, K. L., Cunningham, L. E., Archie, D., & Marsden, T. (2013). The influence of demand characteristics and social desirability on clients’ ratings of the therapeutic alliance. Journal of Clinical Psychology, 69, 696-709.
A Method for Meta-Analytic Confirmatory Factor AnalysisKamden Strunk
Research presentation by Kamden Strunk on A Method for Meta-Analytic Confirmatory Factor Analysis. Originally presented at the Southwestern Psychological Association in 2013.
Running head Final Project Data Analysis1Final Project Data A.docxjeanettehully
Running head: Final Project Data Analysis 1
Final Project Data Analysis 2
Final Project Data Analysis:
Luz Rodriguez
Southern New Hampshire University
Process and calculations
In completing the research on the influence that gender (male/female) has over the length of the hospital stay. We can use several types of statistical tests in analysis a more accurate analysis of the research question. This involves a dot plot and a histogram. In responding to this question, we can place gender in one category but studying it under two separate samples, male and female and the effects of length of stay after a myocardial infarction. We can compute this by resolving quantitative data and the relationship between the two factors s dot plot and a histogram would be effective in achieving this analysis.
Research question
To what extent does gender influence length of hospital stay for MI patients?
Response and predictor variables
Response: Length of hospital stay (LOS)
-Predictor: Gender (female and male)
Type of variable for predictor variable
Predictor: gender (female or male)
Type of diagram for analysis
Dot plot
Histogram
Data analysis
As shown the data tries to compare the differences between gender (male and Female) and the length of stay in hospitals with respect to each other. It’s clear that the length of hospital stay which is represented by 0 is shorter as compared to that of the female which is represented by 1. If there is a larger differences between the two genders, then there is a meaning which would reduce the standard deviation (Gerstman, 2015).
gender
n
mean
variance
Std. dev
Std. err.
median
range
min
max
Q1
Q3
0
65
0
0
0
0
0
0
0
0
0
0
1
35
1
0
0
0
1
0
1
1
1
1
Hypothesis test results:
Difference
Sample Diff.
Std. Err.
DF
T-Stat
P-value
μ1 - μ2
6.49
0.59375453
198
10.930443
<0.0001
References
Gerstman, B. B. (2015). Basic Biostatistics Statistics for Public Health (2nd ed.). Burlington, MA: Jones & Bartlett Learning.
gender 1 0.0 10.0 20.0 30.0 40.0 50.0 60.0 30.0 4.0 0.0 0.0 0.0 0.0 1.0
gender 0 2.0 17.0 34.0 3.0 6.0 1.0 3.0 gender 1 30.0 4.0 0.0 0.0 0.0 0.0 1.0
gender 0 0.0 2.5 5.0 7.5 10.0 12.5 15.0 2.0 17.0 34.0 3.0 6.0 1.0 3.0
Course ProjectCriteriaPointsDescribes the patient that is the subject of the project including diagnoses, medications, and history OR describes the community, its strengths and problems and the mental health issue that will be the subject of the paper.4Includes any substance abuse or violence issues for the patient or community 2Discusses attempted interventions, what has been successful and what has not.4Describes own personal thoughts about the patient's or community's mental health issues. 4Describes any cognitive concerns and possible interventions.2Writes a nursing care plan including three priority nursing diagnoses with r/t and AEB factors.4Includes outcomes in Nursing Outcomes Classification language and interventions in Nursing Intervention Classificati ...
Sustainable Development in Popular Newspapers: How is coverage in De Telegraaf influenced by other newspapers’ attention to sustainable development?
ARIMA modelling with (G)ARCH and Fractional Integration
Asymmetric media responses in the Dutch context: Does newspapers coverage respond to economic information?
Autoregressive Distributed Lags and Error Correction Models
Sustainable development in three newspapers: How does coverage in a particular newspaper influence other newspapers’ attention to sustainable development?
Vector autoregression
Why do journalists from the United States and Europe report in a different way about Climate change?
Differences in focus between US and NL
Influencing factors
Ideology and culture
Journalistic role conceptions
Sources and lobbying
Contributions of professionals
Het onderwerp is de agendasetting functie van zowel media als politiek. Uitgelegd in theoretische zin, hoe dit in de praktijk momenteel is terug te zien en wat er voor de toekomst wordt verwacht.
Presentatie van Masterstudenten Communicatiewetenschap aan de UvA:
Stephanie Macinski
Esther Vlieger
Mark Boukes
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
1. Writing assignment 2:
Effects of antismoking campaigns on non-smokers and smokers
784 words in the results section
Mark Boukes
5616298
1st semester 2009/2010
Structural Equation Modelling (SEM)
Instructors: dr. F.J. Oort and S. Jak
19 December 2009
Communication Science (Research MSc)
Faculty of Social and Behavioural Sciences
University of Amsterdam
2.
3. a) Introduction
A research of the influence of antismoking messages was carried out by Paek and Gunther
(2007). They used a structural regression model to do this with three latent factors and four
observed single indicator factors. Their model was partially latent (Kline, 2005), however a
fully latent model would seem to make it clearer to understand what is happening. Therefore
their data was used to construct a fully latent structural regression model to answer the
following research question: What influence do antismoking messages have on of non-
smokers’ and smokers’ behavioural intention to smoke, their attitude with regard to smoking
and the perceived influence on others of these messages?
b) Method:
Data and participants, analysis and procedure
Paek and Gunther (2007) gathered data in two groups on 21 variables related to: how many
antismoking messages a person did see; how they thought others did see these messages and
what the influence was on others; what their attitude was with regard to smoking; how they
thought they would behave in certain future situations; and finally the demographic variables
gender, race and grade. With a survey among 1687 respondents these answers were collected.
They were split in people who did never smoke and people who were steady smokers or at
least tried smoking already. For both groups a correlation matrix (Paek & Gunther, 2007, pp.
427-428) was presented, and a table showed the standard deviations of the observed variables
of both groups, except for the demographic variables (p. 417). It should be noticed that the
table with standard deviations referred to a somewhat larger number of respondents (N), than
the correlation matrices did. However the number of respondents remained rather large (N = 1293),
which makes it likely that these values would still be representative for both groups. The
correlation matrices taken from Paek and Gunther (2007) can be found in Appendix 1.
Structural Equation Modelling (SEM) was used to do the analysis of this research. A
structural regression model was drafted based on theory. This model was firstly analysed as a
1
4. measurement model with Confirmatory Factor Analysis. When it did not fit acceptable, the
model was improved by reviewing correlation residuals. Thereupon factor loadings in both
groups were constrained to be equal to test for measurement invariance. In this way it could
be investigated if the indicators in both groups measured the same constructs (Kline, 2005).
Finally the measurement model was altered into the theorized structural regression model.
The statistical analysis of this research is done with the computer program Mx 1.7.03,
which uses maximum likelihood estimation (Neale, 2003). This could be used, because
multivariate normality was assumed to occur (McDonald & Ho, 2002). Parameter values are
estimated with it and also the χ2 fit index, with the degrees of freedom (df) and the probability
of it (p). CFI was also calculated with Mx. The null model necessary for this consisted of the
same factors measured by the same indicators that relate in the same way to each other, but
factors did not relate to each other neither by covariances nor by direct effects.
CFI values greater than 0.90 were interpreted as indicating good fit (Kline, 2005). A
model was interpreted as well fitting when the probability of χ2 was below 0.05. However
because there were many variables used and sample size was so high, this index can very
hardly be not significant different, and model fit will always look bad. Therefore another
computer program, NIESEM, was used to calculate point and interval estimates of the
parsimony-adjusted index RMSEA (Dudgeon, 2003). NIESEM was also used to calculate
RDR and ECVI-difference to evaluate the difference between two models. The parsimony-
adjusted index RMSEA was interpreted in accordance with the criteria of McDonald & Ho
(2002). A value of 0.05 and below corresponds to ‘good’ fit, while values up to 0.08
correspond to ‘acceptable’ fit. RDR values lower than 0.05 will be interpreted as indicating
‘essentially equivalent fit’, while values above 0.08 will be interpreted as indicating
‘unequivalent fit’. Besides, it is assumed that when the confidence interval of ECVI-
difference does not contain zero, models are not equivalent and vice versa. When zero is
2
5. included, ECVI difference is not significant, and therefore it is assumed that models do not
differ significantly.
c) Results
The theorized model had four latent factors, with four or five indicators. Between the latent
factors were five direct effects, which resulted in one exogenous factor and three endogenous
factors that together formed the recursive structural part of the model. To see if it was
identified first a confirmatory factor analysis was done, with covariances among all the
factors, instead of the five direct effects. Factors in all models were scaled by imposing unit
loading identification (ULI) constraints. Particular factor loadings to constrain were chosen by
exploring which indicators assessed the factor equally well in both groups.
The measurement model did fit the data poorly: χ2 = 1396.943 (df = 258, p < 0.001),
RMSEA = 0.0827 (90% CI = [0.0785, 0870]), CFI = 0.5229. When the correlation residuals of
this model were reviewed, it seemed that there needed to be a covariance between the
measurement error of two indicators of ‘Perceived exposure and effects on others’: ‘the
effects on close friends’ and ‘the effects on peers’. When those were included in the measurement
2
model, the model fit improved significantly and became acceptable: χ = 777.689 (df = 256,
p < 0.001), RMSEA = 0.0562 (90% CI = [0.0517, 0607]), CFI = 0.7066.
Hereafter factor loadings were constrained to be equal in both groups to test for
measurement invariance. When these equality constraints were imposed on the factor loadings
of ‘Media exposure to the antismoking campaign’, ‘Perceived exposure and effects on others’
and ‘Attitude with regard to smoking’ model fit did not change significantly. Only when
factor loadings of ‘Behavioural intention to smoke’ were constrained to be equal in both
groups, model fit did decrease significantly. Therefore it could be concluded that in both
groups indicators measured the same constructs, except for those measuring behavioural
intention. This was logical, because for people who did never smoke, this behaviour will be
3
6. seen differently than for those who did. The model with equal factor loadings for the three
2
factors fitted the data acceptably: χ = 809.000 (df = 267, p < 0.001), RMSEA = 0.0561 (90%
CI = [0.0517, 0605]), CFI = 0.6952. Overall model fit did not change significantly:
RDR = 0.0538 (90% CI = [0.0320, 0.0760]) and ECVI Δ = 0.0056 (90% CI = [- 0.0046,
0.0224]).
Finally the measurement model with equality constraints was altered into the theorized
structural regression model with the added covariance (see Figure 1). As a consequence
2
model fit did decrease just very slightly and remained acceptable: χ = 809.106 (df = 269,
p < 0.001), RMSEA = 0.0558 (90% CI = [0.0514, 0.0602]), CFI = 0.6963.
Figure 1: Structural regression model used for the analysis of parameter estimates. Note that factor loadings of all
factors except behavioural intention were constrained to be equal and that ULI constraints were used.
4
7. The proportion of explained variance of behavioural intention in this model seemed to be
higher in the group of smokers than in the group of non-smokers (see Table 1); respectively
43.9% and 19.2% were explained. The proportion of explained variance for the factor
‘Attitude with regard to smoking’ seemed to be very low, on the other hand seemed
‘Perceived exposure of and effects on others’ to be explained rather well.
Table 1: Factor variances and covariances and the proportion of explained variance
Variances Standardized R2
Non-smokers group
Media exposure to campaign 0.7374 1.0000 0
Perceived exposure of and effects on others 0.0080 0.2551 0.7449
Attitude with regard to smoking 0.8194 0.9906 0.0094
Behavioural intention to smoke 0.1017 0.8076 0.1924
Smokers group
Media exposure to campaign 0.6770 1.0000 0
Perceived exposure of and effects on others 0.0047 0.2067 0.7933
Attitude with regard to smoking 1.3956 0.9974 0.0026
Behavioural intention to smoke 0.3782 0.5614 0.4386
The standardized estimates of direct effects in the structural part of the model are given in
Table 2. It is remarkable that it seems that media exposure to antismoking messages, has a
positive influence on the intention people have to smoke. This confirms the results of Paek
and Gunther (2007). In the both groups the total effect of media exposure to antismoking
messages on behavioural intention is significantly positive, but small (β = 0.1654, p < 0.05,
respectively β = 0.1159, p < 0.05). The interpretation of those is that when people increase
with one standard deviation on their media exposure to antismoking messages, their
behavioural intention to smoke will increase with this value times the standard deviation.
Media exposure only seems to have a moderate negative effect on this intention via the
indirect effect of ‘Perceived exposure of and effects on others’ in the group of non-smokers
(β = - 0.1712, p < 0.05).
Logically the attitude people have with regard to smoking seems to have a large effect
on the intention people have to smoke (β = 0.3955, p < 0.05, respectively β = 0.6486, p < 0.05).
5
8. However the effect of media exposure to antismoking messages on attitude seems to be little
or not significant and even positive (β = 0.0969, p < 0.05, respectively β = 0.0505, n.s.). If this
effect was positive it means that people who are more exposed to antismoking messages, will
have a more positive attitude with regard to smoking; an opposite effect as intended. Noticed
should also be that media exposure has a large positive effect on how people perceive how
often others are exposed to and effected by the campaign (β = 0.8631, p < 0.05, respectively
β = 0.8907, p < 0.05). While they are self not influenced much by the campaign, they expect
others nevertheless to be influenced.
Table 2: Direct, indirect and total effects of the structural part
Type Effect Standardized (β)
Non-smokers group
Direct effect Media exposure to campaign → Perceived exposure of and effects on others 0.8631*
Direct effect Media exposure to campaign → Attitude with regard to smoking 0.0969*
Direct effect Media exposure to campaign → Behavioural intention to smoke 0.2982*
Indirect effect Media exposure...→ Perceived … others→ Behavioural intention … -0.1712*
Indirect effect Media exposure...→ Attitude … → Behavioural intention … 0.0383*
Total effect Media exposure to campaign → Behavioural intention to smoke 0.1654*
Direct effect Perceived exposure of and effects on others → Behavioural intention to smoke -0.1983*
Direct effect Attitude with regard to smoking → Behavioural intention to smoke 0.3955*
Smokers group
Direct effect Media exposure to campaign → Perceived exposure of and effects on others 0.8907*
Direct effect Media exposure to campaign → Attitude with regard to smoking 0.0505
Direct effect Media exposure to campaign → Behavioural intention to smoke 0.2294
Indirect effect Media exposure...→ Perceived … others→ Behavioural intention … -0.1462
Indirect effect Media exposure...→ Attitude … → Behavioural intention … 0.0327
Total effect Media exposure to campaign → Behavioural intention to smoke 0.1159*
Direct effect Perceived exposure of and effects on others → Behavioural intention to smoke -0.1642
Direct effect Attitude with regard to smoking → Behavioural intention to smoke 0.6486*
* p < .05
To explore if the effects are equal or different in both groups, direct effects were constraint to
be equal between the two groups one by one. It turned out that only the effect of ‘Attitude
with regard to smoking’ on ‘Behavioural intention to smoke’ differed significantly across
6
9. groups. Constraining this effect to be equal across groups resulted in a significant decrease in
model fit.
7
10. References
Dudgeon, P. (2003). NIESEM: A computer program for calculating noncentral interval
estimates (and power analysis) for structural equation modeling. Melbourne:
University of Melbourne, Department of Psychology.
Kline, R. B (2005). Principles and practices of structural equation modeling (2nd ed.). New
York: The Guilford Press.
McDonald, R. P., & Ho, M. R. (2002). Principles and practice in reporting structural equation
analysis. Psychology Methods, 7(1), 64-82.
Neale, M. C., Boker, S. M., Xie, G., & Maes, H. H. (2003). Mx: Statistical Modeling (6th ed.).
Downloaded, 22 October, 2009, from http://www.vipbg.vcu.edu/~vipbg/software
/mxmanual.pdf
Paek, H., & Gunther, A. C. (2007). Smoking how peer proximity moderates indirect media
influence on adolescent smoking. Communication Research, 34(4), 407-432.
8
11. Appendix 1: Correlation matrices and standard deviations
Non-smokers (N=902)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
1 1.00
2 .58 1.00
3 .39 .52 1.00
4 .47 .48 .50 1.00
5 .51 .51 .42 .54 1.00
6 .58 .43 .36 .47 .52 1.00
7 .65 .55 .42 .51 .56 .70 1.00
8 .10 .00 .00 .03 .09 .14 .12 1.00
9 .12 -.01 -.05 .01 .09 .14 .12 .65 1.00
10 .08 .08 .05 .06 .07 .07 .06 -.11 -.14 1.00
11 .08 .07 .04 .04 .09 .09 .06 -.13 -.14 .75 1.00
12 .06 .06 .04 .02 .07 .06 .05 -.10 -.11 .76 .78 1.00
13 .06 .05 .03 .05 .08 .06 .06 -.12 -.14 .72 .76 .78 1.00
14 .06 .08 .03 .07 .09 .07 .07 -.16 -.18 .67 .75 .72 .83 1.00
15 .03 .09 .05 .08 .11 .08 .04 -.14 -.14 .28 .28 .26 .28 .30 1.00
16 .06 .11 .15 .09 .11 .05 .08 -.16 -.20 .25 .25 .25 .26 .28 .56 1.00
17 .05 .10 .09 .08 .11 .06 .06 -.14 -.16 .28 .32 .28 .28 .33 .63 .59 1.00
18 .07 .13 .09 .11 .12 .06 .08 -.17 -.18 .26 .28 .24 .27 .27 .55 .65 .58 1.00
SD 1.29 1.22 1.17 1.26 1.22 1.56 1.13 1.26 1.37 .93 .98 1.00 .96 1.07 .70 .48 .56 .46
Note: Variable names: 1. Antismoking messages on TV; 2. Antismoking messages on the radio; 3. Antismoking messages on the Internet; 4. Anti-smoking messages in
magazines; 5. Anti−smoking messages on TV; 6. Perceived exposure of other peers to antismoking messages; 7. Perceived exposure of close friends to antismoking messages;
8. Perceived effects of antismoking messages on other peers; 9. Perceived effects of antismoking messages on close friends; 10. How do you feel about smoking (grown-up);
11. How do you feel about smoking (good-looking); 12. How do you feel about smoking (exciting); 13. How do you feel about smoking (cool); 14. How do you feel about
smoking (has friends); 15. Behavioral intention (experiment with cigarettes in future?); 16. Behavioral intention (smoke a cigarette at anytime during the next year); 17.
Behavioral intention (Will you be smoking cigarettes 5 years from now); 18. Behavioral intention (If your best friend offered you a cigarette, would you smoke it).
9
12. Smokers (N=391)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
1 1.00
2 .56 1.00
3 .37 .45 1.00
4 .45 .45 .51 1.00
5 .34 .40 .40 .51 1.00
6 .51 .43 .36 .50 .48 1.00
7 .50 .51 .41 .53 .49 .64 1.00
8 .11 .02 .12 .08 .04 .12 .13 1.00
9 .09 .06 .09 .11 .09 .17 .16 .56 1.00
10 .04 .07 -.01 .05 .03 .04 .02 -.14 -.30 1.00
11 .02 .08 .00 .03 .03 .06 .01 -.05 -.24 .71 1.00
12 .04 .08 .00 .06 .04 .06 .04 -.13 -.33 .73 .79 1.00
13 .02 .05 .01 .01 .02 .06 .03 -.13 -.32 .72 .75 .83 1.00
14 .00 .01 -.02 -.01 .02 .03 .02 -.06 -.26 .67 .74 .75 .81 1.00
15 .06 .09 .05 .06 .01 .07 -.01 -.07 -.30 .46 .46 .46 .47 .42 1.00
16 .10 .14 .02 .11 .05 .11 .07 -.12 -.36 .48 .45 .50 .50 .47 .71 1.00
17 .10 .08 .04 .06 .01 .07 .01 -.13 -.30 .47 .43 .49 .46 .44 .67 .68 1.00
18 .03 .07 .00 .07 .02 .05 .03 -.16 -.39 .53 .46 .51 .51 .47 .65 .73 .66 1.00
SD 1.22 1.27 1.25 1.31 1.22 1.20 1.15 1.25 1.50 1.22 1.23 1.28 1.32 1.33 .97 1.03 .88 .99
Note: Variable names: 1. Antismoking messages on TV; 2. Antismoking messages on the radio; 3. Antismoking messages on the Internet; 4. Anti-smoking messages in
magazines; 5. Anti−smoking messages on TV; 6. Perceived exposure of other peers to antismoking messages; 7. Perceived exposure of close friends to antismoking messages;
8. Perceived effects of antismoking messages on other peers; 9. Perceived effects of antismoking messages on close friends; 10. How do you feel about smoking (grown-up);
11. How do you feel about smoking (good-looking); 12. How do you feel about smoking (exciting); 13. How do you feel about smoking (cool); 14. How do you feel about
smoking (has friends); 15. Behavioral intention (experiment with cigarettes in future?); 16. Behavioral intention (smoke a cigarette at anytime during the next year); 17.
Behavioral intention (Will you be smoking cigarettes 5 years from now); 18. Behavioral intention (If your best friend offered you a cigarette, would you smoke it).
10