text:
ORIGINAL RESEARCH article
Sec. Teacher Education
Does SWPBIS Increase Teachers' Collective Efficacy? Evidence From a Quasi-experiment
- University of Liège, Liège, Belgium
Teachers' collective efficacy is predictive of students' success. School-Wide Positive Behavior Interventions and Supports implementation requires the whole team to set itself common goals regarding behavior management. The main purpose of this study was to investigate the medium-term effects of a SWPBIS intervention on teachers' collective efficacy. Nine schools and 139 teachers and staff members (n intervention = 74, n control = 65) took part in the study. The study shows that SWPBIS implementation has a positive effect on teachers' collective efficacy both for primary and secondary schools at post-test 1 (ES = +0.80) and 2 (ES = +0.71). Differences are observed at baseline and at posttests according to the educational level. The link between subscales of a school climate instrument and teachers' collective efficacy is also investigated. The "structure for learning" subscale explains the greatest variance in collective efficacy.
Collective teacher efficacy is one of the most impressive predictors of student achievement, but the remaining question is how to improve it.
According to several authors (Donohoo, 2018; Donohoo et al., 2018; Eells, 2011; Sun et al., 2017), collective teacher efficacy can counteract the negative impact of socioeconomic conditions on student learning. Hope therefore exists for principals of schools that enroll many students from minority, disadvantaged backgrounds.
We posit that a school-wide program working on school culture and common goals can improve teachers' collective effectiveness. Research by Sørlie and Torsheim (2011) has demonstrated this previously. We intend to do so in turn, but in a different context. The novelties brought by our research are the validation of a measure of teachers' collective efficacy, in a French-speaking primary and secondary education context, and to examine the effects of the implementation of SCP (the French name of SWPBIS) in a quasi-experimental and longitudinal design including two post-tests.
The aim of this paper is to measure the evolution of collective efficacy in four schools where SCP was implemented, and to contrast it with the evolution of collective efficacy in five control schools.
What is Teachers' Collective Efficacy?
Collective efficacy is defined as teachers' beliefs about the educational team's ability to educate students. These beliefs constitute a norm that influences the actions and outcomes of schools. This definition comes from Bandura's own original research on this topic in 1993. According to Bandura, teachers operate collectively rather than as isolated individuals within an interactive social system. As such, the author notes that principal leadership contributes significantly to the development and maintenance of effective schools through their ability to bring their teams to work together by understanding the value of collaboration and believing in their ability to overcome obstacles as a team on the road to student success. The belief system of the educational team therefore creates a school culture that can have either vitalizing or demoralizing effects on the way schools function as a social system: vitalizing if the team believes itself to be collectively capable of promoting the academic success of its students, otherwise demoralizing. Bandura (1993) therefore postulates a link between collective effectiveness and academic success. The other definitions found in the literature are all congruent with Bandura's definition.
For example, Goddard et al. (2000; 2001; 2004a, b) define collective teacher efficacy as the judgment of teachers in a school about the ability of the educational team to organize and execute the courses of action required to have a positive impact on students. Tschannen-Moran and Barr (2004) define collective teacher efficacy as the collective perception that teachers in each school have of themselves as making an educational difference for their students, beyond the educational impact of their families and communities. Skaalvik and Skaalvik (2007) define collective efficacy as teachers' beliefs in the ability of the educational team to implement strategies that will enable students to succeed. According to Skaalvik and Skaalvik (2007), schools with high perceived collective efficacy set ambitious goals and demonstrate persistence in their efforts to achieve those goals. These ambitious goals create normative pressure that encourages all teachers in the school to do everything they can to excel and discourages them from giving up when faced with difficult situations.
What is known about the sources and shaping of collective teacher efficacy?
According to the first research conducted on this subject by Bandura (1993), it seems that the collective efficacy perceived by teachers evolves according to the level at which they teach. Fairly low when children enter school, then increasing once children are more acclimated to school routines; when the complexity of academic demands increases in the senior years and some gaps are not filled, teachers perceive the decline of their school in terms of teaching effectiveness. This is even more true in schools attended by a disadvantaged audience. According to Bandura's (1993) research, students' unfavorable socioeconomic conditions affect their academic performance more because of the deleterious effect they have on the educational team's beliefs in its ability to motivate and educate students than because of direct links between these unfavorable conditions and academic performance. But the reverse is also true. With educational teams that strongly believe that, through their efforts, students can be motivated and learn regardless of their social background, schools attended by minority and disadvantaged student populations can achieve high scores on standardized tests in reading and mathematics.
Goddard et al. (2000) developed a model and a measure of collective teacher effectiveness. The foundations of their model are based on the concept of self-efficacy formulated by Bandura in 1993 and on the model of teacher effectiveness developed by Tschannen-Moran et al., 1998. The sources of teachers' collective efficacy are, according to the authors who draw on Bandura (1993), the same as those of self-efficacy and are equally fundamental to the development of this collective belief: mastery experience, vicarious experience, social persuasion, and affective states (Goddard et al., 2000; 2004a, b). While the four sources of information are central to the shaping of collective efficacy, the cognitive processes of analyzing and interpreting information are also crucial. Consistent with the model of teacher efficacy described by Tschannen-Moran et al. (1998), Goddard and colleagues posit that there are two key elements in the development of teachers' collective efficacy: analysis of the teaching task and assessment of teaching skills. Teachers feel effective in teaching certain subjects to certain students in specific situations. They may feel more or less effective when circumstances differ. The authors therefore hypothesize that the development of perceptions of the group's ability to educate students successfully occurs when teachers consider the level of difficulty of the teaching task in relation to their perceptions of group competence. And, although the analysis of the teaching task and perceptions of group competence could be considered separately, perceptions of collective efficacy are formed only after teachers weigh the two elements presented above in relation to each other (Goddard et al., 2000). For the authors, perceived collective efficacy influences both individual and team behaviors.
Like Bandura (1993) before them, Goddard et al. (2000) believe that there is reason to believe that although collective efficacy is a relatively stable property, once it is developed, it can grow. According to the authors, this potential growth is consistent with the natural cycle of efficiency induced by reciprocal causality. For example, if gains in collective efficiency generate benefits, reciprocal causality suggests that these benefits can, in turn, enhance collective efficiency. However, this change in collective efficacy requires substantial effort.
In their 2011 systematic review on teacher efficacy, Klassen et al. note, however, that more research is needed to further investigate the sources and shaping of collective efficacy. As the link between teachers' collective efficacy and students' success has been highlighted by research and specifically in two meta-analyses (Eells, 2011; Sun et al., 2017), some authors have examined possible ways to increase it. School leadership appears to play a role in improving teachers' collective efficacy (Goddard et al., 2000; 2001; 2004a, b; Leithwood et al., 2020, Sun and Leithwood, 2017; Tschannen-Moran and Barr, 2004).
What Has Already Been Shown About Collective Teacher Efficacy?
A first wave of research on collective teacher efficacy aims to shed light on the predictive link between collective teacher efficacy and student achievement in a school. Like Bandura in his 1993 study, Goddard et al. (2000; 2004b) postulate a link between teachers' collective efficacy and students' academic success, mainly via the behaviors that this perception of efficacy induces. To this extent, they believe that there is much to be gained by leading schools along a path that will systematically develop the collective efficacy of educational teams. The authors postulate that the consequences of a high level of collective efficacy will be the acceptance of ambitious goals, great organizational efforts and persistence that will lead to better performance. But the opposite is, therefore, also true. Low collective efficacy will lead to less effort, a propensity to give up, and lower performance.
Goddard et al. (2000; 2004b) developed a questionnaire based on their model and tested and validated it in a first study they did on a sample of 452 teachers from 47 elementary schools in the Midwestern United States. In their study, the authors hypothesized that collective teacher efficacy would be positively associated with school differences in student achievement, based on the theory that collective teacher efficacy can positively influence many teacher behaviors that would tend to improve student achievement. The results of the study show that collective teacher efficacy is a significant predictor of student achievement in both math and reading. The effect of collective teacher efficacy is larger in magnitude than any other demographic on both reading and math achievement. For math, the score on the collective efficacy measure is associated with an average gain of 8.62 points (out of 100). For reading, the average gain associated with the score on the collective efficacy measure is 8.49. Put another way, a one-unit increase in teacher collective efficacy is associated with a more than 40% standard deviation increase in student achievement. These results are consistent with Bandura's 1993 research and posit perceived teacher collective efficacy as predictive of student achievement. The authors (Goddard et al., 2004b) replicated their first study in High schools and the results suggest the same tendency: for a one standard-deviation increase in collective efficacy a gain of 0.25 standard deviation is associated in terms of number of students who pass high-stakes assessments in 12th grade. The paper published by Donohoo et al. (2018) confirms the previous results and brings to the forefront the reciprocal causality between collective teacher efficacy and student progress ratings, already pointed out by Bandura in the first place (1993).
The results detailed above are confirmed by two meta-analyses. First, that of Eells (2011) who investigated the content of 26 studies on the effects of collective teacher efficacy on student achievement, and second, that of Sun and colleagues (2017) who, in turn, examined 11 studies on the effects of collective teacher efficacy and student academic outcomes. In both meta-analyses, on average, a strong positive correlation exists between collective teacher efficacy and student academic achievement. The conclusion that can be drawn from these meta-analyses is that collective teacher efficacy is a strong predictor of student achievement.
A second wave of research investigates the links between collective efficacy and other variables either at the teacher level or at the school level. Two Finnish researchers have investigated the extent to which teacher self-efficacy and collective efficacy mediate the effect of perceived school climate on teacher job satisfaction and burnout. Malinen and Savolainen (2016) investigated the issue through large scale longitudinal follow-up. The structural equation modeling indicates that perceived school climate is a significant predictor of teacher self-efficacy (ß = 0.26) and collective efficacy (ß = 0.51). According to Malinen and Savolainen (2016), this relationship indicates that collective efficacy shares elements with the constructs listed above: school climate and self-efficacy. The results of the study also highlight that self-efficacy correlates with teachers' collective efficacy (r = +0.46), as other researchers had demonstrated before them, for example: Goddard et al. in their 2000 study and, after them, Skaalvik and Skaalvik in 2007. In this study, however, collective efficacy did not show any mediating effect on job satisfaction or burnout.
Skaalvik and Skaalvik (2007), two Norwegian researchers, investigated the links between teachers perceived collective efficacy and their self-efficacy. The authors postulate that collective efficacy is predictive of self-efficacy. To explore this question, Skaalvik and Skaalvik (2007) developed a measure of teacher self-efficacy and a measure of collective efficacy. They used both measures in a study of a sample of 244 teachers from 12 primary and middle schools. Their results indicate that self-efficacy is strongly related to collective efficacy (r = +0.64), according to the structural equation modeling. Collective efficacy is not directly related to burnout, but the authors nevertheless found a moderate indirect link between collective efficacy and burnout, with this relationship being mediated by self-efficacy (r = −0.49). The authors, consistent with previous research, indicate that self-efficacy and collective efficacy are two different constructs and should be measured as such, that the relationship between the two is strong, positive, and reciprocal or bidirectional.
It should be noted, however, that the fit between the concept of collective teacher efficacy itself and the measurement tools used in the research conducted on the subject is not always present (Klassen et al., 2011). According to the authors, only two measures of collective teacher efficacy fit the conceptual definition: Skaalvik and Skaalvik (2007), and Tschannen-Moran and Barr (2004).
School-Wide Positive Behavior Interventions and Supports
School-Wide Positive Behavior Interventions and Support is a systemic approach - the term systemic is used here in the sense of "present at all levels" and implemented by all educational stakeholders - designed to establish support for both the social culture and individual behaviors, both of which are necessary for a school to be a safe and effective learning environment for all students (Sugai and Horner, 2009).
Universal prevention tools are introduced to all students: 1) to support prosocial behaviors, 2) to maximize educational opportunities and academic success, and 3) to prevent the onset of behavior problems (Sugai and Horner, 2009).
SWPBIS organizes a dual three-tiered system of support increasing in intensity according to students' needs and operating in parallel to address both behavioral and learning issues. The system is built around a primary intervention, known as universal prevention, targeting all students in the school. Secondary interventions are designed to reduce or eliminate risk factors for certain students (±15–20% of students) by providing them with "protective" factors. Tertiary interventions are aimed at reducing the complexity, intensity, and severity of behavioral problems in students with identified risk factors (±5% of students). This latter type of intervention is most often multidisciplinary and various experts are brought in. These interventions are always highly individualized to match students' needs most closely (Sugai and Horner, 2002, 2006, 2009; Sugai and Simonsen, 2012).
SWPBIS, or SCP in its French version, is therefore a flexible, contextually, and culturally appropriate system for the school in which it is implemented, with the goal of creating a positive, safe, and effective learning environment by preventing and reducing behavioral problems through the development of values from which behavioral expectations are derived. These expectations must be clearly written in positive terms and observable by the educational team. They are then explicitly taught, displayed, and supported by a system of verbal and/or tangible feedback to build a positive school culture that reinforces students' positive behaviors. Another key element of the system is the collection and use of data to guide the implementation and regulation of interventions in terms of both behavior and learning (Sugai and Horner, 2009).
SWPBIS and Teachers' Sense of Efficacy
The topic has been little investigated. Only one longitudinal study conducted by Sørlie and Torsheim in Norway in 2011 examined the relationship between collective efficacy and the management of inappropriate student behavior. The purpose of Sørlie and Torsheim (2011) was to conduct a multilevel analysis of the relationship between teachers' collective efficacy and behavior problems at school. The authors conducted this study as part of longitudinal research to question the effectiveness of the implementation of the Norwegian version of SWPBIS known as PALS. More than 1,000 teachers and principals from 48 Norwegian elementary school of different sizes participated in the study, which was conducted in two waves: the first in the spring of the 2006–2007 school year (Time 1) and the second, 6 mo later, at the beginning of the following school year, 2007–2008 (Time 2). Of the 48 schools that participated in the study, 28 were experimental schools and benefited from the implementation of the PALS program, while 20 schools formed the control group.
The authors used the Collective Efficacy Scale (CES; Goddard, 2001). This 12-item scale assesses the extent to which an educational team believes in its collective ability to positively influence student learning. To measure the amount of problem behavior, present in schools, Sørlie and Torsheim (2011) used two measures based on teacher observations called Problem Behaviour in the School Environment Last Week and Problem Behaviour in the Classroom Last Week, developed by Grey and Sime (1989) and translated into Norwegian by Ogden (1998). The measures consist of 15 and 20 items respectively.
At the school level, collective efficacy and behavior problems showed strong associations as indicated by correlations ranging from 0.70 to 0.78 (+0.70 < r < +0.78). Schools with high collective efficacy reported lower levels of behavior problems. At the school level, the correlation between collective efficacy and observed behavior problems in the classroom exceeded 0.90 (r = +0.90) and was 0.87 (r = +0.87) for behavior problems in common areas. These differences were stable over time.
The data presented in Sørlie and Torsheim's (2011) longitudinal study confirm that collective efficacy is a significant variable at the school level and likely a stable feature of a school's culture. Sørlie and Torsheim's (2011) findings are consistent with the assumptions made by Goddard and colleagues (2000; 2004b) when they established through their empirical research that collective efficacy is a stable school contextual variable that requires substantial effort to change.
That said, intervention programs such as SWPBIS (PALS in Norway) could change this. For example, Sørlie and Torsheim (2011) found that in schools where collective efficacy increased from T1 to T2, teachers consistently reported a lower prevalence of behavior problems over time. In schools with a negative change in collective efficacy beliefs, teachers reported higher rates of behavior problems at T2 compared to T1. However, the authors state that they also found the opposite connection: the increase in the prevalence of behavior problems at the school over time is related to a decrease in perceived collective efficacy. Thus, according to the authors' empirical research, there is a strong bidirectional predictive relationship between collective efficacy and behavior problems.
Sørlie and Torsheim (2011) conclude that teachers from schools with high collective efficacy implement more consistent positive behavior support practices than teachers from schools with lower collective efficacy. Teachers in more collectively effective schools are also more likely to persist in their efforts to regulate behavior problems and to propose and reinforce a more common set of rules. The reverse is also true: if strong pressure is placed on positive student behavior and consistent responses to rule infractions, as in the implementation of SWPBIS, collective team efficacy may subsequently increase.
Teachers' collective efficacy contributes decreasing inequalities and is a strong predictor of students' achievement. Yet little is known – if anything – about how to improve it. SWPBIS, which has demonstrated its effectiveness at improving school climate and decreasing students' problem behavior, was identified as having the potential to improve teachers' collective efficacy to a large extent. However, to date, no comparative study has investigated the effectiveness of a program, or of SWPBIS in particular, in improving collective efficacy.
Research Questions and Hypotheses
First research question: Does the implementation of the French version of School-Wide Positive Behavior Interventions and Supports influence the collective efficacy perceived by the members of the educational teams?
It is hypothesized that perceived collective efficacy will increase more in schools where SCP is implemented compared to control schools.
Second research Question: Is teachers perceived collective efficacy related to aspects of school climate?
The hypothesis that perceived collective efficacy is related to school climate is formulated.
Third research Question: To what specific aspects of school climate is perceived collective efficacy related?
Previous research (e.g.: Malinen and Savolainen, 2016) highlights a link between school climate and collective efficacy. A predictive link between collective efficacy and student achievement has also been demonstrated by different authors (Bandura, 1993; Donohoo, 2018; Eells, 2014; Goddard et al., 2000; 2004; Sun et al., 2017). The hypothesis that is therefore posited is that the aspects of school climate that affect student success will be those highlighted by the results of the analyses.
Method and Measurement
As this is a quasi-experimental research design, each of the four schools in the intervention group is matched with one or two control school(s) selected according to the following criteria: socio-economic status, size, location, educational network where possible, and the options offered (e.g.: language immersion).
It should be noted that three of the four schools in which School-Wide Positive Behavior Interventions and Supports is implemented are schools attended by students from low to very low socioeconomic backgrounds.
The sample is composed of staff members from the four pilot schools in which SCP is implemented. There are three elementary schools and one middle school. In all, this represents an n = 74 at pretest for the intervention schools. The other part of the sample consists of the teachers of the five control schools in which the research team did not intervene at all, apart from the times of data collection (passing questionnaires). This represents an n = 65 at pretest. One hundred and thirty-nine staff members answered the questions in this scale three times: 101 were teachers (73%), 110 were women (78%), 68 were elementary school teachers (49%), and 86 had been teaching for more than 11 yr (62%).
Table 1 details the demographic characteristics of staff members at the participating schools.
The following table presents the characteristics of the schools participating in the study.
Perceived Collective Teacher Efficacy Scale
The Skaalvik and Skaalvik (2007) scale was selected and translated. It consists of seven items (five points Likert scale) that reflect the ease with which the school educational team carries out its behavioral and cognitive tasks.
Here are the items in their original version, then in their French version.
We translated and adapted the Suite using double translation followed by reconciliation and validation by an expert (Grisay, 2003; Harkness, 2003).
After a field test of the scale with about 20 volunteer teachers, four people were contacted again to conduct a cognitive laboratory to ensure the quality of the translation. The research team wanted to verify that the understanding of the concept in French had the same meaning as in the original version of the measure.
Internal consistency analysis of the scale conducted on SAS 9.4. indicated a Cronbach's alpha of 0.86 on pretest data. Exploratory factor analysis was conducted on MPlus (Múthen and Múthen, 1998-2011) to compute McDonald's hierarchical omega (Béland et al., 2017; Deng and Chan, 2017; Peters, 2014), with the following formula:
The result obtained is: = +0.84. According to McDonald (1999), this is a good index.
The measures of internal consistency of the perceived collective efficacy scale attests to the internal consistency and reliability of the instrument as used in French speaking Belgium. The scale was added to the end of the school climate questionnaire given annually to members of the educational teams in both the experimental and control schools. This choice was made by the researchers not to multiply the number of questionnaires to be completed by the teams.
To ensure the validity of the pre-test measure, the first administration of the scale took place before any detailed presentation of SCP was made, i.e., at the very beginning of the full team training day devoted to the selection and definition of values. The questionnaire was offered at the same time of year (between the third week of November and the winter break) for the next 2 yr.
School Climate Questionnaire
The Georgia School Climate Survey Suite (La Salle et al., 2021), in its "staff member" version (31 items, four points Likert scale), was translated and adapted using the following method: double translation, reconciliation, and expert validation.
The protocol described for the Perceived Collective Teacher Efficacy scale (field test, cognitive laboratory), was applied to the Climate questionnaire.
The sub-dimensions of the climate questionnaire are staff connectedness: staff perceptions of the degree to which they feel they fit in and are a part of their school (α = 0.80); structure for learning: staff perceptions of the degree to which they feel their colleagues treat students fairly, have high expectations, and set clear rules (α = 0.84); physical environment: staff perceptions of maintenance of school grounds and resources (α = 0.74); peer/adult relations: staff perceptions of how students interact with peers and adults in their school (α = 0.88); and parental involvement: staff perceptions of the degree to which parents are involved in their student's education (α = 0.83) and school safety: staff perceptions of their own safety at school (removed due to a lack of internal consistency).
Baseline Comparison
Besides school's characteristics comparability (Table 2), baseline comparability between the intervention and control groups was verified for all sub-dimensions of the school climate questionnaire, as this was the main outcome of the study. The difference between groups at pretest is less than half a standard deviation (Slavin, 2008) for each of the sub-dimensions of the climate questionnaire, with a minimal difference at pretest of ES = +0.02 and a maximal difference of ES = +0.44.
Baseline difference between groups with respect to perceived collective efficacy was greater than half a standard deviation on the total sample of teachers (Hedges g = −0.67). When computed by education level according to What Works Clearinghouse standards (2020), analysis shows that intervention and control groups are highly contrasted in primary education (Hedges g = −1.19) and comparable at secondary level (Hedges g = −0.08).
Data collection and software
Data collection to verify the effects of SCP implementation was conducted as follows: the pre-test questionnaire was administrated before starting SCP preparation. The first post-test questionnaire was administrated at the same period 1 yr later, which means 6 mo after the start of the implementation. The second post-test was administrated at the same period 2 yr later or 18 mo after the implementation started. Timeline for data collection can be found in Table 3.
To answer research questions 1, 2, and 3, SAS 9.4 software was used: to calculate effect sizes, first; to perform a Stepwise regression analysis, second; and to allow the software to test for a link between the climate questionnaire and perceived collective efficacy.
Question 1: Influence of SCP on Teachers' Collective Efficacy
The magnitudes of the effect of SCP implementation on perceived collective efficacy calculated first at post-test 1, then at post-test 2, for all staff members across all levels of education are quite large: ES = +0.80 at post-test 1 and ES = +0.71 at post-test 2. The detailed results are shown in Table 4.
TABLE 4. Teachers' collective efficacy comparison between baseline, post-test 1 and post-test 2 (all educational levels).
Since comparability was not ensured at pretest between intervention and control groups across all educational levels – there is comparability among secondary staff in terms of sense of collective efficacy, whereas this is not the case in elementary schools – effect sizes were computed separately for elementary and secondary schools. They are presented in Tables 5,6.
TABLE 5. Teachers' collective efficacy comparison between baseline, post-test 1 and post-test 2 (at the primary level).
TABLE 6. Teachers' collective efficacy comparison between baseline, post-test 1 and post-test 2 (at the secondary level).
The collective efficacy mean score increased at both time in intervention group, while it remained stable in the control group, being already higher at baseline. At the same time, the difference between intervention and control groups at pretest measured by Student's T became not significant at posttests 1 and 2. The effect sizes of SCP implementation on teachers perceived collective efficacy were +0.90 at post-test 1 and +1.02 at post-test 2 for educational teams in elementary schools. These results must be interpreted with caution considering that the experimental group was less confident in its collective efficacy at baseline.
The situation was different for educational teams in secondary schools. Intervention and control groups were comparable at baseline on demographic and on climate and collective efficacy scales. The effect size of SCP implementation on teachers perceived collective efficacy was positive and in favor of the intervention group at post-test 1 (ES = +0.47) and marginal at posttest 2 (ES = +0.07). In intervention group, mean results increased after 6 mo of implementation, and then remained stable. In control group, there was a slight increase at posttest 1, and another one at posttest 2, with a decrease of the standard deviation.
When mean results are examined, there was an increase in perceived collective efficacy between baseline and post-test 1 at both educational levels. This improvement was maintained at post-test 2, 18 mo after the start of the implementation, for secondary staff. Improvement went further at post-test 2 for primary staff.
Questions 2 and 3: Link Between Collective Efficacy and School Climate
Following the example of Malinen and Savolainen, 2016, the researchers wanted to investigate the possible links between the sense of collective efficacy and school climate. The results of the Stepwise regression analysis performed on SAS 9.4. can be found in Table 7. Model 5 was selected because it has the lowest Mallow relevance index (C(p) = 3.3751) and the highest percentage of variance explained (73.57% of variance explained). Stepwise regression results show that 73.57% of the variance is explained by the variables group (intervention vs. control), gender, structure for learning, physical environment, and peer and adult relationships.
The dimension of the climate questionnaire administered to teachers in the intervention and control groups that explained the greatest percentage of variance in teachers perceived collective efficacy was the dimension "structures for learning." It explained 63.78% of variance in collective efficacy.
Other elements intervened in the explanation of the variance and notably that of belonging to the intervention group. Membership of the intervention or control group explained 5.23% of the variance.
Considering the mean improvement of perceived collective efficacy in the intervention schools, we observe an improvement at both education levels at post-test 1 and post-test 2. Considering the improvement due to the intervention, the extent to which this perception has improved varies depending on the level of education and on the sharpness with which these results are analyzed. As we consider that comparability between groups on all independent (demographic) and dependent (climate and perceived collective efficacy) variables must be present at baseline, then this study concludes that, after the SCP implementation, the secondary school staff perceives an increase in collective efficacy. If some consider that comparability on the independent variables (demographics) and on the main dependent variable (school climate) is an indicator of groups' comparability at baseline, then both primary and secondary teams in schools implementing SCP perceive an improvement in collective efficacy.
As Kelm and McIntosh (2012) and Sørlie and Torsheim (2011) have demonstrated in their studies, the explanation for these effect sizes of SCP implementation on perceived collective efficacy is the decrease in behavioral problems and time spent on them. Indeed, the harmonization of common practices of positive and coherent discipline management within schools implementing SCP, thanks to all the components of the system, allows educational teams to see a decrease in problematic behaviors and the time spent on them. Through the implementation of SCP components, educational team members perceive greater collective effectiveness.
Based on the results of research conducted by (Ross et al., 2012), the researchers also postulate that the sense of collective efficacy of the educational teams in the four pilot schools in our country where SCP is implemented has increased well because teachers working in low socioeconomic status schools benefit from SCP first, which is the case for the pilot schools our country.
The improvement is maintained at post-test 2, although the changes in leadership in all the intervention schools happened shortly before taking the "climate and collective efficacy of teachers" questionnaires at post-test 2 and may have put the educational teams in significant difficulties in terms of the functioning of the schools. Indeed, these changes in terms of leadership and operational difficulties may have affected the teams' sense of collective efficacy, since previous research found a link between executive leadership, institutional functioning, and sense of collective efficacy (Goddard et al., 2000; 2001; 2004a, b; Leithwood et al., 2020, Sun and Leithwood, 2017; Tschannen-Moran and Barr, 2004).
As hypothesized in the second research question and as Malinen and Savolainen (2016) demonstrated in previous research, perceived collective efficacy is related to school climate. Stepwise regression analysis linking items from both climate and perceived collective efficacy instruments demonstrates this. Linking sub-dimensions of school climate to perceived collective efficacy explains up greatest variance in perceived collective efficacy. To the extent that school climate and perceived collective efficacy are part of a school's culture, the link between the two is not surprising. The concept of school climate refers to the quality and characteristics of school life (Cohen et al., 2009; Gage et al., 2016). School climate influences student outcomes in behavior and social skills (Gottfredson et al., 2005; McIntosh et al., 2006; Gage et al., 2016). The Stepwise regression analysis highlighted the subscale "structures for learning" as the part of the climate questionnaire that explained the most variance in collective efficacy. The items constituting this sub-dimension relate to what teachers put in place to support student success (e.g.: "my school promotes success for all students" or "teachers in my school work hard to ensure that all students do well"). The content of these items relates to the efforts made by the educational team to support students' academic success. To the extent that collective efficacy is defined as teachers' beliefs about the ability of the entire educational team to positively influence students and their success (see above) and to the extent that academic success is discussed as both a predictor of and an outcome of perceived collective efficacy (Bandura, 1993; Goddard et al., 2000, 2004b; Eells, 2011; Sun et al., 2017; Donohoo, 2018; Donohoo et al., 2018), it is not surprising to find a link here too between collective efficacy and the elements put in place to promote student success and perceived collective efficacy. This dimension "structures for learning" alone explains for the greatest variance in the sense of collective efficacy in the Stepwise model.
This study is subject to a few limitations. Firstly, at the beginning of the research, the questioning of the collective effectiveness of the educational teams with which the researchers would work to implement SCP indicated a deficit of collective effectiveness. Some of the educational teams were dysfunctional. It is worth noting that the research project on the effects of SCP implementation focused on schools in "adjustment mode." These "underachieving" schools were audited based on a few criteria, one of which concerned the (dys)functioning of the educational team. In other words, it was difficult for the research team to find comparison schools on that criterion. SCP implementation is not intended to solve team cohesion issues. However, thanks to the efforts made by the educational teams to harmonize their practices for the joint management of the various aspects of school discipline – prevention and correction – the collective effectiveness perceived by the teams themselves evolved positively, much more positively than the initial situation would have suggested.
Secondly, the size of the sample on which the research was conducted, and the "pilot" nature of the project do not allow for the generalization of the results observed. If SCP was implemented on a larger scale and the research replicated on a larger sample, the differences observed at baseline between the groups would be reduced. Furthermore, the absence of random components in the construction of the sample and its small size make it impossible to use inferential statistical tools.
Thirdly, the research team did not have access to information from the control schools, apart from that collected through the various questionnaires. It cannot be ruled out that specific situations or elements may have a link with the decrease in collective effectiveness perceived in these educational teams. Investigating the functioning of the schools in the control group would represent an added value for future research to ensure that it is indeed SCP implementation that improves the perceived collective effectiveness and not particular events experienced in the schools in the control group that explain the decrease in this same feeling within these schools.
Our findings give directions for future research. Firstly, schools in the intervention group enroll students from disadvantaged to very disadvantaged backgrounds and are therefore more likely to benefit from SCP implementation. Future research should implement SCP in schools with students from different socioeconomic backgrounds to compare the effects of these demographic characteristics on implementation and to verify that perceived collective efficacy increases more quickly and more in schools with more disadvantaged students.
Secondly, as the Stepwise regression, used to investigate whether a link between collective efficacy and school climate exists, does not allow to analyze mediating variables, SEM analyses could be conducted in future research.
Our findings also has some implications for practice. While the extent to which the perceived improvement in collective efficacy varies, the reasons for this improvement are the same: first, the set of components of SCP and the efforts required from the educational teams to implement them, and second, the socio-economic level of the schools in which SCP has been implemented. Harmonizing practices requires considerable effort, especially considering the pedagogical freedom usually granted to teachers in French speaking Belgium. Getting all members of an educational team to agree on common values, on expected behaviors, on an explicit and active way of teaching them, on a system of reinforcement as well as on a common policy for dealing with inappropriate behaviors is not an easy task, especially since it is not enough to agree in theory, but to apply them in a common and consistent way. Getting all staff members to agree on this common discipline and on its application by all requires considerable effort, which may well correspond to that mentioned by Bandura in his study (1993), and which can lead to a change in perceived collective effectiveness. Changing elements of a school's culture such as perceived collective efficacy requires significant change and effort.
As Sørlie and Torsheim (2011) have previously demonstrated, by jointly and consistently implementing SCP components, such as expected, and commonly known behaviors taught explicitly, reinforcement of appropriate behaviors, and a common policy for managing inappropriate behaviors applied consistently, the educational team acts on their students' behavioral issues and time. By aligning behavior management practices to reduce the occurrence of problem behaviors, the school team's collective effectiveness also increases, if all the key elements of SCP are implemented with fidelity.
It should also be noted that changes in school leadership can have an impact on perceived collective efficacy insofar as the leadership of school principals contributes significantly to the development and maintenance of effective schools through their ability to get their teams to work together by understanding the value of this collaboration and by believing in their ability to overcome, as a team, the obstacles on the way to their students' success (Bandura, 1993; Goddard et al., 2000; 2001; 2004a, b; Leithwood et al., 2020, Sun and Leithwood, 2017; Tschannen-Moran and Barr, 2004).
Overall, the main purpose of this study was to investigate the issue of perceived collective efficacy improvement through SCP implementation in a quasi-experimental research design. Using the scale translated from Skaalvik and Skaalvik (2007), the research team was able to observe the effect of SCP implementation on perceived collective efficacy in the pilot schools. Using measures collected in the control schools, the researchers found that the implementation of the key elements of SCP improved the collective efficacy perceived by the educational team. Regardless of the issue of group comparability at baseline that has been discussed in this article, the implementation of the various components of the SCP has effects on the perceived collective efficacy of educational team members in the intervention group schools.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics Statement
This study was approved by the Ethics Committee of the Faculty of Psychology, Speech Therapy and Education Sciences, University of Liege, Belgium [Project 17-18-70]. Data collection does respect the European Union General Data Protection Regulation (UE 2016/679).
Author Contributions
AB and CD contributed to conception and design of the study. CD organized the database. CM and DD performed the statistical analysis. CD wrote the first draft of the manuscript. AB and CD wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.
This research was funded by Administration générale de la Fédération Wallonie-Bruxelles as part of the Pacte pour un Enseignement d'excellence.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Bandura, A. (1993). Perceived Self-efficacy in Cognitive Development and Functioning. Educ. Psychol. 28 (2), 117–148. doi:10.1207/s15326985ep2802_3
Béland, S., Cousineau, D., and Loye, N. (2017). Utiliser le coefficient omega de McDonald à la place de l'alpha de Cronbach. mje 52 (3), 791–804. doi:10.7202/1050915ar
Cohen, J., Mccabe, E., Michelli, N., and Pickeral, T. (2009). School Climate: Research, Policy, Practice and Teacher Education. Teach. Coll. Rec. 111 (1), 180–213. Available at: https://www.researchgate.net/publication/235420504_School_Climate_Research_Policy_Teacher_Education_and_Practice.
Deng, L., and Chan, W. (2017). Testing the Difference Between Reliability Coefficients Alpha and Omega. Educ. Psychol. Meas. 77 (2), 185–203. doi:10.1177/0013164416658325
Donohoo, J. (2018). Collective Teacher Efficacy Research: Productive Patterns of Behaviour and Other Positive Consequences. J. Educ. Change 19, 323–345. doi:10.1007/s10833-018-9319-2
Donohoo, J., Hattie, J., and Eells, R. (2018). The Power of Collective Efficacy. ASCD resources. Available at https://www.ascd.org/el/articles/the-power-of-collective-efficacy
Eells, R. J. (2011). Meta-Analysis of the Relationship Between Collective Teacher Efficacy and Student Achievement. MS dissertation. Chicago (IL): Loyola University of Chicago.
Gage, N. A., Larson, A., Sugai, G., and Chafouleas, S. M. (2016). Student Perceptions of School Climate as Predictors of Office Discipline Referrals. Am. Educ. Res. J. 53 (10), 492–515. doi:10.3102/0002831216637349
Goddard, R. D. (2001). Collective Efficacy: A Neglected Construct in the Study of Schools and Student Achievement. J. Educ. Psychol. 93, 467–476. doi:10.1037/0022-06188.8.131.527
Goddard, R. D., Hoy, W. K., and Hoy, A. W. (2004a). Collective Efficacy Beliefs:Theoretical Developments, Empirical Evidence, and Future Directions. Educ. Res. 33, 3–13. doi:10.3102/2F0013189X033003003
Goddard, R. D., Hoy, W. K., and Hoy, A. W. (2000). Collective Teacher Efficacy: Its Meaning, Measure, and Impact on Student Achievement. Am. Educ. Res. J. 37 (2), 479–507. doi:10.2307/1163531
Goddard, R. D., LoGerfo, L. F., and Hoy, W. K. (2004b). High School Accountability: The role of Perceived Collective Efficacy. Educ. Pol. 18, 403–425. doi:10.1177/0895904804265066
Gottfredson, G. D., Gottfredson, D. C., Payne, A. A., and Gottfredson, N. C. (2005). School Climate Predictors of School Disorder: Results from a National Study of Delinquency Prevention in Schools. J. Res. Crime Delinquency 42, 412–444. doi:10.1177/0022427804271931
Grey, J., and Sime, N. (1989). "Findings from the National Survey of Teachers in England and Wales," in Elton Report, Discipline in Schools: Report of the Committee of Enquiry Chaired by Lord Elton. Editor Department of Education and Science and the Welsh office (London: Her Majesty's Stationary Office), 222–292.
Grisay, A. (2003). Translation Procedures in OECD/PISA 2000 International Assessment. Lang. Test. 20 (2), 225–240. doi:10.1191/0265532203It254oa10.1191/0265532203lt254oa
Harkness, J. (2003). "Questionnaire Translation," in Cross-Cultural Survey Methods. Editors J. Harkness, F. Van de Vijver, and P. Mohler Wiley, 35–56.
Kelm, J. L., and McIntosh, K. (2012). Effects of School-wide Positive Behavior Support on Teacher Self-efficacy. Psychol. Schs. 49 (2), 137–147. doi:10.1002/pits.20624
Klassen, R. M., Tze, V. M. C., Betts, S. M., and Gordon, K. A. (2011). Teacher Efficacy Research 1998-2009: Signs of Progress or Unfulfilled Promise? Educ. Psychol. Rev. 23, 21–43. doi:10.1007/s10648-010-9141-8
La Salle, T. P., Rocha-Neves, J., Jimerson, S., Di Sano, S., Martinsone, B., Majercakova Aobertova, S., et al. (2021). A Multi-National Study Exploring Adolescent Perceptions of School Climate and Mental Health Problems. School Psychol. 36 (3), 155–166. doi:10.1037/spq0000430
Leithwood, K., Sun, J., and Schumacker, R. (2020). How School Leadership Influences Student Learning: A Test of "The Four Paths Model". Educ. Adm. Q. 56 (4), 570–599. doi:10.1177/0013161x19878772
Malinen, O.-P., and Savolainen, H. (2016). The Effect of Perceived School Climate and Teacher Efficacy in Behavior Management on Job Satisfaction and Burnout: A Longitudinal Study. Teach. Teach. Edu. 60, 144–152. doi:10.1016/j.tate.2016.08.012
McDonald, R. P. (1999). Test Theory: A Unified Treatment. Mahwah, NJ: Lawrence Erlbaum. doi:10.1007/978-1-349-14280-4
McIntosh, K., Chard, D. J., Boland, J. B., and Horner, R. H. (2006). Demonstration of Combined Efforts in School-wide Academic and Behavioral Systems and Incidence of Reading and Behavior Challenges in Early Elementary Grades. J. Positive Behav. Interventions 8, 146–154. doi:10.1177/10983007060080030301
Múthen, L. K., and Múthen, B. O. (1998-2011). Mplus User's Guide. 6th Edn. Los Angeles, CA: Múthen & Múthen.
Ogden, T. (1998). Elevatferd og læringsmiljø: Læreres erfaringer med og syn på elevatferd og læringsmiljø i grunnskolen. Oslo: Kirke-, utdannings- og forskningsdepartementet.
Peters, G. J. Y. (2014). The Alpha and the Omega of Scale Reliability and Validity: Why and How to Abandon Cronbach's Alpha and the Route Towards More Comprehensive Assessment of Scale Quality. Eur. Health Psychol. 16 (2), 56–69. doi:10.31234/osf.io/h47fv
Ross, S. W., Romer, N., and Horner, R. H. (2012). Teacher Well-Being and the Implementation of School-wide Positive Behavior Interventions and Supports. J. Positive Behav. Interventions 14 (2), 118–128. doi:10.1177/1098300711413820
Skaalvik, E. M., and Skaalvik, S. (2007). Dimensions of Teacher Self-efficacy and Relations with Strain Factors, Perceived Collective Teacher Efficacy, and Teacher Burnout. J. Educ. Psychol. 99 (3), 611–625. doi:10.1037/0022-06188.8.131.521
Slavin, R. E. (2008). Perspectives on Evidence-Based Research in Education-What Works? Issues in Synthesizing Educational Program Evaluations. Educ. Res. 37 (1), 5–14. doi:10.3102/0013189X08314117
Sørlie, M.-A., and Torsheim, T. (2011). Multilevel Analysis of the Relationship Between Teacher Collective Efficacy and Problem Behaviour in School. Sch. Effectiveness Sch. Improvement 22 (2), 175–191. doi:10.1080/09243453.2011.563074
Sugai, G., and Horner, R. H. (2009). "Defining and Describing Schoolwide Positive Behavior Support," in Handbook of Positive Behavior Support. Editors W. Sailor, G. Dunlap, G. Sugai, and R. H. Horner (Germany: Springer), 307–326. doi:10.1007/978-0-387-09632-2_13
Sugai, G., and Horner, R. R. (2006). A Promising Approach for Expanding and Sustaining School-wide Positive Behavior Support. Sch. Psychol. Rev. 35 (2), 245–259. doi:10.1080/02796015.2006.12087989
Sugai, G., and Horner, R. (2002). The Evolution of Discipline Practices: School-wide Positive Behavior Supports. Child. Fam. Behav. Ther. 24 (1-2), 23–50. doi:10.1300/J019v24n01_03
Sugai, G., and Simonsen, B. (2012). Positive Behavioral Interventions and Supports: History, Defining Features, and Misconceptions. Mansfield: Center for PBIS and Center for Positive Behavioral Interventions and Supports.
Sun, J., and Leithwood, K. (2017). Calculating the Power of Alternative Choices by School Leaders for Improving Student Achievement. Sch. Leadersh. Manag., 37, 80–93. doi:10.1080/13632434.2017.12963510.1080/13632434.2017.1293635
Sun, J., Zhang, S., and Przybylski, R. (2017). "Conceptualizing the Critical Path Linked by Collective Teacher Efficacy," in 2017 Annual meeting of the American Educational Research Association, San Antonio, TX, April 27–May 1, 2017 (Washington, DC: San Antonio and AERA).
Tschannen-Moran, M., and Barr, M. (2004). Fostering Student Learning: The Relationship of Collective Teacher Efficacy and Student Achievement. Leadersh. Pol. Schools 3 (3), 189–209. doi:10.1080/15700760490503706
Tschannen-Moran, M., Hoy, A. W., and Hoy, W. K. (1998). Teacher Efficacy: Its Meaning and Measure. Rev. Educ. Res. 68 (2), 202–248. doi:10.3102/00346543068002202
What Works Clearinghouse (2020). What Works Clearinghouse Procedures Handbook. Washington, DC: U.S. Department of Education, Institute of Education Sciences. Available at: https://ies.ed.gov/ncee/wwc/handbooks.
Keywords: teachers' collective efficacy, school-wide positive behavior interventions and supports, school climate, quasi-experiment, stepwise
Citation: Deltour C, Dachet D, Monseur C and Baye A (2021) Does SWPBIS Increase Teachers' Collective Efficacy? Evidence From a Quasi-experiment. Front. Educ. 6:720065. doi: 10.3389/feduc.2021.720065
Received: 03 June 2021; Accepted: 27 July 2021;
Published: 16 August 2021.
Edited by:Tara Ratnam, Independent researcher
Reviewed by:Joseph Samuel Backman, Alpine School District, United States
Balwant Singh, Partap College of Education, India
Copyright © 2021 Deltour, Dachet, Monseur and Baye. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Ariane Baye, email@example.com; Caroline Deltour, email@example.com


text:
Citizens of Humanity
Tallulah Jumpsuit
Sorry, this item is no longer available
Thank You!
Your Back in Stock inquiry has been submitted.
We will email you at if this item becomes available.
Thank You!
Your inquiry was successfully submitted, and we will get back to you in 5-10 days.
Estimated Delivery : Aug 23
Preorder items will be charged when shipped.
No returns or exchanges
100% cotton. Made in USA. Hidden button front closures. Breast flap pockets with button closures. Side slit pockets. Back pockets. Lightly distressed fabric detail throughout. Our Style No. CIOF-WR6. Manufacturer Style No. 1538 673.
Established in 2003, CITIZENS OF HUMANITY has charmed the fashion world with a rigorous commitment to cutting edge denim styling, supreme comfort and fit without compromise. CITIZENS OF HUMANITY designs, markets and produces over one million pairs of jeans annually in their own off site laundries. Each piece of CITIZENS OF HUMANITY is unique and handcrafted to perfection.Shop the Collection

subset: commoncrawl


text:
Spatchcock Portuguese Style
Spatchcock is a poussin (young chicken) which best cooked by either roasting or grilling – to make this dish you'll need our delicious Portuguese seasoning from our Food Service range.
- 1 Spatchcock
- Sea salt
- 10g Portuguese style seasoning
- 100g Carrot
- 100g Onion
- 50g Celery
- 50g Leek
- 10g Thyme
- 250mL Brown stock
- 100mL Red wine
- 20mL Olive oil
KEY INGREDIENT: Spatchcock, stock, vegetables
KEY SKILLS: Poultry production (deboning)
EQUIPMENT NEEDS: Chef's knife, peeler, roasting tray, pan
PREPARATION TIME: 20 minutes + marinade
COOKING TIME: 20 minutes + sauce
GARNISHES: Fresh thyme, potatoes, vegetables
- Debone the spatchcock and flatten out for grilling. Roast the bones in the oven at 200°C
- Sprinkle the spatchcock with the sea salt and the Portuguese seasoning and marinate in the fridge for 30 minutes
- WPRW the vegetables and cut the carrots and celery into Macédoine. Use the offcuts and onion for mirepoix. Cut the leek for mirepoix
- Once the bones are browned add the mirepoix and brown lightly
- Add the thyme, stock and wine and simmer for 30 minutes to produce a jus
- Heat 2/3 of the olive oil in a pan and place the spatchcock skin down and seal until browned. Turn over and place in the oven at 200°C for 10-15 minutes. Blanch the Macédoine and refresh
- Strain the jus, adjust the seasoning and heat the Macédoine through in the remaining olive oil
- Pour the sauce onto a pre-warmed plate and sit the spatchcock on top and surround with the Macédoine


text:
SugarBook Feedback: An educated Glucose Dating site?
Glucose relationships try a modern-day way of dating where one another activities consent an economic bundle in exchange for like and you may company. Considered because of the some becoming a great 'soft' sorts of escorting, one individual (commonly an abundant and you will more mature man) will give funding on the Glucose Baby. In exchange for so it backing it obtain the ultimate GFE. The online matchmaking webpage, SugarBook, now offers a market best and deluxe services to possess possible Glucose Children to meet up their future backers.
Contained in this comment, we're going to look closer during the sugar dating website and you may read in the event it actually is the number one place to come across your following Sugar Daddy, Infant otherwise Momma.
What is SugarBook?
Introduced inside 2017 because of the Malaysian businessman Darren Chan, SugarBook try an internet portal in which wealthy someone discover and financially secure the mate of the alternatives in exchange for an excellent matchmaking.
'In which romance suits finance' 's the web site's strapline and you will appropriately describes the technique of sugaring; a term supplied to dating where one party even offers particular means of issue recompense to the other. Will currency, new regards to brand new exchange is right down to individuals involved and may even be everything from basic merchandise and you will purchasing every times so you can taking a regular monetary money (called an 'allowance').
Popular with pupils exactly who use the solution to greatly help buy tuition charge and you will/or rents, sugaring became popular and much more socially acceptable. Whilst others individuals understand the fresh practice to be similar to escorting, those who 'sugar' is actually not as inclined to discover its relationship by doing this.
Have a tendency to private, glucose infants as well as their daddies/mommas are not only trying to be spoiled and pamper also to track down a mutual relationship and interest.
SugarBook aims to promote this services giving an excellent program in which their users are able to find one another and securely discuss.
Sugaring actually another experience as well as the habit could have been common during the parts of asia and you may round the Western Europe for decades. The sole change is that the entire behavior could have been modernized which have sites like SugarBook making it easier and you may safe.
Just how Well-known are SugarBook?
The SugarBook application therefore the desktop site was preferred inside the Germany, You and Canada which have visited towards other Eu regions and additionally Uk. It is quite picking right up specific website visitors and subscriptions in Far-eastern regions instance Malaysia, Singapore, Vietnam and Asia.
In all, the service purports getting more than 700,100000 users around the globe with as much as 20% of them getting typical and you may effective pages of one's webpages at anyone date.
Since , the new SugarBook app try the new #3 Top Grossing App to your Play Store in the Malaysia and you will #15 into the Singapore!
How does SugarBook Really works?
Starting into the SugarBook is truly simple and is completely 100 % free for individuals who just want to browse ahead of investing a premium membership (select lower than).
Prefer their intercourse plus character (Sugar Father/Momma/Baby) then done their reputation playing with as often outline as you is. This is particularly important towards 'Sugar Babies' while the battle is hard with far fewer Glucose Daddies and you will Mommas online as opposed to those that happen to be trying to the patronage.
After you have establish up a legitimate account and character (since a sugar Daddy/Momma) you might use the site to look for compatible people, all you are searching for. There are lots of pre-place filter systems for instance the common place, ages, ethnicity and you can real attributes but you can in addition to lookup having fun with training height, dialects verbal and most notably the approach to life he's in search of.
By the lives i, of course, indicate the latest finances one to the company costs. This selections out of restricted so you're able to reasonable, substantial to help you highest with several Infants are flexible and several exactly who basically http://www.besthookupwebsites.org/escort/rochester-1 looking a practical stipend.


text:
Introduction to R
Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.
Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.
Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.
Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.
Learn to transform and manipulate your data using dplyr.
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.
Learn to combine data across multiple tables to answer more complex questions with dplyr.
Learn the language of data, study types, sampling strategies, and experimental design.
Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
In this course you will learn the basics of machine learning for classification.
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.
Take your R skills up a notch by learning to write efficient, reusable functions.
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests.
Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.
Learn to perform linear and logistic regression with multiple explanatory variables.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Learn the essentials of parsing, manipulating and computing with dates and times in R.
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Learn how to make predictions about the future using time series forecasting in R.
Transform almost any dataset into a tidy format to make analysis easier.
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
In this course you'll learn to add multiple variables to linear models and to use logistic regression for classification.
Learn the core techniques necessary to extract meaningful insights from time series data.
Master sampling to get more accurate statistics with less data.
The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.
Learn to work with data using tools from the tidyverse, and master the important skills of taming and tidying your data.
Learn how to pull character strings apart, put them back together and use the stringr package.
Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.
In this course you will learn to fit hierarchical models with random effects.
Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
In this course, you'll learn about the concepts of random variables, distributions, and conditioning.
Learn to easily summarize and manipulate lists using the purrr package.
Analyze text data in R using the tidy framework.
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
In this course you'll learn how to leverage statistical techniques for working with categorical data.
In this course you will gain an overview clinical trial designs, determine the numbers of patients needed and conduct statistical analyses.
Learn survey design using common design structures followed by visualizing and analyzing survey results.
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Explore latent variables, such as personality using exploratory and confirmatory factor analyses.
This course will show you how to combine and merge datasets with data.table.
Learn to work with time-to-event data. The event may be death or finding a job after unemployment. Learn to estimate, visualize, and interpret survival models!
Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.
Learn to streamline your machine learning workflows with tidymodels.
In this course you'll learn to analyze and visualize network data with the igraph package.
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Apply your importing and data cleaning skills to real-world soccer data.
Discover the top tools Kaggle participants use for data science and machine learning.
Use tree-based machine learning methods to identify the characteristics of legendary Pokémon.
Use logistic regression to determine which treatment procedure is more effective for kidney stone removal.
Load, clean, and explore Super Bowl data in the age of soaring ad costs and flashy halftime shows.
Analyze health survey data to determine how BMI is associated with physical activity and smoking.
Apply hierarchical and mixed-effect models to analyze Maryland crime rates.
Use your logistic regression skills to protect people from becoming zombies!
Predict the impact of climate change on bird distributions using spatial data and machine learning.
Use cluster analysis to glean insights into cryptocurrency gambling behavior.
Apply unsupervised learning techniques to help plan an education program in Argentina.
Use data science to catch criminals, plus find new ways to volunteer personal time for social good.
Explore the salary potential of college majors with a k-means cluster analysis.
Analyze admissions data from UC Berkeley and find out if the university was biased against women.
Analyze the dialog and IMDB ratings of 287 South Park episodes. Warning: contains explicit language.
Experiment with clustering algorithms to help doctors inform treatment for heart disease patients.
Explore acoustic backscatter data to find fish in the U.S. Atlantic Ocean.
Write functions to forecast time series of food prices in Rwanda.
Apply text mining to Donald Trump's tweets to confirm if he writes the (angrier) Android half.
Use regression trees and random forests to find places where New York taxi drivers earn the most.
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
Apply your skills from "Working with Dates and Times in R" to breathalyzer data from Ames, Iowa.
Create and explore interactive maps using Leaflet to determine where to open the next Chipotle.
How can we find a good strategy for reducing traffic-related deaths?
Get ready for Halloween by digging into a FiveThirtyEight dataset with all your favorite candy!
Examine the relationship between heart rate and heart disease using multiple logistic regression.
Examine the network of connections among local health departments in the United States.
Analyze the relative popularity of programming languages over time based on Stack Overflow data.
Explore a dataset from Kaggle containing a century's worth of Nobel Laureates. Who won? Who got snubbed?
Flex your data manipulation muscles on breath alcohol test data from Ames, Iowa, USA.
Compare life expectancy across countries and genders with ggplot2.
Analyze data from the hit mobile game, Candy Crush Saga.
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
Analyze the relative popularity of programming languages over time based on Stack Overflow data.
Write functions to forecast time series of food prices in Rwanda.


text:
I keep my Quicken files in my main Documents folder of one of my iMacs, which is synced among all of my Macs using Resilio Sync. If I'm careful, and always quit Quicken on the machine that currently I'm working on, I can share the Quicken file safely among all of my machines. I also use Carbon Copy Cloner to separately backup the Quicken folder (including all of the automatic backups), to a USB drive that's mounted locally on that iMac. Also, the computers that are sharing the file using Resilio Sync are all backing up locally using Time Machine. No problems so far.
Just picking up on the thread after a few months.
Speaking from practical experience, I've tried for a few years to migrate my Quicken 2007 data into a newer version. Each time it's failed. After reading this thread, I copied my Quicken 2007 data to a HFS+ formatted flash drive and was able to successfully migrate the data.
I'm still figuring out how to get the reports I used in 2007 to match in the current version, but maybe I can upgrade beyond Mojave now.
What Reports did you use in Q2007? I was successful in recreating the Business - Income Statement Report.
Also a question to all new Quicken Deluxe 2022 users:
In Q2007, when I type in a Payee that I have used before, it remembers and brings up the last transaction, including the proper Category; so I can just modify the amount, if needed and move on to the next transaction.
But so far in my limited experience with QD2022, if I type the Payee, it remembers only the full name of the Payee and I have to manually add the rest, including the hardest part to remember, the Category.
Anyone with a work-around?
I had same problem. It seems to be fixed if you go to Quicken preferences- register. At bottom, I checked both "automatically create rule" for payee name and "rule when I edit transaction" — all fields
When you type in a payee name that you have used before in Quicken Deluxe 2022, after typing in a few letters it should offer you a list of "Best matches" below to complete the payee name. If it has carried over your categories properly from Quicken 2007, it should offer you a category name in italics below the payee. Click on that combination and it will enter the Payee name and Category you want.
If it does not remember the category, fill in the payee name and hit Tab to get into the Category box. Then it will offer you something like "Uncategorized" as a category name and above it include a blue box with a place to check followed by "Save Quickfill rule for this payee with the category" entered below. Replace "Uncategorized" with whatever category you want, check the box, and it should enter it automatically every time you use the same payee if you follow the procedure in the preceding paragraph.
If you are entering information by downloading from a bank or other financial institution, Quicken 2022 will pick up the category supplied in the download. Odds are that won't be what you want and you will have to change it.
If you want you can also use the blue box's dropdown menu to tell Quickfill to save all fields in the transaction, including category, tags, memo, and amount.
In Quicken 2007, I would manually download my American Express and Citibank MC and Visa cards transactions manually from their website to then manually Import into Quicken.
Originally these financial institutions supported downloads as QIF, but some years ago abandoned that.
So then I would download as CSV and use Moneythumbs' CSV2QIF converter to provide me with the necessary QIF file for Quicken 2007 to. import.
Now with the new Quicken 2022, I have experimented with auto-update on my two AmExp cards and I am happy so far.
But I still want to manually import transactions for my two Citicards.
Is there a way in the new Q2022 to do this for either QIF or CSV?
I do notice that Citibank also allows me to download as QFX, QBO or OFX formats, but I have no experience with these formats.
QFX is basically Quicken 2022's version of QIF, so that should work. I use the QFX file Apple generates for my Apple Card to import those transactions into Quicken.
Ever since QFX format was introduced, I've used it to manually download my credit card statements to my checkbook program (first Quicken and now Moneydance). With the exception of some coding issues with Citibank many years ago, the processing was smooth, with me only needing to change some transaction categories to my own custom categories and merge the preexisting payment transaction with the incoming on from the statement.
Is this where I would Import the recent month's QFX data? I am fearful of losing my complete history of a credit card file in my Quicken if I make a mistake:
I would imagine Bank or Brokerage File would do it, but I've always just double-clicked the QFX file which runs Quicken (or brings it to the front) and does the import. You could make a copy of your data file and then try it (or just restore from a backup if things go awry).
I agree. It took about 6 months to find the rhythm. There's more data above the Reconcile listings than with Quicken. For one thing, a quick check of the Deposit list in SEE and the Deposits and Other Additions on the bank statement to see they agree and that's done.
2 posts were split to a new topic: Running an M1 Mac mini as a headless server
Great thread!
After a long time using Quicken 2007 on my G4 PowerPC, I hit the dreaded "Transaction File is Full" error yesterday on one of my files, which has pushed me to find a solution. Since I'm a reluctant upgrader, my other ("modern") Mac is a mid-2007 iMac running El Capitan. As the new Quicken only supports the last three OS versions, if I want to upgrade to Quicken 2022 I'll need to buy a machine capable of running Catalina or newer.
Before I make that investment (and leap!), I need to figure out if Q2022 is going to do what I need it to do. I've read this article and the subsequent comment thread in its entirety, and I have just a few questions. Assuming I buy a new iMac and subscribe to Q2022:
Will I be able to take a copy of my G4 PPC Q2007 data file, put it on an HFS+ flash drive, and use that on the new iMac to to convert to Q2022?
Will all my categories and sub-categories remain intact? (I don't (at least not yet) use online connectivity to download transactions, I do them manually.)
Is there a software-architecture based "transaction limit" in Q2022, similar to the one I ran into in Q2007?
I use Balance Sheet reporting (as well as Income Statements, noted in the thread). Can I accomplish this in Q2022?
This seems silly, but if I import my entire data file, will I be able to access all transactions, and use them in reporting? Or are transactions older than a certain date "locked out" somehow?
My alternative to taking this route is to create a new "blank" Quicken 2007 file with all my categories and accounts intact, beginning with transactions dated 01/01/22, and make opening-day balance adjustments, and continue on using my G4 and Q2007. Now, I'm not opposed to doing so, if it is indeed my best option, but I'd really like a longer-term, more "stable" solution.
Thank you in advance for any advice!
In my experience, upgrading from O7 to 22, the data file is upgraded/converted intact. I lost no accounts, categories, classes, notes etc. My transactions go back to 1993 and everything is there. I enter all transactions, I do not download credit card or investment transactions so I cannot speak to that. I did need to reformat my reports but they are easily customized, as they were in past versions. All in all I found the transition easy. There are visual changes but it functions essentially the same. Also, the old data files remain in addition to the converted files. And old quicken app also remains, so you can experiment with 22 if you wish before making the leap.
Very quick question for @glennf (or anyone else who may be able to answer). If you see my query just above you will see what I'm trying to do.
From reading the thread, it seems Glenn successfully moved his data from PowerPC architecture to Intel architecture. Can Glenn (or anyone) tell me how they did it?
I'm having trouble importing a Q2007 .qdfm data file from my PowerPC to my new iMac. I have successfully rebuilt the database, made a .qdfm copy using "Make A Copy" onto an HFS+ thumbdrive, and moved it over to my new iMac. It uploads to the Quicken icloud, converts, and seemingly downloads, but gives me an error on importing.
Thinking it may be because it's an old, large data file, I went back to my PowerPC and created a brand new Q2007 data file with two accounts and two register entires each. Same error.
Any guidance or info would be appreciated!
Did you compress it as a ZIP file before moving it? That seems to be key.
Hi Glenn, and thank you for the quick response.
I did compress the file to ZIP, then moved it to the new iMac (Intel / Monterey) via HFS+ thumb drive, with the same result.
To clarify what I think I read, were you successfully able to move a PowerPC .qdfm data file to an Intel iMac, and have "new" Quicken import it? Or did you use another step between, like perhaps importing to an older version of Quicken?
Also, it appears there was an Intel-native version of Q2007; I'm wondering if that would help with my data translation.
Quicken 202* can directly import Quicken 2007 files—you don't need to export. I'd forgotten that. There's a cloud-conversion stage you must given permission to, as it uploads your file transiently to the cloud to perform it. I'm sorry that I just don't remember any more than is in the article, though: it was complicated, it worked, and I dumped the knowledge from my brain after writing this, apparently. Good luck!
To answer some of your questions:
I was able to upload MOST of my Q7 file, but it failed miserably on my investments, importing only about a year's worth. I don't know if I fumbled something or if Quicken Deluxe did not like the way I had the files structured. In other areas, it was able to import data back to 2000.
Your categories and sub-categories will remain intact. However, if you try downloading transactions – which I find very convenient – you will have to override the categories Quicken imports from banks and credit cards, which have their own codes.
I know of no problem with accessing all transactions except the investments which Quicken Deluxe hosed.
While I am annoyed with some parts of Quicken Deluxe, I can't recommend staying with a badly outdated financial problem that you can only run on an outdated Mac.


text:
NORWALK -- Norwalk native Billy Genuario will be performing his show "Rockers and Crooners" at 8 p.m. on Friday, Feb. 1, at 8 p.m. at The Bijou Theatre in Bridgeport. The show will benefit The Alzheimer's Association of Connecticut. The show is a tribute to some of the greatest singers and bands of our time and is dedicated to Billy's mom, Kathi, who passed away from Alzheimer's last month.
- Norwalk hopes to finish new charter for a vote in November 2023
- Norwalk watches improvements grow at Colonial Village
- Brien McMahon Class of '72 celebrate 50th reunion in September
- Health care company with Norwalk offices announces layoffs
- Photos: Construction begins on city's first new school in 50...
- Broadway in the Park returns to Norwalk
- City's deal with software company in limbo amid contract concerns
- New Canaan business fails tobacco inspection, police say
- Norwalk pushes for vaccinations after pandemic decline
- More than 2,800 Norwalk residents have voted in CT primary
In-Depth Coverage


text:
But With this action, we're addressing what takes place Once you build your list. What exactly do you have to be texting subscribers — so that they don't start tuning out of the messages?
And if you don't have already got a web site that will get site visitors, it is possible to Make a website from scratch using GetResponse or produce a landing site and travel traffic to the page from Facebook, Instagram, WhatsApp, etc.
Just remember that SMS needs a slightly various strategy by using a lighter contact than with the electronic mail marketing.
You need to initially discover the basics, gradually raise the complexity of use instances, until eventually you're Completely ready to use SMS across the board.
With SMS with the ability to attain consumers very promptly and directly, it's No surprise that it's an significantly well-known digital marketing channel.
The other significant, and arguably far more critical, distinction between SMS and e mail is the personal mother nature from the channel.
Want to start out a text message discussion with our customer care crew? Just enter your amount here.
Persons usually read texts promptly the moment they obtain them. In fact, they open ninety% of these inside of 3 minutes.
Some critical issues to speak to buyers is what sort of messages you'll send. And how frequently they anticipate to receive texts from you. Whenever you build your concept style and frequency, Make sure you follow the expectations you set.
We'll share an easy two-action tactic You may use to collect telephone figures and produce a contact listing jam packed with potential clients:
So before you decide to even deliver your initial SMS campaign, be sure you have the mandatory authorization from a prospects.
Multimedia information is much more engaging than text. So it's essential to spend money on an SMS marketing Answer that means that you can deliver multimedia messages.
In order to ensure continued achievement, check in together with your objectives often. See should you're attaining results or if you must change your method of maintain you on track.
Arrange a landing webpage and signup form. In case you have a web site that gets a superb quantity of traffic, you may send sms api out your people to the signup sort that lets folks opt in.
About sms marketing
But With this action, we're addressing what takes place Once you build your list. What exactly do you have to be texting subscribers — so that they don't start tuning out of the messages?


text:
Patrick Marleau Jersey
Dak Prescott Authentic Jersey
Matt Read Womens Jersey
Danton Heinen Womens Jersey
Brad Marchand Jersey
Miami Dolphins Womens Jersey
Sorry, we aren't online at the moment. Leave a message.
Need more help? Save time by starting your support request online.
Help us help you better! Feel free to leave us any additional feedback.


text: Subscribe to our newsletter By subscribing, you agree with Revue's Terms of Service and Privacy Policy and understand that The Wine Business Advisor™ will receive your email address. Oct 12, 2018 Wine Marketing Weekly - The Marijuana Issue #14 Weed, cannabis, dagga, marijuana or whatever you want to call it may be the biggest threat to face th Wine Marketing Weekly - The Marijuana Issue #14 By Mike & Roland • Issue #14 • View online Weed, cannabis, dagga, marijuana or whatever you want to call it may be the biggest threat to face the wine industry in years? Or perhaps it is the biggest opportunity? Take a few minutes to peruse this curated selection of views and opinions on this momentous global shift. Will Wines of South Africa or VINPRO address this? Could the WOSA acronym have a new meaning? Has James Downes contemplated Merlot & Marijuana? Is Beyers Truter already working on a Pinotage & Pot 375ml for Tesco's? TOP 5 - WINE & WEED BUSINESS IN THE NEWS TERROIR: WHAT DO WEED & WINE HAVE IN COMMON? PRESTIGE CAN BE BASED ON WHERE YOU GROW. Weed and wine have a whole lot more in common. Think of Bordeaux, Champagne, Stellenbosch and Napa. These wine-makers have set themselves apart from the competition based on geographical region. Now, cannabis producers are looking to cash in on a similar prestige by setting the standard for their chosen region. But for cannabis, these appellations might be called Mendocino, Banhoek or Humboldt—regions that have long acted as the breadbasket of cannabis. herb.co • Share Pinotage & pot? GOURMET: THE BEST WEED & WINE PAIRINGS TO TRY. When thinking of a side to accompany your wine, you may think cheese, charcuterie or olives, but have you ever considered weed? Yes, weed, cannabis, dagga, marijuana or whatever you want to call it actually pairs quite nicely with wine. Merlot & marijuana anyone? Pinotage & pot? www.vix.com • Share Have a Coke and a smile? BIG BUSINESS: EVEN COCA-COLA IS DIPPING INTO THE CANNABIS INFUSED DRINK MARKET Long gone are the controversial days of Coca-Cola infusing cocaine into its popular product, now they've turned their attention to cannabis. If the company is able to capture just 10% of the projected market size, it would bring in $2.2 billion in revenue by 2022. Have a Coke and a smile? www.forbes.com • Share BIG BUSINESS: MOLSON COORS ENTERS THE CANNABIS DRINKS MARKET Beer giant Molson Coors has landed a deal with a medical marijuana firm in a bid to turn a profit in the small but fast-growing cannabis drinks category. Heineken became the first major drinks giant to launch a non-alcoholic cannabis beer in the US infused with THC – the psychoactive component of the plant. Could this be big business? Is Distel's Richard Rushton doing any market research in his spare time? www.thedrinksbusiness.com • Share WTF? A NON-ALCOHOLIC, CANNABIS-INFUSED WHITE WINE MIGHT BE A WINNER? In place of alcohol, a bottle of this Sauvignon Blanc comes with 20 milligrams of tetrahydrocannabinol (THC) by volume, the principal psychoactive chemical in cannabis. That's approximately 5 milligrams per glass. It's not an exact science, but 5 ml of THC is supposed to produce a quick buzz. Better than smoking it? www.forbes.com • Share BIG BUSINESS: THE 3RD ANNUAL WINE & WEED SYMPOSIUM IN CALIFORNIA IN 2019 This one day conference will explore, in depth, what the wine industry can expect the impact to be over the next year and what opportunities, as well as threats, will exist. The conference will feature experts from both categories to discuss what's actually legal and what's not as well as regulations around licensing, hospitality, growing and other topics of interest. Worth a trip? Hehehehe … wine-weed.com • Share TWEET OF THE WEEK BY @ROBERTJOSEPH wine thinker @robertjoseph As predicted, California Cannabis Appellations are on their way... https://t.co/8rbpibLAj9 https://t.co/q2GrTKXMTO 5:03 PM - 11 Oct 2018 THE CORPORATE VIDEO OF THE WEEK! Cannabis industry poised for tremendous growth says Constellation Brands CEO. Did you enjoy this issue? By Mike & Roland Insights, innovation, news, views and cutting-edge wine business trends from around the world. Visit our website to learn more about how Wine Business Advisors™ can help you maximise profits in your wine business. www.winebusinessadvisors.co.za Tweet Share In order to unsubscribe, click here. If you were forwarded this newsletter and you like it, you can subscribe here. Powered by Revue Stellenbosch, South Africa Share Share to Twitter Share to Facebook Subscribe to our newsletter By subscribing, you agree with Revue's Terms of Service and Privacy Policy and understand that The Wine Business Advisor™ will receive your email address.


text:
Bipartisan proposals spearheaded by state Rep. John Roth that will allow victims of assisted reproduction fraud to seek justice were advanced overwhelmingly by the Michigan House today.
The plan, which also includes bills from state Reps. Bronna Kahle (R-Adrian), Karen Whitsett (D-Detroit) and Kelly Breen (D-Novi), protects people born through assisted reproductive methods who seek the truth regarding their backgrounds. The bills allow for civil liability when an individual knowingly, intentionally or willfully provides false or misleading information related to a procedure, establish a new criminal conduct framework and a 15-year lookback timeframe for charges from the time of the offense or DNA identification, and provide investigatory powers to the state to evaluate allegations.
"There is currently no recourse for the people who are born through assisted methods – only for parents who opt for an assisted method and realize what they were told up front wasn't reality," said Roth, of Traverse City. "When people find out they were lied to about their backgrounds, it's incredibly jarring. These plans offer protection for everyone involved in assisted reproduction who seeks the truth. They also provide a strong deterrent for people who intentionally mislead or knowingly provide false information during the assisted reproduction process."
There are currently no federal requirements that facilities such as sperm banks obtain and verify a donor's medical history, educational background, or criminal record – leading to the potential for misrepresented information. The plans work to improve general oversight into assisted reproductive facilities at the state level.
House Bills 5713-17 now move to the Senate for consideration.
State Rep. John Roth today criticized Gov. Gretchen Whitmer for a proposed plan to suspend the state's sales tax on school supplies after she vetoed Republican-led tax reduction plans in the last few months.
Rep. Roth says the state budgets for the 2023 Fiscal Year signed into law over the last two weeks provide additional dollars for mental health in Michigan, and mean local community colleges will be able to provide four-year nursing degrees.
State Rep. John Roth today voted to advance a budget plan that puts students, workers, families and communities throughout northern Michigan in a better position to thrive. Roth specifically highlighted education funding within the Legislature's plan. A $19.6 billion school aid fund includes a record-high per-student base grant of $9,150, and a $575 million plan […]
© 2009 - 2022 Michigan House Republicans. All Rights Reserved.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.


text:
Druva Announces $10 Million Data Resiliency Guarantee
Druva has announced a $10 million data resiliency guarantee for customers spending more than $25k a year.
SIOS High Availability Software
SIOS is a good option for old-school style clustering and failover software for 2- and 3-node SQLServer and SAP clusters.
Fungible Can Be Anything But What Is It Today?
Fungible believes the future of infrastructure is composable and scale-out based on DPUs. But what is it today?
Kasten is the Cure for Container Chaos
Kasten is a solution to a problem that shouldn't exist, but does because people are terrible.
3-Tier Cloud Native Architectures
We advocate for a three tier cloud native architecture that embraces the benefits of cloud native techniques where it makes sense to do so but also retains existing, proven approaches to data storage.
StormForge For Optimal Kubernetes
StormForge wants to put automated Kubernets optimisation into your DevOps workflow.
Pulumi Goes Business Critical
Pulumi launches a business critical tier for 24×7 enterprise customers of its infrastructure automation software.
NetApp's Ongoing Reinvention
NetApp continues its reinvention into a cloud services portfolio company, which is still very much a work-in-progress.
The IT Tyrant Test
The IT Tyrant Test is simple: What if Terraform decided to be evil today?


text:
Buccaneers observations: Tampa Bay finally turns in best performance of season
So that result was expected all along.
OK, not really.
If you had the Tampa Bay Buccaneers winning 27-7 over the Washington Redskins on Sunday at FedEx Field, we'd love to hear your Powerball picks. The Bucs improved to 2-8 after what was, by far, their best effort of the season.
The defense swarmed. Mike Evans starred. An offense that proved listless for so much of the year shredded an overmatched opponent.
Yeah, just like what everyone expected.
Here are a few thoughts from the Bucs' rout ...
1. It doesn't matter what the Bucs' depth chart says, Mike Evans is Tampa Bay's top receiver.
The Texas A&M product continues to impress, and he became the first rookie wide receiver since Randy Moss in 1998 to produce three straight games with at least 100 yards receiving and one touchdown. He closed Sunday with seven catches for 209 yards and two touchdowns, and he has become a spark this offense needs without many weapons elsewhere.
Vincent Jackson has been a strong performer in the NFL for many years, but Evans is becoming a more valuable receiving option. With Evans' youth, he's only going to improve.
Look for him early. Look for him often.
Evans' ceiling is high, and the Bucs should be pleased about the potential of their first-round pick. He's a star in the making.
2. Robert Griffin III looked like he's a long way from where he needs to be.
Credit Tampa Bay's defense for making Griffin look uncomfortable all afternoon. He closed 23-of-32 passing for 207 yards with one touchdown and two interceptions. He was sacked six times. He didn't do anything notable in the second half, and this was quite the feather for the Bucs' defense to claim.
This kind of performance is what Lovie Smith envisioned when he chose to bring his version of the Tampa-2 to the Bucs. This was an aggressive, effective unit that made Washington look anemic. The Bucs outgained the Redskins 329 yards to 322.
Where has this been most of the season? The more this type of swarm continues, the better.
3. That was the Bucs' best first half of the season.
This game was won before halftime. The Bucs forced three turnovers. They sacked Griffin three times. Smith's defense attacked. It was opportunistic.
Truth be told, the Bucs should have led by more than 13-0 midway through the second quarter. Both field-goal drives could have ended in touchdowns.
Still, credit Tampa Bay for this breath of fresh air. Sure, a five-play, 79-yard touchdown drive by Washington to close the second quarter was disappointing. But overall, that first half was impressive.
4. Johnthan Banks continues to grow into a fine CB.
Remember when Smith wanted Mike Jenkins to be a starting cornerback opposite Alterraun Verner? That seems like a mistake averted after another Sunday when the second-year Mississippi State product made an impact.
Banks showed his athleticism in catching a tipped pass from Griffin and returning it 19 yards for a touchdown late in the first quarter. He also fared well as Tampa Bay's de-facto No. 1 cornerback with Verner inactive because of a hamstring injury.
Banks' emergence has become a bright spot in a disappointing year for a once-hyped defense. This unit needs more than two players (Gerald McCoy and Lavonte David) to draw hope from.
Banks is becoming a possible third name to follow.
5. Don't look now, but the Bucs are two wins out of first place in the NFC South.
Remember those wild statements by Smith that the Bucs are in contention in the NFC South? Well, they became a little less far-fetched with Tampa Bay improving to 2-8.
The New Orleans Saints' loss to the Cincinnati Bengals means the NFC South leader has no more than four victories. If the Bucs play like they did Sunday, it's easy to see them beating the Chicago Bears next Sunday. Then who knows what can happen against the Bengals, Detroit Lions, Carolina Panthers, Green Bay Packers and Saints the rest of the way?
Tampa Bay still needs to prove that Sunday was a preview of things to come, not an aberration. But this rout was a strong first step.
Who would have thought?
Up next: The Bucs face the Bears next Sunday at 1 p.m. at Soldier Field.
You can follow Andrew Astleford on Twitter @aastleford or email him at firstname.lastname@example.com.