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when I try to find SCT variable. it's come out with the three reciprocal determinism (behavioral, personal and environment). when reading articles its discuss about self efficacy and outcome expectancies. self efficacy and outcome expectancies are related to which reciprocal determinism? is it behavioral or personal? I don't think it's environmental..

other theories i saw straight to the point and even provide IV and DV. so researchers could know and justify their study.

what is your opinion?

I want to use social cognitive theory on leadership. I could say that the prominent variables exist in the SCT model  is self efficacy and outcome expectancies that always repeated in every model. is there any other variables?

I´m searching for research on attitudes towards e-mental health resp. online self-help services (including acceptance, preferences, adherence, engagement, expectancies, concerns, etc.) in the general population. I´m looking forward to your feedback - thank you!

I am asking it for marketing research, and I am doing a comparative study. I want to generate Placebo expectancies through electronic word of mouth communication (palcebo/non placebo) to check its impact on consumer attitude toward the product, and the relationship is going to be mediated by advertising (palcebo/non placebo). But I am not sure about its validity. I am using persuasive communication as a placebo only (no tangible product). Can anyone please help?

Hi,

I am looking for a scale to determine if participants are more prone to accept scientific or 'transcendental explanations to alternative therapies. My hypothesis is that if expectancies/beliefs and explanations provided are coherent, the alternative therapy will have a bigger effect.

Is there any validated tool to measure a similar construct? I am thinking about some scale containing items like 'science cannot explain everything', 'if a therapy is not scientifically tested, it should not be used', or 'some phenomena are beyond our understanding'.

Thanks!

I am running an EFA to examine factor structure of a measure of alcohol expectancies. When interpreting the factor structure, I noticed several high, negative loadings. These were all reverse scored items (which I have double checked--I reverse scored them prior to the EFA). Is there any obvious explanation for this? Since they are negative loadings, it does not make conceptual sense why they loaded onto this particular factor. So I want to make sure I understand before I just eliminate them.

I am currently undertaking a meta-analysis (in psychology) considering the relationship (r) between expectancies and side effects, and whether the pooled effect sizes differ depending on moderator analyses (i.e. the use of different scales). There was not a significant difference between effect sizes with one of the moderator analyses, however Orwin's failsafe calculation indicated that there was potentially publication bias. When this was corrected with trim and fill analyses one of the effect sizes dropped substantially. I am wondering whether it is appropriate to investigate whether the difference between effect sizes is statistically significant following the adjustment. If so, how can I calculate this? I am using the program Comprehensive Meta-Analysis. 

Thank you in advance for any assistance  

Hello! I am a little confused about when to use the ANCOVA vs. when to decide for a multiple regression and would really appreciate your suggestions to help me understand this better!

In a study that I'm currently reading the researchers are comparing two groups of students (experimental and control group). Prior to the intervention the students's success performance is measured with a multi-item self-report scale. The methods part describes: "The data were analyzed using multiple regression with dummy codes representing the nesting of students, teachers, and schools. The focal predictor war the interaction between the dummy code for experimental condition (0 = control, 1= relevance) and student's performance expectation for the course. We predicted that this interaction term would be negative, such that the intervention effect would be more positive for those with low as opposed to high performance expectations".

So, I assume that the variable "performance expectation" acts somehow as a moderator here, which is continuous while the group variable is clearly categorical.

However, I don't understand why the researchers decided to use the multiple regression instead of ANCOVA and what the advantages of this method are in this specific case.

Further the authors conclude in the results part: "The predicted values from the regression equation indicate that students with low success expectancies (one standard deviation below the mean) reported more interest in science at the end of the semester in the relevance condition than in the control condition, whereas students with high success expectancies (one standard deviation above the mean) reported similar level of interest regardless of experimental condition. Do I get it right that they are creating two categories here (out of the continuous variable)by putting numbers in the regression equation in order to show the interaction very clearly in their graph?

Thank you so much for your advice!