Roy,

I attach a pdf of The New Science of Wise Psychological Interventions by Gregory Walton of Stanford University, that give great ideas for interventions and great references for the background of Mindsets that impinge on performance in academics.

I am so grateful for your initiative and concern and for the work you have done for many years in developing the young people whom you serve.

Kind regards,

Janet

Thank  You Janet!

That was a very interesting and useful article. It helped clarify some of my thinking regarding my PhD research topic. The conceptual model I am testing is based on the work of Carol Dweck (amongst others).

Kind Regards

Roy

Roy,

My dissertation will be a validation study of the What's My School Mindset? survey. I'm interested if you have come across any articles relating to school level validation surveys for Mindset?

Janet

Janet,

My focus is on the Learning Environments of Mathematics classrooms so I don't have many specific articles for you however you may be interested in the following:

Jonsson, A.-C., et al. (2012). "Teachers’ implicit theories of intelligence: influences from different disciplines and scientific theories." European Journal of Teacher Education: 1-14.

A sample of 226 Swedish high school teachers from various knowledge domains completed self-report measures of intelligence regarding implicit theories and scientific theories of intelligence. A mixed ANOVA showed that teachers from language, social science and practical disciplines had a significant preference for an incremental theory of intelligence compared to an entity theory of intelligence whilst the teachers in mathematics did not. One of the conclusions was that entity theories of intelligence may be more pronounced among teachers in mathematics. Second there is a significant relation between naïve beliefs in intelligence as fixed and inborn, entity theories, and the scientific g-factor theory. Last, it was the oldest and most experienced and youngest and least experienced teachers who preferred an entity theory of intelligence the most.

Also there has been a lot of research on the School Climate in the Learning Environments field where they often need to validate school level surveys. Some of these may be useful - for example

Aldridge, J. and K. Ala’I (2013). "Assessing students’ views of school climate: Developing and validating the What’s Happening In This School? (WHITS) questionnaire." Improving Schools 16(1): 47-66.

This article describes the development and validation of a six-scale survey to assess school climate in terms of students’ perceptions of the degree to which they feel welcome and connected, together with a scale to assess students’ perceptions of bullying. The development of each survey involved a multi-stage approach, including: 1) an extensive review of research related to school climate to identify components that can be considered important for effective schools made up of diverse students; 2) elucidating the scales identified in step one; and 3) writing individual items within the scales. Items from previously validated questionnaires were examined and, if appropriate, adapted. We used Trochim and Donnelly’s (2006) framework for construct validity to guide the validation of the new questionnaire. When the questionnaire was administered to a sample of 4067 high school students from eight schools, various statistical analyses ensured the questionnaire’s discriminant, convergent, concurrent and predictive validity.

I'm also interested in using Multi-level modelling to control for the Nested nature of the school learning environment: Student within Class within School. Two of the methodology focused papers I found useful are

Lüdtke, O., et al. (2009). "Assessing the impact of learning environments: How to use student ratings of classroom or school characteristics in multilevel modeling." Contemporary Educational Psychology 34(2): 120-131.

In educational research, characteristics of the learning environment (e.g., social climate, instructional quality, goal orientation) are often assessed via student reports, and their associations with outcome variables such as school achievement or student motivation then tested. However, studying the effects of the learning environment presents a series of methodological challenges. This article discusses three crucial elements in research that uses student reports to gauge the impact of the learning environment on student outcomes. First, from a conceptual point of view, it is argued that ratings aggregated at the relevant level (e.g., class or school level), and not individual student ratings, are of primary interest in these studies. Second, the reliability of aggregated student ratings must be routinely assessed before these perceptions are related to outcome variables. Third, researchers conducting multilevel analyses need to make very clear which centering option was chosen for the predictor variables. This article shows that conclusions about the impact of learning environments can be substantially affected by the choice of a specific centering option for the individual student ratings.

Marsh, H. W., et al. (2012). "Classroom Climate and Contextual Effects: Conceptual and Methodological Issues in the Evaluation of Group-Level Effects." Educational Psychologist 47(2): 106-124.

Classroom context and climate are inherently classroom-level (L2) constructs, but applied researchers sometimes?inappropriately?represent them by student-level (L1) responses in single-level models rather than more appropriate multilevel models. Here we focus on important conceptual issues (distinctions between climate and contextual variables; use of classroom L2 rather than student-level L1 measures) and more appropriate multilevel models. To illustrate these issues, we consider the effects of two L2 classroom climate variables and one L2 classroom contextual variable on two L1 student-level outcomes for 2261 students in 128 classes. Through this example, we illustrate how to apply evolving doubly latent multilevel models to (a) evaluate the factor structure of L1 and L2 constructs based on multiple indicators of classroom climate and context measures, (b) control measurement error at L1 and L2, (c) control sampling error in the aggregation of L1 responses to form L2 constructs (the average of student-level responses to form classroom-level constructs), and (d) provide guidelines for appropriate analysis of classroom climate as an L2 construct. [Supplementary materials are available for this article. Go to the publisher's online edition of Educational Psychologist for the following free supplemental resources: Substantive basis of the present investigation and more detailed description of the methodology.]

Hope these were of some use. I have copies of each article if you're unable to locate them.

Regards

Roy

Roy,

The information you provided was awesome! I have ordered the articles and also find the concept of Centering useful as I had not included this in the design specifically as yet. However, if this concept is what I think it is, the survey I am validating uses a Level 1 Center for its means and the surveys I am comparing to use a level 2 center. I wonder if this will make a difference in the correlation analysis if I am looking to identify relationships between the factors (constructs) of the surveys not necessarily the outcomes,

Janet