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US NSF - SBE - SES - Division Static Template on 2009-10-30
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Spencer Foundation - Login on 2009-09-25
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FS09-TE-150-019 Reflections on Learning on 2009-09-14
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I have started something simliar with my dog. I have trained her to raise her paw everytime I say: "Luka, if you're the best dog in the world, raise your paw." I first just tried getting her to raise her paw, speaking in English which makes no sense to her. After a few minutes I would continue saying raise your paw while lifting her paw off the ground while I said it. Associating the words and inflections in what I was saying to the specified desired action made it somewhat more understandable for her. What really made it stick was when she started to understand it I would bribe her with a treat so from this point on whenever I say: "Luka, raise your paw if you're the best dog in the world" she associates getting a dog biscuit with my desired action. I have to start the sentence first thought by saying: "If you're the best dog in the world..." because if I start it with "Raise your paw if you're the best dog in the world" she stops listening after the raise your paw part.
In this instance Luka has been conditioned classically to associate the raising of her paw with a treat. The conditioned response is her raising her paw, her unconditioned response is knowing she'll recieve a treat if she performs specific actions. The conditioned stimulus is the treat and the unconditioned stimulus is my voice telling her what to do.
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APA Dissertation Research Award Program - Information & Application Instructions on 2009-08-27
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science-oriented doctoral students of psychology
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graduate students of psychology
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Applicants must have had their dissertation proposals approved by their dissertation committees prior to application
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Each psychology department (i.e., not individual programs within a department) may endorse no more than three (3)
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raduate department other than psychology is eligible to apply for the Dissertation Research Award only if she/he demonstrates that she/he is writing a psychological science dissertation and that her/his graduate course of study has been primarily psychological in nature.
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KeepVid: Download and save any video from Youtube, Dailymotion, Metacafe, iFilm and more! on 2009-08-25
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Derry, Levin, Osana, Jones, & Peterson (2000) on 2009-08-04
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An a priori coding scheme, based on a set of norms derived from the cognitive literature on statistical reasoning, was used to evaluate student responses. Interviews were transcribed and coded to identify the positive and negative features of students' explanations in terms of the 5 criteria listed above. Two coders, "blind" to both student identity and pretest/posttest status, coded all the transcripts; discrepancies between coders' judgements, when they arose, were resolved through discussion. Scores were determined such that student explanations exhibiting the greatest number of positive features and the least number of negative features received the highest scores. Students pretest scores were also used as a measure of pre-course course knowledge so that the interaction between initial knowledge and amount of improvement could be assessed.
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Structured Interviews on 2009-08-04
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A structured interview is one in which a sequence of questions or "probes", often accompanied by a set of tasks which students are asked to complete, is carefully chosen beforehand. The props, tasks, questions, and responses to students' statements (e.g., the particular wording of follow-up questions, requests for clarification, and focusing prompts) are planned in advance to insure consistency across students and groups of students.
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The data collected via structured interviews can be analyzed in multiple ways. Scoring-rubrics are often applied to interview data in order to test hypotheses about the relationship between various pedagogical techniques, technological tools, or student strategies and differences in learning outcomes.
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interviews are conducted individually
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Univariate GLM: Statnotes, from North Carolina State University, Public Administration Program on 2009-07-27
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GLM uses a generalized inverse of the matrix of independent variables' correlations with each other, it can handle redundant independents which would prevent solution in ordinary regression models.
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GLM uses a generalized inverse of the matrix of independent variables' correlations with each other, it can handle redundant independents which would prevent solution in ordinary regression models.
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GLM uses a generalized inverse of the matrix of independent variables' correlations with each other, it can handle redundant independents which would prevent solution in ordinary regression models.
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Finally, because GLM uses a generalized inverse of the matrix of independent variables' correlations with each other, it can handle redundant independents which would prevent solution in ordinary regression models.
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Data are assumed to come from a random sample for purposes of significance testing
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he variance(s) of the dependent variable(s) is/are assumed to be the same for each cell formed by categories of the factor(s) (this is the homogeneity of variances assumption).
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However, in GLM the researcher must ask for "Parameter estimates" under the Options button in the GLM dialog
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The R-square from the Regression procedure will equal the partial Eta squared from the GLM regression model.
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Note that analysis of variance tests the null hypotheses that group means do not differ. It is not a test of differences in variances, but rather assumes relative homogeneity of variances.
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key ANOVA assumptions are that the groups formed by the independent variable(s) are relatively equal in size and have similar variances on the dependent variable ("homogeneity of variances"). Like regression, ANOVA is a parametric procedure which assumes multivariate normality (the dependent has a normal distribution for each value category of the independent(s))
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One may also perform planned comparison or post hoc comparisons to see which values of a factor contribute most to the explanation of the dependent
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ANCOVA uses built-in regression using the covariates to predict the dependent, then does an ANOVA on the residuals (the predicted minus the actual dependent variables) to see if the factors are still significantly related to the dependent variable after the variation due to the covariates has been removed.
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f you select Custom, your model should not include interactions of factors with covariates: that is used beforehand in testing the equality of regressions assumption discussed below in the "Assumptions" section, but not in the ANCOVA model itself
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It does not contain factor-by-covariate interactions
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Since randomization in principle controls for all unmeasured variables, the addition of covariates to a model is rarely or never needed in experimental research.
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In regression models, to fit regressions where there are both categorical and interval independents. (This third purpose has become displaced by logistic regression and other methods. On ANCOVA regression models, see Wildt and Ahtola, 1978: 52-54
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The formulas for the t-test (a special case of one-way ANOVA), and for the F-test used in ANOVA, thus reflect three things: the difference in means, group sample sizes, and the group variances. That is, the ANOVA
F-test is a function of the variance of the set of group means, the overall mean of all observations, and the variances of the observations in each group weighted for group sample size. T
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Two-way ANOVA is less sensitive than one-way ANOVA to moderate violations of the assumption of homogeneity of variances across the groups
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egression models. In univariate GLM, entering only covariates and no factors in the model is equivalent to specifying a regression model.
The "Parameter Estimates" table in univariate GLM (ANOVA) gives the same b coefficients for a regression model as in the "Coefficients" table in SPSS OLS regression output, as shown in the figure below
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factors as well as covariates,
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where an equal number of subjects is assigned randomly to each of the cells formed by the factors (treatments)
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When there is an à priori reason for thinking some additional independent categorical variable is important, the additional variable may be controlled explicitly by a block design (see below), or by a covariate (in ANCOVA) if the independent is a continuous variable.
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Latin square designs also reduce the number of observations necessary to compute ANOVA.
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Factorial ANOVA is for more than one factor
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Balanced designs are simply factorial designs where there are equal numbers of cases in each subgroup
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ssuring that the factors are independent of one another (but not necessarily the covariates)
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In RCB designs, subjects are matched together in blocks (ex.,age group), then one (usually) member of each block is randomly assigned to each treatment.
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provided (1) every treatment appears at least once in some blocks and (2) each block has some of the same treatments.
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f, however, a treatment does not appear in any block, then significance tests should utilize Type IV sums of squares, not the default Type III.
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The essential difference is that the planned multiple comparison tests in this section are based on the t-test, which generally has more power than the post-hoc tests listed in the next section.
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Also note that all these t-tests are subject to the equality of variances assumption and therefore the data must meet Levene's test,
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That is, post-hoc tests are used when the researcher is exploring differences, not limited by ones specified in advance on the basis of theory. These tests may also be used for confirmatory research but the t-test-based tests in the previous section are generally preferred.
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However, note that post hoc tests do not control for the levels of other factors or for covariates (that is, interaction and control effects are not taken into account)
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The more unequal the sample sizes in the cells, the more likely violation of the homogeneity assumption.
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a rule of thumb is that the ratio of largest to smallest group variances should be 3:1 or less.
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ANOVA is robust for small and even moderate departures from homogeneity of variance (Box, 1954
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If the Levene statistic is significant at the .05 level or better, the researcher rejects the null hypothesis that the groups have equal variances
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The Levene test is robust in the face of departures from normality.
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however, that failure to meet the assumption of homogeneity of variances is not fatal to ANOVA, which is relatively robust, particularly when groups are of equal sample size.
Example.
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When groups are of very unequal sample size, Welch's variance-weighted ANOVA is recommended.
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But if there are no data for some of the cells, the ordinary computation of sums of squares ("Type III" is the ordinary, default type) will result in bias. When there are empty cells, one must ask for "Type IV" sums of squares, which compare a given cell with averages of other cells.
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r with any regression model
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for any models mentioned above and any balanced or unbalanced model as long as there are no empty cells in the design
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he dependent variable should be normally distributed in each category of the independent variable(s)
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he F test in ANOVA is robust even for moderate departures from multivariate normality, so this is among the less crucial assumption of ANOVA, assuming kurtosis is non-estreme (from -1 to +2) and sample size is not very small (ex., <= 5).
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Unbalanced designs require adjustments in how ANOVA is computed. This is done automatically in ANOVA and MANOVA in SPSS.
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is assumed for repeated measures designs and for random effect designs.
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ANOVA does
not assume
linear relationships and can handle interaction effects in most cases. However, note that for block designs, ANOVA assumes
additivity -- that raw scores are an additive combination of the mean, the group effect, the block effect, and an error term, meaning that it assumes there is no interaction between the group factor (ex., the independent variable representing the treatment) and the block factor (ex., the independent variable used as an explicit control in assignment of subjects).
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Imperfect measurement reduces the statistical power of significance tests for ANCOVA and for experimental data, there is a conservative bias (increased likelihood of Type II errors: thinking there is no relationship when in fact there is a relationship) . As a rule of thumb, covariates should have a reliability coefficient of .80 or higher.
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Scatterplots of the covariate and the dependent for each of the k groups formed by the independents is one way to assess violations of this assumption.
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ovariates may be transformed (ex., log transform) to establish a linear relationship.
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The covariate coefficients (the slopes of the regression lines) are the same for each group formed by the categorical variables and measured on the dependent.
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he more this assumption is violated, the more conservative ANCOVA becomes (increased likelihood of Type II errors: thinking there is no relationship when in fact there is a relationship).
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can be tested under the Model button of Analyze, General Linear Model, Univariate; select Custom under the Model button; enter a model with all main effects of the factors and covariates and the interaction of the covariate(s) with the factor(s). These interaction effects should be non-significant if the homogeneity of regressions assumption is met.
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o high multicollinearity of the covariates
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whose squared correlation with prior covariates is .50 or higher.
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The values of the dependent are an additive combination of its overall mean, the effect of the categorical independents, the covariate effect, and an error term.
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If the covariate is influenced by the categorical independents, then the control adjustment ANCOVA makes on the dependent variable prior to assessing the effects of the categorical independents will be biased since some indirect effects of the independents will be removed from the dependent.
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Regression Experimental Designs: A Beginning Example on 2009-07-26
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This is true of paired designs too
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all completely
randomized designs can be put into a regression context
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INFO SHEET : GENETICALLY MODIFIED FOOD: PROS AND CONS on 2009-06-14
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sues. It is the product of a 5 year study by our expert working group, looking at the knotty
ethical and social questions in plant and animal genetic engineering. The working group comprised senior scientists working in the field as well as specialists in ethics, theology, sociology, public perception and risk. This multi-disciplinary approach is central to SRT's work. It has enabled us to present a unique perspective balancing different viewpoints, and examining the wider social implications as much as the ethic
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