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Department
Psychology
Course
PSYC 3380
Professor
Jeffrey Spence
Semester
Winter

Description
Week One 1/16/2013 2:24:00 PM Chapter One pp. 3-13 statistics reform: incorporates effect size estimation, the reporting of confidence intervals, and synthesis of results from replications in a meta- analysis some critical problems with behavioral science research are: - most articles published in our research literature are never cited by other authors and thus, by definition, have little or no impact on the field - there are problems with the quality of many published studies in terms of their actual scientific contribution, how the data were analyzed, or how the results were interpreted - there is a disconnect across many behavioral science disciplines between the conduct of research on the one hand and the application of those results on the other Chapter Two pp. 15-35 there are three healthy aspects of the research tradition in the behavioral sciences: anchor to reality - sometimes students new to the behavioral sciences are surprised at the prominent role accorded to research in academic programs - there are some potential advantages to possessing the ability to think critically about how evidence is collected and evaluated that is afforded by a research-based education (such as having a skeptical attitude about a proposed medical treatment, the need for evidence reduces extreme claims made by practitioners, having realistic beliefs) rise of meta-analysis and meta-analytic thinking - meta-analysis: a set of statistical techniques for summarizing results collected across different studies in the same general area; a type of secondary analysis where findings from primary studies are the unit of analysis; the central tendency and variability of effect sizes are more relevant than the statistical significance of each individual study - meta-analytic thinking includes: reporting of results should be made so that they can easily be incorporated into a future meta-analysis (including the reporting of sufficient summary statistics so that effect sizes can be calculated), a researcher should view their own individual study as making at best a modest contribution to a research literature, an accurate appreciation of the results of previous studies is essential (especially in terms of effect sizes), and retrospective interpretation of new results (once collected) are called for via direct comparison with previous effect sizes waxed exceeding mighty - information explosion is fueled by computer technology which has made possible electronic publication and distribution over the internet - open-access journals: refereed electronic journals that can be accessed without cost and are generally free of many copyright and licensing restrictions - self-archiving research repositories: electronic databases where works by researchers in a common area are stored for later access by others - information fatigue: refers to the problem of managing an exponentially growing amount of information (for example, the total number of scientific journals is now so great that most libraries are unable to physically store the printed versions of them all, much less afford the total cost of institutional subscriptions) - impact factor: a descriptive, quantitative measure of overall journal quality; it is a bibliometric index published annually by the Institute for Scientific Information (ISI) which analyzes citations in over 14, 000 scholarly journals – it reflects the number of times the typical article in a journal has been cited in scientific literature; the IF is subject to bias, it is based on mainly English language scientific journals (which only consist of about one quarter of peer-reviewed journals worldwide), online availability of articles (those with full-text availability are cited more often than those available on a more restricted basis), the IF is computed for a whole journal, but citations generally refer to articles, not journals (thus it is possible that a relatively small number of frequently cited articles are responsible for most of the value of IF for a whole journal), the tendency for authors to cite their own publications can inflate IF; a more controversial use of the IF is as a measure of the quality of work of individual scholars or entire academic unites there are four negative aspects to our research literature: 1. skewness and waste - the rejection rate for journals ranges from 80-90% - a typical journal article receives few citations and has relatively few interested readers - only a small number of published articles are both widely read and cited (the 80/20 rule: about 20% of published articles generate about 80% of the citations) - the uneven distribution of publications and citations in science supports the elite in the scientific realm through new discoveries or the development of new paradigms (a shared set of theoretical structures, methods, and definitions that supports the essential activity of puzzle solving, the posing and working out of problems under the paradigm) 2. wide gap between research and policy or practice - example: the formation of education policy is infrequently informed by the results of education research - specifically within education, there are cases where education research follows policies (such as learning disabilities) – in the U.S. a learning disability is defined in federal law based on an IQ-achievement discrepancy model, in which children are identified as learning disabled when their IQ scores are in the normal range but their scores on scholastic achievement tests are much lower – they are entitled to remedial services under federal law but children who have a low IQ score and low achievement test score are considered slow learners and are not entitled to remedial services because it is believed that they will not benefit from it – there is little evidence that IQ scores and achievement test scores measure two different things 3. lack of relevance for practitioners - researchers can communicate poorly with practitioners when reporting their findings using language that is pedantic or unnecessarily technical (using excessively complicated statistical techniques and the description of results solely in terms of their statistical significance) - clinical psychology practitioners have said that research topics are sometimes too narrow or specific to be of much practical value 4. incorrect statistical results - some of the errors in reported statistical results was due to typographical errors in printed values of test statistics and some can be due to errors in the reporting of summary statistics the next three problems are catastrophic concerning the scientific merit of our research literature; they are also interrelated in that weakness in one area negatively affects quality in other areas (these problems afflict “soft” research areas more than “hard” research areas): 1. little real contribution to knowledge - only about 10% of all journal articles present new enlightening information 2. lack of cumulative knowledge - theoretical cumulativeness: empirical and theoretical structures build on one another in a way that permits results of current studies to extend earlier work - the number of true scientific breakthroughs in psychology over the last few decades is very modest 3. paucity of replication - replication is paid scant attention in the behavioral science research literature there are three possible reasons for the overall poor state of behavioral science research: 1. soft science is hard - soft science (non-experimental research) is more difficult than hard science (experimental) because in some cases, for ethical reasons or even human limitations, it is not possible to randomly assign individuals to certain groups - human behavior may be much more subject to idiographic factors (specific to individual cases; concern discrete or unique facts or events that vary across both cases and time – experiences and environments) than to nomothetic factors (genetics and common neural organization) than physical phenomena (the general laws or principles that apply to every case and work the same way over time) – if this is true, then there is less potential for prediction - context effects tend to be relatively strong for many aspects of human behavior (how behavior is expressed often depends on the particular familial or social context) – also known as interaction effects; tend to reduce the chance that a result will replicate across different situations, samples or times - our practices concerning measurement in the behavioral sciences are often too poor, especially when we try to assess the degree of a hypothetical construct - the soft behavioral sciences lack a true paradigm which is necessary for theoretical cumulativeness – the use of common tools is only a small part of a paradigm; there is little agreement in the soft behavioral sciences about what main problems are and how to study them 2. overreliance on statistical tests - not only do we rely on statistical significance tests too much, but we also misinterpret the outcomes - it has also been said that research progress is hindered by our dysfunctional preoccupation with statistical tests 3. economy of publish or perish - least publishable unit (LPU): refers to the smallest amount of ideas or data that could generate a journal article – used in a sarcastic way to describe the pursuit of the greatest quantity of publications at the expense of quality - sometimes the publish or perish economy for academicians is rationalized by the thought that active researchers make better teachers, however the correlation between these two domains (research productivity and teaching effectiveness) among professors is zero. Chapter Four pp. 73-76 comparative studies: at least two different groups or conditions are compared on an outcome (dependent) variable quantitative research: there is an emphasis on 1) classification and counting of behavior, 2) analysis of numerical scores with formal statistical methods and 3) role of the researcher as an impassive, objective observer qualitative research: the researcher is often the main data-gathering instrument through immersion in the subject matter, such as in participant observation there are three basic steps involved in connecting your research question with a possible design and there are three possible research questions: 1. descriptive: involves the simple description of a sample of cases on a set of variables of interest; it is relatively rare when research questions are solely descriptive 2. relational: concerns the covariance between variables of interest; more common; typically about the direction and degree of covariance 3. causal: concerns how one or more independent variables affects one or more dependent variables – better to evaluate these questions with multiple groups, some of which are exposed to an intervention but others are not or a single sample that is measured across multiple conditions, such as before- and-after treatment (both comparative studies) if assignment to groups or conditions is random, then the design is experimental; if any other method is used and a treatment effect is evaluated, the design is quasi-experimental if one-to-one matching is used to pair cases across treatment and control groups, then part of the design has a within-subject component; otherwise the design is purely between-subject if each case in every group is tested only once testing each case on multiple occasions also implies a design with a within- subject component, in this case a repeated measures factor Chapter Four pp. 92-116 in quasi-experimental designs, cases are assigned to treatment or control groups using some method other than random assignment – this implies that the groups may not be equivalent before the start of the treatment nonequivalent-group designs: the treatment and control groups are intact, or already formed; these groups may be self-selected; ideally they should be as similar as possible and the choice of group that receives the treatment is made at random - the most basic nonequivalent-group design has two groups measured at posttest only (posttest only design); the absence of pretests makes it extremely difficult to separate treatment effects from initial group differences, therefore, the internal validity of this design is threatened by all forms of selection-related bias - pretest-posttest design: tests before and after treatment; still subject to many selection-related threats even if the tests are identical (ex. Selection- regression bias if cases in one group were chosen due to extreme scores, selection-maturation concerns the possibility that the treatment and control groups are changing naturally at different rates in a way that mimics a treatment effect, selection-history is the possibility that events occurring between the pretest and posttest differentially affected the treatment and control groups), all forms of internal validity threats for multiple-group studies apply to this design as well - ANCOVA: a covariate analysis that statistically controls for group differences on the pretest; its use to adjust group mean differences on the dependent variable for group differences on pretests in nonequivalent-group designs is problematic because unless the pretests measure all relevant dimensions along which intact groups differ that are also confounded with treatment, then any statistical correction may be inaccurate - an alternative to running an ANCOVA is using an MR (multiple regression) – any type of ANOVA is just a restricted form of MR (one of these restrictions is the homogeneity of regression assumption which can be relaxed in an MR because it is possible to represent in regression equations the inequality of slopes of within-group regression lines) – in MR, a standard ANCOVA is conducted by entering group membership and the covariate as the two predictors of the outcome variable; the specific form of this equation is: Y = B(sub1)X + B(sub2)Z + A - where Y is the predicted score on the outcome variable, B(sub1) is the unstandardized regression coefficient (weight) for the difference between treatment and control (X), B(sub2) is the unstandardized coefficient for the covariate (Z), and A is the intercept (constant) term of the equation - B(sub1) equals the average difference between the treatment and control groups adjusted for the covariate (the predicted mean difference) – interpretation of this predicted mean difference assumes homogeneity of regression - moderated multiple regression: the term moderated refers to the inclusion in the equation of terms that represent an interaction effect for this equation, add B(sub3)XZ – this is the product of X (group membership scores) and Z (covariate) - propensity score analysis (PSA): another alternative to an ANCOVA, more complex; important method for statistically matching cases from nonequivalent groups across multiple pretests; the first phase in a PSA are propensity scores (the probability of belonging to the treatment or control group, given the pattern of scores across the pretests (these scores can be estimated using logistic regression – where the pretests are treated as a dichotomous variable to the selection of group being treatment or control) – each case’s scores are reduced to a single propensity score; the second phase consists of standard matching of treatment cases with non-treatment cases based on pretest scores - hidden bias: the degree of undetected confounding that would be needed to appreciably change study outcome - double pretest design: the administration of the same measure on 3 occasions, twice before treatment, and once after; accounts for selection- maturation bias (can tests group rate change between two pretests and see if both groups change at the same rate/same direction and compare to posttest to see if there is a difference due to treatment); internal validity is still susceptible to selection-history, selection-testing, and selection- instrumentation bias regression-discontinuity designs: cases are assigned to conditions based on a cutoff score from an assignment variable, which can be any variable measured before treatment – there is no requirement that the assignment variable should predict the outcome variable; the cutting score is often established based on merit or need; implies that groups are not equivalent before treatment begins; because the selection process (how cases wind up in treatment or control groups) is totally known, the internal validity of this design is much closer to that of experimental designs than that of nonequivalent-group designs - a type of pretest/posttest design where participants are measured before treatment and after - the selection process permits statistical control of the assignment variable in regression analysis - when looking at a scatterplot of treatment vs. control groups, a treatment effect would show a “break” in the regression line right near the cutoff score; the increase in the treatment group would be constant - the magnitude of the discontinuity between the regression lines at the cutting score estimates the treatment effect - there is no selection bias, differential maturation or history bias, regression artifacts bias, or measurement error in the assignment variable within a regression-discontinuity design - assuming linearity and homogeneity of regression, the predictors of the dependent variable are (1) the dichotomous variable of treatment vs. control (X) and (2) the difference between the score on the assignment variable (O sub a) and the cutting score (C) for each case – this subtraction forces the computer to estimate the treatment effect at the cutting score (which is also the point when the groups are the most similar); the equation for this is: Y = B(sub 1)X + B(sub 2)(O sub a –C) + A - B(sub 2) estimates the treatment effect at the cutoff point - if the assumption of either linearity or homogeneity is not met, the results may not be correct using this equation one-shot case study: a single group is measured once after an intervention; no control group; most primitive of quasi-experimental designs one group pretests-posttest design: also no control group, pretests before treatment and posttest after treatment about the only way to strengthen the internal validity of a study without a control group is to use multiple pretests or posttests removed-treatment design: an intervention is introduced and then later removed repeated-treatment design: an intervention is introduced, removed, and then re-introduced; threats to the attribution of changes to treatment in a repeated treatment design would have to come and go on the same schedule as the introduction and removal of treatment a time series is a large number of observations made on a singular variable over time interrupted time-series design: the goal is to determine whether some discrete event – an interruption – affected subsequent observations in the series - the basic aims of a time series analysis are threefold: 1. statistically model the nature of the time series before the intervention, taking account of seasonal variation 2. determine whether the intervention had any appreciable impact on the series, and if so then 3. statistically model the intervention effect (concerns whether the intervention had an immediate or delayed impact, whether this effect was persistent or decayed over time, and whether it altered the intercept or slope of the time series) - autoregressive integrative moving average model (ARIMA): uses lags and shifts in a time series to uncover patterns, such as seasonal trends or various kinds of intervention effects; it is also used to develop forecasting models for a single time series or even multiple time series; an advanced statistical technique for time series analysis that may require 50 observations or so case-control/case-referent/case-history/retrospective design: a type of comparative design but not for directly evaluating treatment effects; cases are selected on the basis of an outcome variable that is typically dichotomous - it is common to match cases across the two groups on relevant background variables - case-control designs are appropriate in situations where randomized longitudinal clinical trials are impossible or when infrequent outcomes are studied - threats to internal validity include selection bias, differential attrition of cases across patient and non-patient groups - the real value of case-control studies comes from review of replicated studies (from a meta-analytic perspective) nonexperimental studies only have statistical control when possible confounding variables are included in the analysis to support causal inferences; the accuracy of statistical control depends on the researcher’s ability to identify and measure potential confounders there are statistical techniques that estimate direct and indirect causal effects among variables that were concurrently measured such as: - path analysis: an older technique in SEM; estimates causal effects for observed variables, modern versions can also estimate causal effects for latent variables (constructs); can be interpreted as evidence for causality only if we can assume that the model is correct however, researchers rarely conduct SEM analyses in a context where the true causal model is already known equivalent models: explain the data just as well as the preferred model but do so with a diff configuration of hypothesized effects among the same variables; it offers a competing account of the data it is only with the accumulation of the following types of evidence that the results of nonexperimental studies may eventually indicate causality: 1. replication of the study across independent samples 2. elimination of plausible equivalent models 3. the collection of corroborating evidence from experimental studies of variables in the model that are manipulable 4. the accurate prediction of effects of interventions Week 2 1/16/2013 2:24:00 PM Pg. 39-72 Chapter 3 Trinity Overview  Design – internal validity, external validity  Measurement – construct validity  Analysis – conclusion validity Design  5 Structural Elements of an empirical study o 1) Samples (groups) o 2) Conditions (treatment or control) o 3) Method of assignment to groups or conditions (eg. Random) o 4) Observations o 5) Time, or the schedule for measurement or when treatment begins or ends  Random assignment = experimental design  Quasi-experimental design o 1) Cases are divided into groups that do/do not receive treatment using any other method OR o 2) There is no control group but there is a treatment group o Difficult to reject alternative explanations  Cause-probing designs – inferences about cause-effect relations are of paramount interests (experimental and quasi-experimental)  Non-experimental designs o Presumed causes and effects may be identified and measured o Difficult to make plausible causal inferences o Presumed causes are not directly manipulated  3 Types of designs are not mutually exclusive  Best Possible Design o 1) Theory-grounded b/c theoretical expectations are directly represented in the design o 2) Situational in that the design reflects the specific setting of the investigation o 3) Feasible in that the sequence and timing of events, such as measurement, is carefully planned o 4) Redundant b/c the design allows for flexibility to deal with unanticipated problems without invalidating the entire study o 5) Efficient in that the overall design is as simple as possible, given the goals of the study  Hypotheses express questions/statements about the existence, direction, and degree of the relation/covariance b/w 2 or more variables  Designs provide context and control extraneous variables o Nuisance variables – introduce irrelevant or error variance that reduces measurement precision  Controlled through a measurement plan that specifies proper testing environments, tests and examiner qualifications o Confounding variables (lurking variables, confounders) – two variables are cofounded if their effects on the dependent variables cannot be distinguished from each other  Design must also generally guarantee the independence of observations – the score of one case does not influence the score of another o Assumed in many statistical techniques that scores are independent (eg. Analysis of variance, ANOVA) o Critical assumption b/c the results of the analysis could be inaccurate if the scores are not independent o No statistical fix or adjustment for lack of independence  Local molar causal validity (internal validity) – emphasizes that o 1) Any causal conclusions may be limited to the particular samples, treatments, outcomes, and settings in a particular investigation o 2) Treatment programs are often complex packages of different elements, all of are simultaneously tested in the study  Three general conditions must be met before one can reasonably infer a cause-effect relation o 1) Temporal precedence: the presumed cause must occur before the presumed effect o 2) Association: There is observed covariation – variation in the presumed cause must be related to that in the presumed effect o 3) Isolation: There are no other plausible alternative explanations of the covariation b/w the presumed cause and presumed effect  Temporal precedence is established in experimental or quasi- experimental designs when treatment begins before outcome is measured o Can be ambiguous in non-experimental designs Measurement  3 Purposes o 1) The identification and definition of variables of interest o 2) An operational definition, which specifies a set of methods or operations that permit the quantification of the construct o 3) Scores, which are the input for the analysis – relatively free of random error  Construct validity is the main focus of measurement o Concerns whether the scores reflect the variables of interest, or what the researcher intended to measure  Requirement for construct validity is score reliability Analysis  3 Main goals o 1) Estimating covariances b/w variables of interest, controlling for the influence of other relevant, measured variables o 2) Estimating the degree of sampling error associated with this covariance o 3) Evaluating the hypotheses in light of the results  Experimental or quasi-experimental designs, the covariance to be estimated is b/w the independent variable of treatment and the dependent variable, controlling for the effects of other independent o Point estimate of a population parameter with a single numerical value  Interval estimation: estimation of the degree of sampling error associated with the covariance o Involves the construction of a confidence interval about a point estimate  Confidence interval: range of values that may include that of the population covariance within a specified level of uncertainty  Conclusion Validity is associated mainly with the analysis o 1) Whether the correct method of analysis was used o 2) Whether the value of the estimated covariance approximates that of the corresponding population value o Might indicate whether a treatment program has been properly implemented  Five Fundamental Things To Know About ANOVAs o 1) Don’t just use it to conduct F-tests o 2) Awkward to include a continuous variable o 3) Restricted case of multiple regression o 4) It permits the distinction b/w random effects (randomly selected by researcher) and fixed effects (intentionally selected by the researcher) o 5) The statistical assumptions of ANOVA are critical (homogeneity of variance)  Analysis of Covariance (ANCOVA) o A covariate is a variable that predicts outcome but is ideally unrelated to the independent variable o Variance explained by a continuous covariate is statistically removed – reducing error variance o Works best in experimental designs where groups were formed by random assignment, and it is critical to meet its statistical requirements (assumptions)  1) Scores on the covariate are highly reliable  2) The relation b/w the covariate and the outcome variable is linear for all groups  3) Homogeneity of regression Internal Validity  The requirement that there should be no other plausible explanation of the results other than the presumed causes measured in your study  Addressed through control of extraneous variables o 1) Direct manipulation o 2) Random assignment (randomization) o 3) Elimination or inclusion of extraneous variables o 4) Statistical control (covariate analysis) o 5) Through rational argument o 6) Analyze reliable scores  In behavioural science direct manipulation is usually accomplished in experimental designs through the random assignment of cases to groups or levels of independent variables that represent conditions, such as treatment versus control  Randomization equates groups o Failure of randomization: when unequal groups are formed  Elimination of an extraneous variable involves converting it to a constant o Inclusion of an extraneous variable involves the direct measurement of such a variable and its addition as a distinct factor in the design  Statistical control o An extraneous variable is directly measured, but it is not explicitly represented as a factor in the design  Rational arguments – made by researchers
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