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### 67 Cards in this Set

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 What is a Concept? A concept is something that can be quantifiable in some manner What is a construct? A construct is a special concept. However, it is not readily quantifiable. One must use a theoretical framework to decide for operational variables to test for the construct. Ex: Chair is a concept. Comfort of the chair is a construct What scales are used? Nominal ordinal interval ratio What scales are used? Nominal ordinal interval ratio What is a nominal scale Nominal scales are names that can be associated with numbers. It codes a variable into a number. This is the lowest form of a scale, and some places do not recognize this as such. Characteristics of an ordinal scale First real scale magnitude varies along a continuum no math manipulations ex. class rank a track record a simple list of 1st, 2nd, 3rd pitfall: doesn't address distances between magnitudes Interval scale characteristics equal distances on a number line explains equal maginitudes can have linear transformations ex: temp scale - Celcius relative: pull to the right or left, distances remain the same shapes of distribution should remain the same If you see two shapes and want to see if they share the same interval scale, plot out and if in a straight line, they share the scale Each interval scale is also ordinal, and nominal pitfall: doesn't give info about proportions ratio scale characteristics zero is fixed. can't be moved, isn't relative proportion of magnitude is also equal to proportion on number line hierarchal in nature - upgraded interval scale Slope may vary depending upon the equation for transformation. However, it always goes through zero. Kelvin scale is example of temperature as a ratio. What is meta-analysis summary of results over a huge body of studies. You increase the validity of findings IF the study has been conducted properly. Qualitative study Uses Inductive reasoning generalize over separate studies Quantitative Study Hard sciences like Psychology Deductive Reasoning is used Uses more causal relationships (If Then) Major types of Validity Content Validity Criterion Related Validity Construct Validity Three types of Validity Validity in Qualitative Research Validity in Quantitative Research Validity in Measurement Explain Construct Validity this is the degree to which a test measures the theoretical construct or trait it was designed to measure. Based on current theory regardng the trait, the researcher makes predictions about how test scores will behave. The predictions are tested. If data supports prediction, construct validity is enhanced. If data are not supported: 1.the exp. was flawed 2.theory was wrong and should be revised 3. test doesn't measure trait How do you make a prediction support consruct validity? Allow for group differences allow for changes look for correlations Look at processes What factors threaten internal validity? history, maturation, pretest effects, instruments, statistical regression towards the mean, differential selection of participants, mortality, and interactions of factors What are the threats to external validity? Interaction effects of selection biases and treatment, reactive interaction effect of pretesting, reactive effect of experimental procedure, and multiple treatment interference Types of validity in Qualitative research Description Interpretation Theory Also, validity threats: Researcher Bias (researcher's existing theory or preconceptions and the selection of data stand out to researcher.) Reactivity (influence of researcher on the settings or individuals) Validity threats in qual studies Researcher Bias (researcher's existing theory or preconceptions) Reactivity (influence of researcher on the settings or individuals) Checklist of options to reduce validity threats MO approach (hold validity threats in contrast - treat as events) Search for discrepant events and negative cases analyzing these data is key! Triangulation collecting info from a diverse range of individuals and settings Feedback Member checks- feedback from the people you are studying Rich data - "Thich" in that it is detailed and complete enough to reveal the picture of what is going on Quasi-statistics running the simple numeric results that can readily be derived from the data. Comparison Generalization in Qualitative Research Internal generalizability - gen of a conclusion w/in the setting or group External generalizability - not as crucial - face value? 3 steps that make a random design 1. Randomly select participants 2. random assignments of participants to groups 3. random assignemtn of treatments to groups Quasi-experimental design deals with intact groups like classrooms major goal of randomness To provide control in equal conditions that may affect outcome of treatment How to begin randomizing 1. Decide and define population 2. Select a random representative sample. Remember to look at clusters or strata for population. This shrinks image of population. This is called the stratification approach Validity with experimental design Internal validity: Does treatment produce any difference w/in a certain study? External validity: Does treatment make a generalization to a population? Sample group size The more factors you introduce, the smaller the sample group. (think of this as refining a search on the internet!) best random design? Randomized Solomon 4-group design Reliability The degree to whichthe measurement is accurate and precise Types of reliability Internal consistancy- indicates to what degree the test scores reflect the same construct Cronbach's alpha varies between 0-1 0=low reliability 1= high reliability test-retest reliability reliability for stability across time points ex. changes in vision Pearson correlation, r r shows the correlation between x & y value between -1 and +1 want a + score for test/retest content validity 1. face validity (arm chair) 2. logical validity - exam the degree to which items reflect content domain measured by test. all cells proportionally represented at all levels Criterion Related Validity ex: entrance exam test 2 aspects to criterion related validity Predictibility (predictive validity) concurrent construct validity to what degree does measurement measure what it is supposed to measure theoretical frameworks operationalize measurable variables CFA confirmatory factor analysis will allow to test for hypothesized structure of test p-values when alpha is set before testing, the p-value is the actual figure of error found. Thus, use the highest alpha you can afford, but typically is 5%. So: if p=0.023 when an alpha has been set to 0.05, the error is less than 5%. Then we reject the null hypothesis. (Thus saying that the hypothesis is not equal to zero.) Accept the alternate hypothesis. The smaller the alpha, the easier it is to reject or accept the null. Variance problems not all numbers are in the same unit, so must find standard deviation Standard Deviation drawbacks to finding SD: when you square the variance, you're artificially inflating the numbers variance has different units of measure target variable dependent variable outcome Predictor variable Independent variable Variance proportion square the correlation. This answers: What proportion of variance in Y is explained by the variance in x? Variance a magnitude that indicates the spread of scores about the mean. finding variance Ssquared=(x1-meanx)squared+(X2-meanx)squared....divided by n-1 measurement validity how well does the test measure the construct? labeling construct will depend upon your background knowledge. measure of linear relationships correlation between two variables scatterplot design the closer the dots are to a median line, the higher/stronger the relationship finding standard deviation take Ssquared and find the square root covariance if neg, neg correlation if pos, pos correlation is found by taking covariance if neg, neg correlation if pos, pos correlation is found by taking sig p-value semi-partial, or squared part correlation gives proportion of unique contribution ANOVA Analysis of Variance a statistical procedure F A statistic higher F= lower p value=higher chance to reject null zero correlation if line on graph is horizontal Lack of relationship big circle of plots on graph with no linear line Curve linear or quadratic when the line on a graph is curved how to study a curve linear graph Analyze the intervals to form a linear relationship examine meaningful sub groups population coefficient large P with x and y beneath Null hypothesis Ho=zero This means no correlation Alternate hypothesis Ha is not equal to zero Type 1 error to falsely reject the null hypothesis Level of acceptable error 5% Alpha value of acceptable error 0.05 level of significance More on alpha Alpha is the probablility of a type 1 error.