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Class Notes from Spring 2011 Semester (ENTIRE TEXTBOOK)

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Cathy Mc Farland

Chapter 14- Generalizing Results College Students: Smart (1966)- college students were studied in over70% of the articles published between 1962 and 1964. Unrepresentative subjects, yet easily attainable. Gender Considerations: Researchers use either males or females simply because this is convenient or the procedures seem better suited to either males or females. Gender bias may arise: confounding gender with age or job status and selecting response measures that are gender-stereotyped. Solution= be aware of possible gender differences and include both males and females in research investigations. Locale: Participants in one locale may differ from participants in another locale. Findings obtained in one geographic region may not generalize to people in other regions. Statistical Interaction (Generalization): Generalization is like an interaction in a factorial design- this interaction occurs when a relationship between variables exists under one condition but not under another. Researchers can address generalizability by including the subject types (gender, age, ethnicity) as a variable in the study. Criticism does not mean that results cannot be generalized. Replication of studies acts as a safe guard against limited generalizability. Cultural Considerations: More recently, most samples of college students are ethnically diverse External validity of the research is increased Cultural research attempts to identify similarities and differences that may exist in personality and other psychological characteristics as well as ways that individuals from different cultures respond to the same environments. Broader view of the importance of cultural diversity needed (Miller 1999) Generalizing to other Experimenters: Conductor of the study is the source of a generalization issue-must make sure any influence the experimenter has on the subjects is constant through entirety of the experiment. Characteristics: Experimenters personality, gender, the amount of practice in the role of experimenter. Ex: participants perform better by experimenters of the opposite sex. Solution: use two or more experimenters, both male and female, to conduct the study Pretest Generalization: Limits the ability to generalize to populations that did not receive a pretest, in the real world people are rarely given a pretest. Importance: allows researchers to assess mortality (drop outs) effects when it is likely that some participants will withdraw from the experiment- ability to tell whether the people who withdrew are different from those who completed the study. Solution: Solomon four-group design: half receive a pretest and half receive only the post- test. Laboratory Setting Generalization: Allows experimenters to study the impact of I.V under highly controlled conditions Whereas, in a field study the experimenter examines and manipulates the I.V in a natural setting Solution: conduct experiment under both a field study as well as in a laboratory setting Replication- overcoming any problems of generalization that occur in a single study a) Exact replicationattempts to replicate precisely the procedures of a study to see whether the same results are obtained. Used to build on the findings of a previous study. Mozart Effect- failure to replicate a study b) Conceptual replicationattempts to use different procedures to replicate a research finding. The same I.V is manipulated in a different way. Does the relationship hold when other ways of manipulating or measuring the variable are studied (generalization). Meta-Analysis- researcher combines the actual results of a number of studies. Focuses on effect size, table will show the effect size obtained in a number of studies along with a summary of the average effect size. Allows statistical, quantitative conclusions. Literature Review (1)- summarizes what has been found (2)-tells the reader what findings are strongly supported and what are weakest (3)- points out inconsistent findings and areas where research is lacking (4)- discusses future directions for research. Allows for the possibility of future direction in studies and identifies trends in literature. External Validity- degree to which the results of an experiment may be generalized Chapter 12- Understanding Research Results (Description and Correlation) Scales of Measurement: Mainly all Independent Variables are nominal, right and left-handed individuals are an exception Whenever a variable is studied- there is an operational definition of the variable and there must be two or more levels of variable Four scales of measurement: (1) Nominal, (2) Ordinal, (3) Interval, (4) Ratio Nominal = scale of measurement, categories that have no numerical value Ordinal = measurement categories form a rank along a continuum Interval = intervals between numbers on the scale are all equal in size Ratio = absolute zero present, (physical measures such as weight, or timed measures such as duration or reaction time) Analyzing the Results: Comparing group percentages Correlating individual scores Comparing group means- compare the mean responses of participants in two or more groups Frequency Distributions: Indicates the number of individuals that receive each possible score on the variable Pie charts- divide a whole circle into slices that represent relative percentages. Good for nominal scaled data. Bar graphs- separate and distinct bar for each piece of information. (x= possible responses) (Y=number of individuals who chose each response) Frequency polygons- line to represent frequencies. Histograms- bars to display frequency distribution for a quantitative variable. The values are continuous. Depicts what scores are continuous and most frequent. Descriptive Statistics: Allows researchers to make precise statements about the data. Two numbers: 1 number that describes how participants scored overall, 1 number describes the variability Central Tendency: Mean- obtained by adding all the scores and dividing by the number of scores, overall set of scores. Median- score that divides the group in half Mode- the most frequent score Variability: Standard deviation- indicates the average deviation of scores from the mean Range- simply the difference between the highest score and the lowest score Correlation Coefficient: Statistic that describes how strongly variables are related to one another Pearson product-moment correlation coefficient- (r) 0.00 to +/- 1.00. Closer to +/-1.00 indicates a stronger relation Curvilinear relationship- correlation coefficient will not indicate a curvilinear relationship as it is only used to depict straight lines. Thus, a scatterplot is required, as the [r] value would be represented as 0.00. Effect size: Refers to the strength association between variables Regression Equations: Calculations used to predict a persons score on one variable when that persons score on another variable is already known Y(score to be predicted) = a (constant) + b (weighting adjustment factor) X (slope of the line) Multiple Correlations: Used to combine a number of predictor variables to increase the accuracy of prediction of a given criterion or outcome variable Correlation between predictor variable and single criterion variable
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