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PSYC 2360
Mark Fenske

Naturalistic Observation -designed to describe and measure the behavior of people and animals as it occurs in their everyday lives -focus on multiple behaviours in a particular setting and qualitative description and interpretation Advantages: useful for providing insight into real-world behavior, can examine behaviors that cannot be manipulated by researcher Disadvantages: time consuming, potential for subjective interpretation Janie Goodall’s Research -observed and recorded behavior of chimps in Africa, one of first to record tool use in non-humans Types of Observational Research Designs Unacknowledged participant – Roys observations in the raincoat factory: chance to get intimate info from workers, but researcher may change the situation; poses ethical questions Acknowledged participant – Whyte’s study of “street corner society”: ethnically appropriate, but might have been biased by friendships, potential for reactivity Unacknowledged observer – recording the behaviors of people in a small town: limits reactivity problems, but poses ethical questions Acknowledged observer – Pomerantz’s study of children’s social comparison, researchers able to spend entire session coding behaviors, but potential for reactivity since children knew they were being watched Case Studies -individual cases, sometimes studied in naturalistic settings, often brought into clinical setting for in-depth assessment -descriptive records of one or more individual’s experiences and behaviors, often in- depth analysis of a single case -case is interesting because it is unusual, qualitative interpretation of the case Phineas Gage -brain damage left him with change in personality and deficits in reasoning, key indication that specific parts of the brain are associated with specific functions Systematic Observation -observational research and case studies can provide a detailed look at ongoing behavior, but the qualitative nature of the data often not very objective -these concerns can be over using systematic observation -focus on specific behaviors in a narrow context, quantitative -coding system to measure behaviors, categories defined before project begins, based on theoretical predictions Stop Sign Study -McKelvie & Schamer, observed whether males and females made complete stop while driving at night -men equally stopped 40% of time, women stopped 10% of time and 60% of time Sampling Strategies -event sampling: focuses on specific behaviors that are theoretically related to social comparison -individual sampling: randomly selects one person to be the focus of all the observers for an observational period -time sampling: involves each observer focusing on a single participant for a time period before moving on to another participant Archival Research -based on an analysis of any type of existing data sources: statistical records (daily temps, sports records, and crime data), survey archives, and written and mass communications like the newspaper Content Analysis -essentially the same as systematic coding of observational data -includes the specification of coding categories, uses more than one rater Hypothesis Testing – new week Samples and Population -research findings are based on samples drawn from populations -inferential stats allow us to infer what the population is like, based on sample data Two Group Means -research q: are there sex-related differences in alcohol consumption? -ask samples of males and females about number of drinks consumed during last week -results: avg number of drinks consumed males – 2.5 females – 1.3 -is the mean different for girls and boys? If we assume sample means are different does the difference generalize to the entire population and do the population means also differ? Measure of Central Location mean: add all numbers / by amount of categories deviations from the mean: the numbers d1= x1-x, d2=x2-x, etc. are called deviations from the mean sum of squares: the sum is called the sum of squares of deviations from the mean – (equation on page 6 of slides) median: line up numbers from least to greatest – pick middle Measure of Variability (Dispersion, Spread) -range (R = max-min) -variance, standard deviation -sample variance – (equation on page 7 of slides) -the sample SD (equation on page 7) is the square root of the sample variance Interpretations of s -in normal distributions: sd – approx. distance from mean to inflection point on histogram -approximately 68% observations will lie within one SD of the mean, 95% of observations lie within two SDs of the mean Hypothesis Testing (Difference of two means) Sampling Distribution -distribution of all possible values of stats, i.e. mean -sampling distribution of the mean collects samples from the population, calculates the mean for each samples, plot the means – distribution of sample means *as a sample size gets larger shape of the distribution approaches normal distribution -central limit theorem: even if the population is not distributed normally, the sampling distribution will be normal Null Hypothesis -assumes observed data do not differ from what would be expected on the basis of change, h0 Testing: Statistical Significance -to reject null, observed data must deviate more than what would normally be expected under the sampling distribution Probability Value -we assume that all observations come from the same parent population -p is the probability that: an observed difference could have occurred simply by change Judging the plausibility of the null -the sample mean should be plausible, under the sampling distribution of the mean -plausibility of the null is judged by computing the probability p of observing a sample mean that is at least as deviant from the population mean as the value we have observed Alpha (a) -arbitrary threshold at what level of the investigator is willing to discount the role of change as an explanation for an observed group difference Statistical Decisions -when p < alpha, then asset chance could not be the explanation of the observed group difference -this leaves systematic error and the independent variables as the remaining explanations – statistically significant! -when p > alpha, then assert chance could not be ruled out as the explanation of the observed group difference – this is NOT statistically significant! P-Values -two-sided p values: used to test research hypothesis and take into consideration that unusual outcomes may occur more than one way -one-sided p values: can be used in some special cases One-tailed vs. Two-tailed -use a one-tailed test when you have specific reason to believe that the effect will be in a particular direction, and you do not acre if the effect is I the opposite direction, they will always result in smaller p values and hence a greater chance of reaching the significance for your directional hypothesis -decision whether to use one-tailed or two-tailed must be paid prior to data collection Type 1 Error -occurs when we reject the null when it’s true, likelihood is set to alpha .05, 5% is a reasonably low probability of being wrong, but could set lower -saying there is a differenc
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