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# PY 355 Notes- Week 5 PY 355

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This 6 page Class Notes was uploaded by Alexia Acebo on Sunday October 2, 2016. The Class Notes belongs to PY 355 at University of Alabama - Tuscaloosa taught by Craig Walter Cummings in Fall 2016. Since its upload, it has received 2 views. For similar materials see General Experimenta Psychology in Psychology (PSYC),Arts and Sciences at University of Alabama - Tuscaloosa.

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Date Created: 10/02/16

Chapter 5: Selecting Research Participants Sampling: the process of a researcher selecting participants for a study many different ways COMMON MISCONCEPTIONS: o Most beh. Reseach DOES NOT use random sampling o Random= difficult to obtain, don’t always work well Probability Sample: selected sample so that the likelihood that an individual in the population will be selected- can be identified. SIMPLE random sample: most common probability sample; every possible sample of desired size has same chance of being selected. requires sampling frame: list of population from which the sample comes o Prob. Samples= RARE in psychology beh. Research does NOT usually aim to describe how a population behaves If GOAL is to make inferences representative sample: accurate, unbiased estimates of characteristics of population can be drawn. Error of Estimation-degree to which data from sample are expected to differ from population only important when dealing with probability sample o Sampling error: amount individuals selected differ from population o why sample results differ from population FUNCTION OF THREE THINGS: 1. sample size 2. population size 3. variance of data Systematic Sampling: taking every so many individuals for the sample th EX: every 4 person that walks into the room is interviewed -don’t need sampling frame, because you can’t Stratified Random Sampling: population divided into strata, then randomly selected from each stratum: subset of pop. That shares particular characteristic ensures adequate number from each Cluster Sampling: sample groupings/ clusters of participants based on naturally occurring groups, usually based on proximity Multistage Cluster Sampling: 1. divide pop. Into large clusters & randomly smaple clusters 2. sample large cluster 3. sample smaller clusters if needed EX: counties schools classrooms students Nonresponse Problem: failure of participants to respond once selected Ways to prevent: 1. persistence 2. incentives 3. limit time requirement 4. tell people in advance that they will be contacted Misgeneralization: generalized results that differ from population which sample was drawn Nonprobability Sampling: researchers do not know the probability that a particular case will be chosen for sample. o Error of est. cannot be calculated o Most research= this type of sampling o Valid method when the goal is to test hypotheses about how variables relate to behavior than population Convenience Sampling: use whatever participants are readily available -common with college students -may not relate to general population o Quota Sampling: type of convenience sample where researcher ensures that certain types of participants are obtained in specific proportions o Ex equal # of girls/ boys Purposive Sampling: judgement is used to decide which participants to be used in sample; try to get respondents typical of population **IN probability samples, key issue in determining sample size is error of estimation so opt for economic sample: provides reasonably accurate estimate of population @reasonable effort/cost POWER: ability of research design to detect any effects of variables being studied that exist in the data all else equal, larger sample= higher power CHAPTER 6: Descriptive Research designed to describe characteristics/behaviors of a pop. In a systematic/accurate fashion MOST COMMON TYPE= SURVEYS Cross-sectional survey design: sample=cross section of population Successive independent samples survey design: 2+ samples answer same q at different points in time Longitudinal/panel survey design: single sample questioned more than once o dropouts Internet Surveys: inexpensive, no interviewers, no data entry, own time o Little control Demographic Research: describing patterns of basic life events/ experiences like birth, marriage, death etc. Epidemiological Research: studies occurrence of disease in different groups of people Describing and Presenting Data: Good description= accuracy, conciseness, understandability Raw data= not concise, not understandable NUMERICAL METHODS: summarize data by numbers (%, averages) GRAPHICAL METHODS: summarize data in graphical, pictoral groups Frequency Distribution: table summarizing raw data by hsowing # of scores/ category TYPES: Simple Frequency Distribution # of participants who obtained each score o Lowest to highest o Grouped Frequency Distribution- frequency of a subset of scores o Relative frequency: proportion of total number of scores per class interval o Frequency Histograms & polygons o Class intervals on x axis, number of scores per interval on y axis o HISTOGRAM= when variable on X axis is on interval or ratio scale o BAR GRAPH= when variable is on nominal or ordinal scale HISTOGRAM V. BAR GRAPH depends on equal intervals If NO= Bar graph If YES=Histogram o FREQUENCY POLYGON= axes labeled like histogram but lines drawn to connect frequencies of intervals Measures of Central Tendency Mean: mathematical average o MOST COMMON o When presenting a few, researchers present in text. o With several, presented in table or graph. Median: middle score of the distribution Mode: most frequent score Error Bars: above/below means in a graph -indicate confidence in mean value Confidence Interval- usually 95%, range within mean is likely to fall Measures of Variability -variability tells how typical the mean is -if variability is small= good rep. of scores -how much scores vary Range: difference between largest and smallest Variance: takes into account ALL SCORES when calculating variability Standard Deviation: square root of variance. Easier to interpret. Normal Distribution rises to rounded peak @ center, most scores= midrange (-1 to +1) SKEWS Positive Skew: more low scores than high scores Negative Skew: more high scores than low scores Z-Score: describe participant score relative to rest. -indicates how far from the mean -useful w/ outliers

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