Psy 202 Exam 1 Study Guide
Psy 202 Exam 1 Study Guide Psy 202
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This 6 page Study Guide was uploaded by Anna Ballard on Monday September 12, 2016. The Study Guide belongs to Psy 202 at University of Mississippi taught by Mervin R Matthew in Fall 2016. Since its upload, it has received 30 views. For similar materials see Elementary Statistics in Psychology at University of Mississippi.
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Date Created: 09/12/16
Fall 2016 PSY 202 Section 4 Exam 1 Study Guide Chapter 1 • Experiments V. Quasi experiments v. observational designs - RESEARCH o Experiments – random assignment, takes people by random and groups them Helps determine cause and effect Too much control –> reduces external validity instead of increasing it o Quasi-Experiments – NOT RANDOM Can see some cause and effect More external validity with decreased control o Observational Studies Does NOT tell cause and effect All external validity All observations of outside world – experimenter has NO CONTROL • Populations v. Samples - Population – dealing with the whole population o GREEK LETTERS - Sample – subset of population o English letters - Definitions o Get data points from different sources; Whole picture –> population size Test a sample of population to generalize back to that population Sample MUST represent a population - Population Parameter –> Greek letters - Sample Statics –> English letters • Representatives - Sampling techniques – simple random, stratified, cluster, systematic, deliberate/purposive, convenience o Simple random – most preferred way because everyone gets equal chance to be chosen May not get same representation between sample and population o Stratified sampling – gives same percentage between the two Better for smaller groups o Cluster sampling – subgroups in a sample –> pull some from that group –> get sample o Systematic sampling – not everyone has an equal chance because every ____th person will be chosen No representation of population A little biased o Deliberate/purpose sampling – sample that compares with another sample; specific population o Convenience sampling – easiest data to collect but harder to generalize to the population Mostly used in college settings • Variables - Independent V. Dependent o Independent (IV) –> does not change; dependent variable relies on independent o Dependent (DV) –> variable that depends on other factors (including IV) Manipulated variable - Qualitative V. Quantitative o Qualitative –> categorical; no fractions; discrete o Quantitative –> numerical; fractioned; continuous - Continuous V. Discrete • Measurement Scales - Nominal – only categorical - Ordinal – categorical and ranking information - Interval – distance, rank, and categorical; zero is just a point of reference - Ratio – rank, categorical, distance, and true zero o True zero – absence of something • Reliability (in general) - General results of an experiment or study - Higher reliability means results are consistent - Lower reliability means inconsistent results - Reliability can give us a clear prediction o Cannot have validity without reliability • Validity (in general) - Measure what you think you are measuring to make a valid conclusion - Ensure validity comes down to operational definitions - Control any variable that may change alongside the IV Chapter 2 • Quantitative Data – Frequency Tables –> organize in descending order and count each group - Simple frequency – shows number of times a piece of data shows up o Include all possible values between high and low (use 0 for values not in table) - Relative frequency – how much is “a lot”? o Must be relative to something o round to 2 decimals o sums to 1.00 o round to 3 when multiplying and dividing - Cumulative frequency – Start with “n” and subtract f(y) as you descend o simple frequency and grouped cumulative should match up at the end o should end with last f(y) - Group Frequency Distributions o Grouping values –> used when there is a large range of data o Groups we are dealing with (intervals) How many intervals How wide should the intervals be? AKA how many values o How many intervals should we have? Between 10 and 20, depending on distance between low and high value o How wide should our intervals be? 2, 3, 5, or a multiple of 5, depending on the number of values used and subsequently, intervals. • Histograms, frequency polygons, ogives, and stem-and-leaf plots - Histograms – simple and relative frequencies o Great graph when there is a lot of data o X-axis –> values of raw scores Values of raw scores in ascending order (either grouped or ungrouped) o Y-axis –> frequencies o Vertical bar for each group (AND TOUCHING) Compares scores for us - Frequency Polygons – similar to histograms o X-axis: still values from lowest to highest o Point for each f(y) value o Connected points suggest values for scores on a continuum Points just outside of range touch the X-axis - Ogives o X-axis still o Y-axis –> cumulative frequencies (simple or relative) Highest cumulative frequency always = 1.00 (relative) or n (simple) Points not necessarily connected by lines - Stem-and-Leaf plot o Get shape and scores only o individual scores o intervals divide evenly into 10 o All group Chapter 3 • Measures of Central Tendency Mode – which score that has highest relative frequency 0 2 2 2 3 4 4 5 7 8 9 - Our class does not like mode because it could be far away from the central tendency - There can also be more than one mode - Mode does not consider anything else in a distribution - Only use mode when you absolutely have to because it only gives us info on category membership - Applies to all data Median – score that has 50% distribution below and above 0 2 2 2 3 4 4 5 7 8 9 - If 2 different scores straddle median… average the 2 - We like this more than mode because it tells us about rank - Missing info: does not give us distance between scores - Better when there are outliers Mean – preferred because it includes the most information (category, rank, and distance) - Incorporates how much the scores weigh - The average of all the scores - CREATES A BALANCE POINT - Most sensitive to outliers n µ = ∑ Yi/n µ –> populi = 1 mean i = 1 –> start with this score n (above ∑) –> finish with this score Y –>raw score; sum all of these scores –> inclusive • Variability More variability –> less confident - Range – Everything between highest score and lowest score (subtract low from high) o super duper sensitive to outliers - Interquartile Range – better to use to “cut off” outliers • Average Deviation vs. variance and Standard Deviation - Average deviation – absolute value bars prevent cancelling out - Variance – squared average deviation also prevents cancelling out - Standard deviation – square root allows data to be closer to original units o Definitional formulas rely on the mean – can get rounding errors and make those rounding errors worse by squaring • Degrees of Freedom – number of categories minus 1 (n-1) - For every parameter we estimate, we lose one degree of freedom Skewness and Kurtosis • Skewness – measure of distribution - Negative distribution – mean closer to negative value - Positive distribution – mean closer to positive value • Kurtosis – measure of curvature - Assumes 0 - Negative distribution is flat compared to positive with high arch Box and Whisker Plots - give us measure of central tendency and measure of variance o measures and medium; lower and higher; range - allows us to see distance Chapter 4 • Frequency tables – Almost always going to be ungrouped - Order does not matter - no cf(Y) because you cannot have scores that are below a certain level • Bar Graph – scores are discrete (bars do not touch) - when dealing with categorical scores there is no skewness or kurtosis • Pie Chart – harder to read visually - good to use when dealing with budget - slices can be in any order ** both pie chart and bar charts are histograms because they only show relative frequency and percentage *** Chapter 5 Expressing the Ordinal Position of a Score • Percentile and percentile rank –> give ordinal only * Percentile Rank : raw scores –> cumulative relative frequency - used more often because of an easier conversion • Percentile : cumulative relative frequency –> raw scores *** Cumulative relative frequency (CRF) is always written in decimal form *** - use that to figure out % at or below your level - CRF = 0.63 –> 63 percentile rank - Always round down to chop off extra decimal places (AKA no fractions @ percentiles) Interpreting Percentile Rank - Can tell you if someone is above or below someone else but not by how much - Aka no distance info! Setting Standard: Normal distribution - How many standard deviations is it above/below mean? - These are standardized (z) scores o Mean always = 0 in distribution scores o Standard deviation always = 1 o (+) –> high magnitude o (-) –> low magnitude
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