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# Study Guide: Exam 1, Ch. 1-4 Psy 202

OleMiss

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This 9 page Study Guide was uploaded by Jessica Crump on Sunday September 11, 2016. The Study Guide belongs to Psy 202 at University of Mississippi taught by Dr. Melinda Redding in Fall 2016. Since its upload, it has received 40 views. For similar materials see Elementary Statistics in Psychology (PSYC) at University of Mississippi.

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Date Created: 09/11/16

Study Guide Exam 1: Chapters 1-4 Chapter 1: Intro to Statistics Statistics – techniques to summarize data to help answer questions; values (usually numerical) that describe a (1) ________________. Parameter – values that describe a (2) ______________. Descriptive Statistics – organizing, summarizing and describing data. E.g. graphs Inferential Statistics – making inferences about a population based on information gathered from a sample. (3) _______________ - characteristics that change May be (a)_____________ – whole numbers May be (b)_____________– values between whole numbers; have real limits (4) _______________ the participants who are measured on the variables. (5) _________________________________ allow researchers to make cause and effect conclusions by manipulating an independent variable and seeing the effect it has on the dependent variable. **requires random assignment E.g. studying improves test grades. Test grades are a dependent variable and study time is the independent. (6) _________________________________ allow researchers to study relationships between variables as they exist naturally. I.e. no experimental manipulation – no independent variable. **Does not show a causal relationship. E.g. murder rates and ice cream sales may have similar correlations, but murders are not affecting ice cream sales. (7) ________________________________ an experiment that is set up like an experimental study but the cases are assigned based on characteristics they already possess. E.g. smoking habits, eye color Explanatory variable – the variable that causes or influences the Outcome variable – the variable that is influenced or comes after the explanatory variable Correlational Experimental Quasi- experimental Explanator Predictor Independent Grouping y variable variable variable variable Outcome Criterion Dependent Dependent variable variable variable variable Levels of Measurement 1. Nominal – naming categories; qualitative data; frequency counts 2. Ordinal – ordered or ranked data 3. Interval – equal distances between numbers on the scale, no meaningful zero point 4. Ratio – equal distances between numbers with a meaningful zero point Identify the level of measurement for the following: 8. GPA ____________________ 9. Military rank ____________________ 10. Temperature ____________________ 11. The alphabet ____________________ 12. Class standing (freshman, sopohomore, etc.) ____________________ 13. Income ____________________ 14. Number of siblings ____________________ 15. Shirt size (Sm, Med, Lg) ____________________ 16. Zip Code ____________________ 17. Age ____________________ Nominal and ordinal variables are always (18) ___________. Chapter 2: Frequency Distributions and Graphs (19) _________ distribution – all raw data in no particular order at all Ungrouped frequency distribution – show data in no particular order; the first column is the scores, the second is the frequencies. Grouped frequency distribution – show data in intervals in the first column and frequencies in the second. **a good example of the difference between the grouped and ungrouped frequency tables is Table 2.1 in your book Cumulative frequency distribution – grouped frequency distribution table with an extra column for summing up each class below it. (20) _____________________ distribution – data ranked in a valued order such as high to low or low to high. (21) _____________________ are used to graph discrete data. (22) _____________________ are used to graph continuous data. **remember, frequency is always on the y-axis (23) _____________________ refers to how many high points in a distribution there are. (24) _____________________ refers to how peaked (leptokurtic) or flat (platykurtic) a distribution is. (25) _____________________ refer to whether a distribution is symmetrical or slanted to one side or another based on its tail. The most common shape is a “normal” curve. It is symmetrical, (26) _______________, and (27) ______________. Label the following with the direction of skewedness (28) _________________________ (29) ________________________ Chapter 3: Measures of Central Tendency and Variability A single value that represents a typical score within a set of scores is called (30) _________________________. - Measured in 3 ways: 1. Mean – add up all the values and divide by the number of values; disadvantage is that it is affected by outliers. 2. Median – cuts a ranked distribution in half, finds the midpoint of data; advantage is that it’s not affected by outliers. 3. Mode – the number that occurs most in a set of data; its always the highest value no matter the skewedness (except for normal distribution because all 3 values are equal) ***In a normal distribution, mode = median = mean** Nominal data is best reported as the (31) ___________. Ordinal data is best reported as the (32) ____________. Interval and ratio data depend on shape, for normal distribution data is best reported as the (33) ________________. For a skewed distribution, the data is best reported as the (34) _________________. The spread of scores within a distribution refers to its (35) __________________. High Variability Low Variability 4 ways to measure variability: 1. Range – simplest measure of variability; highest value – lowest value a. Disadvantage is that it is affected by outliers 2. Interquartile Range – uses only 50% of the scores; highest of the middle of the distribution – lowest of the middle of the distribution a. Often reported as two different numbers 3. Variance – the average of the squared deviations i.e. that average of the squared deviations from the mean; unaffected by outliers, but difficult to interpret because of its squared values. a. Find the deviation score = X - b. Square it c. Sum the squared deviation scores d. Find the mean of the squared scores μ M X−¿ X−¿ ¿ ¿ ¿2 or ¿2 Σ¿ Σ¿ 2 2 σ =¿ s =¿ 4. Standard deviation – tells how much scores deviate from the mean; square root of the variance Chapter 4: Standard Scores, Normal Distribution, and Probability A raw score expressed in terms of how many standard deviations it is from the mean is called (36) ____________________ or z-score. X−μ X−M z= or z= σ s **z-score distribution, or standard normal distribution, has a mean of (37) _______ and standard deviation of (38) ___________. Advantages: 1. Z-scores tell us how far above or below the mean a given score falls 2. Allows for comparisons across unalike distributions 3. Allows one to estimate the probability of an event (corresponds to the percentage) Probability – how likely it is that an event or outcome will occur; the number of ways a specific outcome(s) can occur, divided by the total number of possible outcomes. It’s always positive. Percentile Ranks – tell the percentage of cases whose scores at or (38) _________ a given level in a frequency distribution SYMBOLS: X – outcome variable N – number of cases - summation sign f – frequency - population mean M – sample mean 2 - population standard deviation s - sample standard deviation Answers 1. Sample 18. Discrete 2. Population 19. Raw 3. Variables 20. Ranked a. Discrete 21. Bar graphs (and pie b. Continuous charts) 4. Cases 22. Frequency polygons 5. Experimental study and histograms 6. Correlational study 23. Modality 7. Quasi-experimental study 24. Kurtosis 8. Ratio 25. Skewness 9. Ordinal 26. Unimodal 10. Interval 27. Mesokurtic 11. Ordinal 28. Negative skew 12. Nominal 29. Positive skew 13. Ratio 30. Central Tendency 14. Ratio 31. Mode 15. Nominal 32. Median 16. Nominal 17. Ratio 33. Mean – although it’s 35. Variability really pretty unimportant 36. Standard score because they are all equal 37. Zero 34. Median – because it’s 38. One not influenced by outliers 39. Below

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