PSYB07: Data Analysis in Psychology MIDTERM REVIEW Chapter 1 ...

PSYB07: Data Analysis in Psychology MIDTERM REVIEW Chapter 1 – Basic Concepts KEY TERMS:  Random sample (1.1)  Random assignment (1.1)

 Population (1.1)

 Sample (1.1)

 External validity (1.1)

 Internal validity (1.1)

 Variable (1.1)

 Independent variable (1.1)

 Dependent variable (1.1)

 Discrete variables (1.1)

 Continuous variables (1.1)

 Quantitative data (1.1)

 Measurement data (1.1)

 Categorical data (1.1)

 Frequency data (1.1)

 Qualitative data (1.1)

 Descriptive statistics (1.2)

 Inferential statistics (1.2)  Parameter (1.2)

 Statistic (1.2)

 Nominal scale (1.3)

 Ordinal scale (1.3)

 Interval scale (1.3)

 Ratio scale (1.3)

PRACTICE QUESTIONS:  Under what conditions would the entire student body of your college or university be considered a population  Under what conditions would the entire student body be considered a sample?  If the student body of your college or university were considered a sample, would this sample be random or non-random? Why?  Why would choosing names from a local telephone book not produce a random sample of the residents of that city? Who would be underrepresented and who would be overrepresented?  Give two examples of independent variables and dependent variables.  Give three examples of continuous variables. Give three examples of discrete variables.  Give an example of a study in which we are interested in estimating the average score of a population.  Give three examples of categorical data. Give three examples of measurement data.

 Give one example of each kind of measurement scale.

Chapter 2 – Describing and Exploring Data  Frequency distribution (2.1)  Histogram (2.2)

 Midpoints (2.2)

 Outlier (2.2)

 Stem-and-leaf display (2.4)

 Stem (2.4)

 Leaves (2.4)

 Symmetric (2.5)

 Bimodal (2.5)

 Unimodal (2.5)

 Modality (2.5)

 Negatively skewed (2.5)

 Positively skewed (2.5)

 Skewness (2.5)

 Kurtosis (2.5)

 Mesokurtic (2.5)

 Platykurtic (2.5)

 Leptokurtic (2.5)

 Sigma (2.6)

 Measures of central tendency (2.7)

 Measures of location (2.7)

 Mode (Mo) (2.7)

 Median (Mdn) (2.7)

 Median location (2.7)

 Mean (2.7)

 Range (2.8)

 Interquartile range (2.8)

 First quartile, Q1 (2.8) Third quartile, Q3 (2.8) Second quartile, Q2 (2.8)

 Sample variance (2.8)

 Population variance (2.8)

 Standard deviation (s) (2.8)

 Coefficient of variation (CV) (2.8)

 Unbiased estimator (2.8)

 Degrees of freedom (df) (2.8)

 Boxplots (2.9)

 Box-and-whisker plots (2.9)

 Quartile location (2.9)

 Inner fence (2.9)

 Whiskers (2.9)

 Standardization (2.12)

PRACTICE QUESTIONS:  Any of you who have listened to children tell stories will recognize that children differ from adults in that they tend to recall stories as a sequence of actions rather than as an overall plot. Their descriptions of a movie are filled with the phrase “and then. . . .” An experimenter with supreme patience asked 50 children to tell her about a given movie. Among other variables, she counted the number of “and then. . .” statements, which is the dependent variable. The data follows: 18 15 22 19 18 17 18 20 17 12 16 16 17 21 23 18 20 21 20 20 15 18 17 19 20 23 22 10 17 19 19 21 20 18 18 24 11 19 31 16 17 15 19 20 18 18 40 18 19 16 a. Plot an ungrouped frequency distribution for these data.

b. What is the general shape of the distribution?

Chapter 3 – Nominal Distribution  Normal distribution

 Standard normal distribution (3.2)

 Deviation score (3.2)

 z score (3.2)

 Standard scores (3.6)

 Percentile (3.6) PRACTICE QUESTIONS:  Assume that the following data represent a population with mew = 4 and omega = 1.63: X = [1 2 2 3 3 3 4 4 4 4 5 5 5 6 6 7] a. Plot the distribution as given.

b. Convert the distribution in part (a) to a distribution of X2m.

c. Go the next step and convert the distribution in part (b) to a distribution of z.

 Suppose we want to study the errors found in the performance of a simple task. We ask a large number of judges to report the number of people seen entering a major department store in one morning. Some judges will miss some people, and some will count others twice, so we don’t expect everyone to agree. Suppose we find that the mean number of shoppers reported is 975 with a standard deviation of 15. Assume that the distribution of counts is normal. a. What percentage of the counts will lie between 960 and 990?

b. What percentage of the counts will lie below 975?

c. What percentage of the counts will lie below 990?

 A dean must distribute salary raises to her faculty for the next year. She has decided that the mean raise is to be $2000, the standard deviation of raises is to be $400, and the distribution is to be normal. a. The most productive 10% of the faculty will have a raise equal to or greater than $_________ . b. The 5% of the faculty who have done nothing useful in years will receive no more than $ ____________ each. Chapter 4 – Sampling Distributions and Hypothesis Testing

 Sampling error (Introduction)

 Hypothesis testing (4.1)

 Sampling distributions (4.2)

 Standard error (4.2)

 Research hypothesis (4.3)

 Null hypothesis (H0) (4.3)

 Alternative hypothesis (H1) (4.4)

 Sample statistics (4.5)

 Type I error (4.7)

 a (alpha) (4.7)

 Type II error (4.7)

 b (beta) (4.7)

 Power (4.7)  One-tailed test (directional test) (4.8)

 Two-tailed test (nondirectional test) (4.8)

 Conditional probabilities (4.9)

PRACTICE QUESTIONS:  Suppose I told you that last night’s NHL hockey game resulted in a score of 26–13. You would probably decide that I had misread the paper and was discussing something other than a hockey score. In effect, you have just tested and rejected a null hypothesis. o What was the null hypothesis?

o Outline the hypothesis-testing procedure that you have just applied.

 Define “sampling error.”

 What is the difference between a “distribution” and a “sampling distribution”?

 Give two examples of research hypotheses and state the corresponding null hypotheses.

Chapter 5 – Basic Concepts of Probability  Analytic view (5.1)

 Frequentist view (5.1)

 Subjective probability (5.1)

 Event (5.2)

 Independent events (5.2)

 Mutually exclusive (5.2)

 Exhaustive (5.2)

 Additive law of probability (5.2)

 Multiplicative law of probability (5.2)

 Joint probability (5.2)  Conditional probability (5.2)

 Unconditional probability (5.2)

 Factorial (5.6)

 Binomial distribution (5.8)

 Bernoulli trial (5.8)

PRACTICE QUESTIONS:  Give an example of an analytic, a relative-frequency, and a subjective view of probability.

 Give two examples of discrete variables.