PSYC 3101 1st EditionExam # 1 Study Guide Chapters: 1 - 6Chapter 1 1.1 Descriptive and Inferential StatisticsStatistics: branch of mathematics used to summarize, analyze, and interpret what we observe.- Two types:1) Descriptive: stats that summarize observations2) Inferential: stats used to interpret the meaning of descriptive statsDescriptive Stats- Specifically used to describe/summarize numeric observations (data)Ex: Suppose we want to study anxiety among college students…o Anxiety=state/feeling of worry/nervousness (this doesn’t describe anxiety numerically)o Anxiety=number of times students fidget during an in-class presentation (this defines anxiety as a number) - Descriptive stats is usually presented graphically/tabularInferential Stats- Procedures used to infer/generalize observations made with samples from the largerpopulation.Ex: Anxiety among college students…o Population=all students enrolled in collegeo Population parameter=characteristic that describes the population (anxiety/fidgeting during n-class presentation)- Disadvantage: population parameters hard to determine because researchers rarely have access to the entire population.o Alternativeget a sample of individuals in the populationo Sample statistic=characteristic that describes the sample (like population parameter but for sample NOT population)- Sample selected from population to learn more about characteristics in the population of interest. - Ex: A local paper says the average college student plays 2 hours of video games per week. You want to test if this is true for your school. You randomly approach 20 fellow students and ask how long (in hours) they play video games per week. You find the average student plays 1 hour per week. o Population=all college students at your schoolo Sample=20 students askedo Sample statistic=1 hour per week of video games1.2 Statistics in ResearchResearch Methods- Three types:1) Experimental (True)2) Quasi-experimental3) Correlational Experimental/True Method- Any study that demonstrates cause- Conditions must be controlled- Three requirements1) Randomization of assignment2) Manipulation of variables and levels of independent variables3) Comparison to control and variablesEx: Measure the effect of distraction on test scoreso Independent variables: distractiono Dependent variables: exam performance (grades)o Randomly assign participants to 2 levels of distraction (low and high)o Compare how students did on exams with a low distraction environment and ahigh distraction environment- Operational definition: dependent variable defined in terms of how it will be measuredo Ex: exam performance is a score between 0 and 100 on a testQuasi-Experimental Method- Lacks randomization, manipulation, or comparison- Most often occurs in one of two ways:1) Includes quasi-independent variable2) Study lacks comparison group- Variables can’t be manipulated which make random assignment impossible (variables preexisting or inherent to participants themselves)Ex: Multitasking ability by gender (gender cannot be randomly assigned)Correlational Method- Can determine whether a relationship exists between variables, but lacks appropriate control needed to demonstrate cause and effect.- 2 variables measured and comparedEx: compare computer use and exercise1.3 Scales of Measurement Scales of measurement: rules that describe properties of numbers.Nominal Scales- Identify something or someoneEx: zip codes, license plate numbers, credit card numbers, country codes, telephonenumbers, social security numbers (These numbers simply identify something, one credit card number is not greater than another)- Categorical variables converted to numerical values (male=1, women=2)Ordinal Scales- Conveys order alone- Indicates that some value is greater than anotherEx: finishing order in a competition, educational eve, rank Ranks do not convey differences, they only indicated that one rank is greater than or less than anotherInterval Scales- 2 defining principles1) Equidistant scales2) No true zeroEx: rating scale (satisfaction on 1-10 scale)- Equidistant scale: distributed in units equidistant from one anotherEx: scale 1-7 is assumed equal distance- Interval scale has no true zeroEx: temperature at zero doesn’t mean no temperature existsRatio ScalesEx: Measure amount of time between mealsAmount of food consumedAmount of time it takes to memorize a list- In each example, behaviors measured with ratio scaleo Draw conclusions in order, differences, and ratioso No restrictions- true zero and equidistant1.4 Types of Data- Two Categories1) Continuous or Discrete2) Quantitative or Qualitative Continuous and discrete variables- Continuous variable: measured along a continuumo Measured at any place beyond the decimal point (can be measured in whole or fractional units.Ex: Olympic sprinters timed to the nearest hundredths place but if judges wanted to clock them to the nearest millionths they could. - Discrete variable: measured in whole units or categories (not distributed along a continuum)Ex: number of siblings you haveQuantitative and qualitative variables- Quantitative variable: varies by amounto Variables measured in numeric unitso Both continuous and discrete can be quantitative- Qualitative variable: varies by classo Variables often labeled in behaviors we observeo Only discrete variables can be qualitativeEx: socioeconomic class (working, upper, middle)Rounding rules:- If over half, round upEx: 1.9875 rounds to 1.99 (round to nearest hundredth)Ex: 3.66666667 rounds to 3.67- If under half, round downEx: 5.67231 rounds to 5.67Ex: 8.56498 rounds to 8.56- If exactly half, round to the nearest even digitEx: 3.48500000000 rounds to 3.48Ex: 3.475 rounds to 3.48Chapter 2 2.1 Why Summarize Data? Suppose you scored a 90% on the 1st exam and you want to know how you did in relation to the rest of the class. The graph below probably wouldn’t help in figuring that out. 90% 80%59% 72%64% 84%77% 87%88% 60%78% 66%94% 78%96% 73%65% 81%79% 55%Instead frequency of scored may show this better:So 90% is extremely good because only 3 other students did as well or better.Frequency: describes number of times/how often a category, or range of scores occurs2.1 Frequency Distributions for Grouped DataFrequency distributions: summarize how often/frequently scores occur in a data set.- Simple frequency distribution: allows us to summarize how often each individual score occurs or how often scores occur in defined