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Intro to SPSS Lab 3 Kenny Chen MIDTERM REVIEW! Hypothesis Template In a comparison of (unit of analysis), those who are (one part of independent variable) are more likely to (dependent variable) than those who are (other part of independent variable). Eg. In a comparison of individuals, those who are women are more likely to have higher annual income than those who are men. Hypothesis formulation exercise • Gun leads to increased rates of homicide. • Some states have stricter gun laws than other states. • We are curious whether or not we are able to observe lower homicide rates in states with strict gun laws. How would you hypothesize this relationship using our template? Null and Alternative Hypothesis • Alternative Hypothesis (H1): what researchers interested in showing to be true (aka ‘research hypothesis’). There is a relationship between the two variables of interest. • Null Hypothesis (H0): some form of ‘nothing happening. There is no relationship between the two variables in the population H1, H0 Examples Example: Aspirin and Heart Attacks • Null Hypothesis: There is no relationship between taking aspirin and risk of heart attack in the population. • Alternative Hypothesis: There is a relationship between taking aspirin and risk of heart attack in the population. Philosophy of Science Don’t assume it’s unimportant • Popper’s scientific revolution: deductive method? inductive method? What are they and which one did Popper support and why? 1. All men are mortal. Harold is a man. Therefore, Harold is mortal 2. Harold is a grandfather. Harold is bald. Therefore, all grandfathers are bald • Compare and contrast quantitative and qualitative methods (Mahoney & Goertz) Levels of Measurement • Nominal variable: gender, party id • Ordinal variable: income range, ranks, agreement • Interval variable: income by dollar • Ration Variable (absolute zero): height, weight DV and IV • DV (outcome variable):The presumed effect in an experimental study. The values of the dependent variable depend upon another variable, the independent variable. • IV(explanatory variable): The presumed cause in an experimental study. All other variables that may impact the dependent variable are controlled. The values of the independent variable are under experimenter control. Measures of central tendency Range= Upper bound-lower bound Frequency Normal distribution: mean=median=mode (bell curve) • Central limit theorem: with some conditions, if you draw a large enough random independent variables, the mean will look like a normal distribution. • • • • Skewness and Kurtosis Sample vs Population Capitalization In general, capital letters refer to population attributes (i.e., parameters); and lower-case letters refer to sample attributes (i.e., statistics). For example, P refers to a population proportion; and p, to a sample proportion. X refers to a set of population elements; and x, to a set of sample elements. N refers to population size; and n, to sample size. Population Parameters By convention, specific symbols represent certain population parameters. For example, μ refers to a population mean. σ refers to the standard deviation of a population. σ2 refers to the variance of a population. ρ is the population correlation coefficient, based on all of the elements from a population. N is the number of elements in a population. Sample Statistics By convention, specific symbols represent certain sample statistics. For example, x^bar refers to a sample mean. s refers to the standard deviation of a sample. s2 refers to the variance of a sample. r is the sample correlation coefficient, based on all of the elements from a sample. n is the number of elements in a sample. SD and Z-score SD • a “standard” way of knowing what’s normal amongst a bunch of data. • SD=sqrt(variance) • From our dog example, we can statistically define what type of dog is particularly “Large” or “Small” based on SD. Z-score • Essentially, we quantify how many SDs from the mean of particular value is. • The number of SDs a data value is above/below the mean is called the Z score. • Z score=(data value-mean)/SD Problem Set 2: quick review • Please submit your work in a SINGLE document! • No percentages in crosstab: Fine for now, but not in the future, as your interpretation may likely to be wrong! • Interpretation is more difficult than running SPSS! Q1 • NES2012=> libcon3+gender (descriptive statistics)=>form a hypothesis based on the template=>cross tab (gender=IV; libcom3=DV) Q1 Solution • Open NES2012 • Analyze=>Descriptive Statistics=>Frequencies=>gender, libcon3 into variable list=>Statistics=>skewness, kurtosis, mean, median, mode, min, max..=> Continue=>OK • Interpretation? • Hypothesis? • Analyze=>Descriptive Statistics=>Cross tabs=>gender in column, libcon3 in row=>cell=>Column percentages=>continue=>OK • Interpretation? Q2 • World=> polity+gdp_cap3 (descriptive statistics)=>form a hypothesis based on the template=>mean comparison(gdp_cap3=IV; polity=DV) Q2 Solution • Open World.sav • Analyze=>Descriptive Statistics=>Frequencies=>polity, gdp_cap3 into variable list=>Statistics=>skewness, kurtosis, mean, median, mode, min, max..=> Continue=>OK • Interpretation? • Hypothesis? • Analyze=>Compare Means=>Means=>gdp_cap3 as IV polity as DV=>OK • Interpretation: The mean polity score of countries that fall under low GDP is 2.90.