6/20/18. Prac+cum #1 Wednesday 6/27. Lecture Ques+on. Quick review. Establishing Causality. Causality

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6//8 Prac+cum # Wednesday 6/7 Agenda I. II. III. Reliability and Validity IV.

points each) mul+ple choice ques6ons (3 points each) PART B (Computer 8 points) 3

Establishing When we test a hypothesis, we are ayemp6ng to establish causality.

1 6//8 Prac+cum # Wednesday 6/7 Agenda I. II. III. Reliability and Validity IV.Introduc6on to SPSS PART A (Paper-and-pencil 8 points) 3 short answer ques6ons (6 points each) mul+ple choice ques6ons (3 points each) PART B (Computer 8 points) 3 short answer ques6ons (6 points each) Oﬃce Hours TuW :5-:5p Week, Day 3 Quick review Lecture Ques+on What is science? The process of research entails following the scien6ﬁc method, which includes asking ques6ons and developing hypotheses. By what standards do we test hypotheses in poli6cal science? Establishing When we test a hypothesis, we are ayemp6ng to establish causality. How do we establish causality in a rela+onship?. Your rela6onship needs to make sense: causal mechanism. A confounding variable should not aﬀect the rela6onship. 3. Your rela6onship should not be endogenous.. Your factors should co-vary.

6//8 Homicides per month (every, person) Ice Cream & Crime Causa+on and Correla+on 8 6 8 6,, 6, 8,,,, Ice cream sales per month An example of a spurious variable!

Homicide rates Temperature is the process by which we measure the ideas in our hypothesis.

2 6//8 Homicides per month (every, person) Ice Cream & Crime Causa+on and Correla+on ,, 6, 8,,,, Ice cream sales per month An example of a spurious variable! X Ice cream causes an increase in crime rates. Y Z Ice cream sales Establishing causality is at the heart of scien6ﬁc research. Homicide rates Temperature is the process by which we measure the ideas in our hypothesis. Concept: Broader ideas In tes6ng a encompassed in your hypothesis, we move hypothesis. from concepts to Measurement: The measurements; this assignment of values to is also known as outcomes following a set of opera+onaliza+on. rules. When our concepts in poli6cal science deal with behavior, how do we opera6onalize them?

3 6//8 Example of Hypothesis: A sense of increased poli6cal efficacy leads to increased levels of poli6cal par6cipa6on. What are our concepts in these hypothesis? Poli+cal Efficacy Independent variable People like me don t have any say about what government does. Poli+cal Par+cipa+on Dependent variable How ofen have you ayended a poli6cal rally in the last year? Levels of Measurement Levels of Measurement: The par6cular levels at which we measure our outcomes; a classifica6on system Nominal Ordinal Interval Ra+o Least precise Ordinal Interval Most precise Dataset gender major classstanding midterm Nominal Variables Defined by the characteris6cs of an outcome that fit into one and only one class of category. The variable is measured in categories; non-rankable, nonordered intervals Examples: gender, race, marital status, major gender Ordinal Variables Defined by the characteris6cs of an outcome that are able to be ordered. The variable is measured in categories; rankable and ordered intervals Examples: party ideology, income (in categories), class standing classstanding 3 Interval-ra+o variables Defined by the ability of being able to describe an underlying con6nuum such that we can talk the measureable difference between a high or low performance. Some6mes with an absolute zero value. The variable is measured in values and their scales are equal to one another, without an absolute zero point. Examples: temperature, GDP, percentages, test scores 3

6//8 Also: Dichotomous or Binary Variables gender Variables that

which should detail how your variables are measured.

consistently tap into the same concept over and over.

ideas. Internal consistency Tes6ng diﬀerent items to reliability.

4 6//8 Also: Dichotomous or Binary Variables gender Variables that are only measured using and as values. Most ofen treated as nominal variables. Typically, a dataset will also be accompanied by a codebook, which should detail how your variables are measured. Reliability Codebook Whether or not a measurement can consistently tap into the same concept over and over. is important to tes6ng a hypothesis and will come into play ofen in this course. Tes+ng Reliability Re-tes+ng a measure at two diﬀerent +me periods. Parallel forms reliability. Tes6ng two diﬀerent instruments or forms to get at the same ideas. Internal consistency Tes6ng diﬀerent items to reliability. get at the same concept. Inter-coder reliability. Tes6ng two raters in their ability to come to similar es6mates. Validity Test-retest reliability. The property of an assessment tool that indicates that the tool does what it says it does.

6//8 Tes+ng Validity Face Validity. Content Validity.

Developing measures requires them to be both

5 6//8 Tes+ng Validity Face Validity. Content Validity. Criterion Validity. Construct Validity. Is this measure valid on its face? Tes6ng the validity of a number of measures in the same universe. Tes6ng a new measure in comparison to an established measure. Tes6ng a higher-order concept. Why do we care? Developing measures requires them to be both reliable and valid. Introduc+on to SPSS Spreadsheet programs SPSS is a sta6s6cal sofware program that allows users to analyze quan6ta6ve data. Cell Row (case) Column (variables) 5

In this module we will cover Correla4on and Validity.

In this module we will cover Correla4on and Validity. A correla4on coefficient is a sta4s4c that is o:en used as an es4mate of measurement, such as validity and reliability. You will learn the strength