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Characteristics and Formulation of Research Questions and Hypotheses

This lecture covers the definition of research questions, testable hypotheses, methods to test hypotheses, and the concept of statistical significance. It also explores the characteristics of good research questions and the problems of variables in social science research design.

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Characteristics and Formulation of Research Questions and Hypotheses

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  1. URBDP 591 I Lecture 4: Research Question Objectives • How do we define a research question? • What is a testable hypothesis? • How do we test an hypothesis? • What is statistical significance?

  2. Characteristics of a Good Research Question Feasible Interesting Novel Ethical Relevant

  3. Novel Research Question Confirms or refute previous findings Extends previous findings Provides new findings

  4. Ethical Human Subjects Committee Internal Review Board Human Subjects Review Board

  5. Relevant To scientific knowledge To society/policy/management To future research directions

  6. Early detection of problems inthe study design Adequate number of subjects Comprehensive and detailed theory frame work but impractical Estimation of sample size in order to answer to your questions. Realistic estimate of number of subjects likely to be available. Adequate technical expertise Skills, equipment and experience Affordable in time and money Available resources Manageable in scope Too ambitious, too many measurements, too many questions

  7. Variables What are variables? Properties of objects and events that can take on different values are referred to as variables. Kinds of variables: Discrete variables & Continuous variables Independent, Dependent, & Confounding variables

  8. Problem Variables The fundamental research design problem of social science: lurking variables: a. Lurking variable ("omitted variable"): a variable which has an important effect on the relationship among variables in the study but is not included in the study. b. Confounding variables: two variables whose effects cannot be distinguished from each other

  9. Hypothesis formulation One way of evaluating hypotheses generally is in terms phenomena. 1 Y and X are associated (or, there is an association between Y and X). 2 Y is related to X (or, Y is dependent on X). of the amount of information they provide about 3 As X increases, Y decreases (or, increases in values of X appear to effect reduction in values of Y).

  10. Types of Relation Relation between variables can be described in a variety of ways: • No relationship (Null hypothesis) • Direction of Relation: Positive or Negative · • Magnitude of Relation: Strong, weak, unrelated. · • Causal, Predictive, Association ·

  11. Types of Research Questions • Descriptive: What events are occurring? What are the characteristics of a place, person, organization? How prevalent are certain phenomena? • Explorative:Which characteristics relate to certain phenomena? • Correlative: What are the relationships between variables or phenomena? • Predictive: What will happen if one variable change? • Explanatory: What are the causes of certain phenomena? • Evaluative: How well does a certain process or intervention work?

  12. Sample Research Questions • Descriptive: How prevalent is the use of private car for commuting to work in Seattle? • Explorative: Do households with greater number of cars have higher vehicle miles traveled per capita? • Correlative: Is there a relationship between development patterns and travel behavior measured in per capita vehicles miles traveled ? • Predictive: Will increase in mixed used development reduce per capita vehicle miles traveled by car? • Explanatory: Does gasoline price affects per capita vehicle miles traveled? • Evaluative: Which policy is more effective in reducing per capita vehicles miles traveled by car?

  13. Does increase in mixed used development reduce per capita vehicle miles traveled by car? Dependent Construct Relationship Independent Construct OPERATIONALIZE Development Pattern Vehicle Miles Traveled Causality

  14. Research Hypotheses Hypotheses derived from the researcher’s theory about some social or ecological phenomenon are called research hypotheses. Null hypotheses are, in a sense, the reverse of research hypotheses. They are also statements about the reality of things, except that they serve to refute or deny what is explicitly indicated in a given research hypothesis.

  15. Statistical hypotheses When we transform research hypotheses and null hypotheses into numerical quantities, we have statistical hypotheses. For example, statistical hypotheses concerning the differences in average ages between groups A and B can be represented symbolically as: Ho: Xa = Xb (null hypothesis); Ha: Xa =/= Xb (research hypothesis). If we were to test the research hypothesis that group A is older than group B, we would portray this hypothesis differently symbolically: Ho: Xa < Xb (null hypothesis); Ha: Xa > Xb (research hypothesis).

  16. Testing hypotheses Testing hypotheses means subjecting them to some sort of empirical scrutiny to determine if they are supported or refuted by what the researcher observes. Decision rules usually specify the conditions under which the researcher will decide to refute or support the hypotheses he or she is testing. The decision rules relate to • the level of significance, and • the specification of the sampling distribution.

  17. Hypothesis testing • Step 1: Set up hypothesis • you should determine whether it is 1-tailed or 2-tailed test • Step 2: Compute test statistics • Step 3: Determine p-value of the test statistic • for a pre-determined alpha, you can find the corresponding critical limit • Step 4: Draw conclusion • reject H0 if p-value < alpha (ie greater than the critical limit) • accept H0 if p-value > alpha (ie less than the critical limit)

  18. 1 2 A statistical hypothesis is a statement about the parameters of a probability distribution. Null hypothesisHo:1 = 2 Alternative hypothesisHa:12 • is the mean of a distribution

  19. Level of significance • When a difference in characteristics (e.g., median income, vehicle miles traveled, land value etc. ) between two groups is observed, at what point do we conclude that the difference is a significant one? • To answer this question, we need to look at probability theory which defines the likelihood that one’s observations or results are expected.

  20. Statistical Significance • If we set a probability level or significance level at <0.05 only when the coin comes up heads (or tails) 95 times or more out of 100 throws, then we say the coin is biased. • The difference is statistically significant if the probability of the difference occurring by chance is less than 5 times out of a hundred (i.e., something else other than chance has affected the outcome).

  21. Type I and Type II errors We use the level of significance to help us to decide whether to accept or reject the null hypothesis. • If we reject the null hypothesis when it is true and should not be rejected, we have committed a Type I error. • If we accept the null hypothesis as true when it is false and should be rejected, we have committed a Type II error.

  22. Experiments to control Type I error: Significance test • The P-value calculated in most familiar statistical tests indicates the probability of obtaining a test statistic at least as extreme as the one calculated from the data, if H O were true. • The significance level is a critical value of  --the maximum probability of Type I error (rejecting H O when it is true) that the scientist is willing to tolerate.

  23. Experiments to control Type II error: Power Analysis Power analysis is used to estimate , the probability of Type II error, and its complement, statistical power (1- ), the probability of detecting a specified treatment effect when it is present. Statistical power is a function of several variables: - sample size, N; - variance of the observed quantities; - effect size (the treatment effect the experimenter wants to be able to detect); and -  (the maximum rate of Type I error tolerated).

  24. Confidence Intervals and Power H0 (null hypothesis) true H1 (alternative hypothesis) false H0 (null hypothesis) false H1 (alternative hypothesis) True In Reality We Conclude 1-  (e.g., .95) THE CONFIDENCE LEVEL The probability we say there is no relationship when there is not  (e.g., 20) Type II Error The probability we say there is no relationship when there is one We accept H0 We reject H1  (e.g., .05) Type I Error The probability we say there is a relationship when there is not 1-  (e.g., 80) THE POWER The probability we say there is a relationship when there is one We reject H0 We accept H1

  25. The Four Components to a Statistical Conclusion sample size the number of units (e.g., people) In the study the effect of the treatment or independent variable relative to the noise the odds the observed result is due to chance the odds you’ll observe a treatment effect when it occurs effect size alpha level power

  26. a priori Power Analyses -- r You want to be able to reject H0: if r is as large as .30 pick the power you want probability of rejecting H0: if there is a relationship between the variables in the population (H0: is wrong) .80 is “standard” -- 80% confidence will reject H0: if there’s an effect go to the table look at the column labeled .30 (r = .30) look at the row labeled .80 ( power = .80) you would want S = 82

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