07 May WHAT STATISTICAL TEST WOULD BE MOST APPROPRIATE FOR TESTING THE NULL HYPOTHESIS OF NO INTERVENTION EFFECT?
Some researchers claim that exclusive breastfeeding of infants from birth to six months of age can boost a child’s intelligence. Others are skeptical and believe that previous findings to this effect may be attributable to confounding by maternal socioeconomic status. That is, higher socioeconomic status mothers may be more likely to practice exclusive breastfeeding through six months of age; and high material socioeconomic status may contribute to the development of intelligence in the child through mechanisms other than breastfeeding. An investigator studying these issues has a dataset on 1422 mother-child dyads. The dataset contains the following key variables: CHILDIQ, the intelligence of the child as measured via the Stanford-Binet IQ test age age 6 years, with higher scores indicating greater intelligence; BRSTFD, the mother’s self-report of whether or not she breastfed the child exclusively through six months of age (0 = no, 1 = yes); and MOMSES, an index of maternal socioeconomic status derived from information about educational attainment, income, and her own parents’ occupations. The investigator finds that the mean score on Stanford-Binet IQ test was 107.32 among the 454 children who were exclusively breastfed for six months; and 102.55 for the 968 children who were not exclusively breast fed through six months of age. Thus, the average breastfed child had an IQ 4.77 points higher than the average non-breastfed child. To determine the extent to which this difference could attributable to confounding by maternal socioeconomic status rather than to an actual effect of breastfeeding on intelligence, the investigator next runs the following linear regression model:
CHILDIQi = b0 + b1BRSTFDi + b2MOMSESi +ei.
If the difference is due in part to confounding by maternal SES, how would the value of the coefficient b1 in this model likely compare to the raw difference of 4.77?
True or False: A boxplot is a useful way of examining the distribution of a dichotomous variable.
Suppose you have a dataset that includes the variable BMI3CAT as described in question 8, and you wish to create a bar chart that shows how many people fall into the three categories: normal, overweight, and obese. What SPSS syntax would you use to obtain that bar chart?
Suppose you are trying to use linear regression analysis to determine whether the effect of one variable, X1, on another variable, Y, depends upon the value taken by a third variable, X2. What type of term should you include in your regression model?
A curvilinear term
A logistic term
An interaction term
An orthogonal term
None of the above
True or False: A cross-tabulation is a useful way of examining how two categorical variables are related.
Suppose you have obtained from SPSS the correlation matrix in Appendix 1. According to the information in this matrix, which two variables exhibit the strongest linear relationship?
In your own words, what is “confounding” and why is it sometimes a problem in observational studies?
Suppose that your agency has been evaluating an intervention using a posttest-only control group design. There were 155 people in the treatment group and 140 in the control group. The outcome variable is continuous and, according to boxplots, appears to follow a bell-shaped distribution with similar variances in the treatment and control groups. What statistical test would be most appropriate for testing the null hypothesis of no intervention effect?
Suppose your agency has been evaluating an intervention using a one-group pretest-posttest design with 20 participants. The focal variable is continuous but, in looking at the boxplots, you see that it is skewed heavily to the left both before and after the intervention. What statistical test would be most appropriate for testing the null hypothesis of no intervention effect (i.e., no change from before to after the intervention)?
Suppose you wish to include a nominal variable that takes four different values as an independent variable in a logistic regression model. How many dummy variables should you include in your logistic regression model in order to accomplish this?
True or False: ANCOVA is often used to test the null hypothesis of no intervention effect in the context of an impact evaluation using pretest-posttest control-group design with a continuous dependent variable.
Suppose your agency has been evaluating an intervention using a one-group pretest-posttest design with 200 participants. The focal variable is dichotomous. What statistical test would be most appropriate for testing the null hypothesis of no intervention effect (i.e., no change from before to after the intervention)?
A linear regression model with a single dummy variable predicting a continuous dependent variable is equivalent to which of the following statistical tests?
Fisher’s Exact Test
Independent samples t-test (unequal variances version)
Paired t-test
Mann-Whitney U test
Independent samples t-test (equal variances version)
Suppose that your agency has been evaluating an intervention using a posttest-only control group design. There were 95 people in the treatment group and 111 in the control group. The outcome variable takes the following three ordinal values: normal, overweight, and obese. What statistical test would be most appropriate for testing the null hypothesis of no intervention effect?
True or False: When running a two-sample t-test, if the Levene’s test gives a p-value of .023, you should look at the equal variances rather than the unequal variances version of the t-test.
True or False: Before you run a logistic regression model, you must first use a COMPUTE command to enact the log-odds or logit transformation on the dichotomous dependent variable.
Suppose you want to conduct a chi-square test of independence and Fisher’s exact test for the relationship between two dichotomous variables. The first variable, named TREAT, is coded 0 for control group members and 1 for treatment group members. The second variable, named POSTSMOKE, indicates smoking status assessed one month after the intervention being evaluated; it is coded 0 for participants who were not smoking, and 1 for participants who were smoking, at that time. What SPSS syntax would you use to obtain these hypothesis tests?
Suppose you want to conduct a paired t-test to see if post-intervention knowledge scores, KNOWPOST, differ significantly on average from pre-intervention knowledge scores, KNOWPRE, in a dataset containing information on people exposed to the intervention. What SPSS syntax would you use to obtain the paired t-test?
What do you get when you exponentiate a logistic regression coefficient?
A relative risk
A hazard ratio
A quadratic term
An odds ratio
An intercept
Suppose you are want to conduct a Mann-Whitney U test to test for an effect of an intervention on a continuous but highly skewed outcome in a small scale experiment using a posttest-only control group design. The variable TREAT is coded 0 for control group members and 1 for treatment group members. The continuous but skewed outcome variable is a measure of blood glucose; in the dataset it is named GLUCOSE. What SPSS syntax would you use to obtain the Mann-Whitney U test?
Suppose that the analysis of data from an impact evaluation using a posttest-only control group design with a dichotomous outcome variable, SICKPOST, resulted in the SPSS output appearing in Appendix 2. How would you quantify the estimated effect of the intervention in terms of a relative risk, and do the statistical tests provide support for the effectiveness of this intervention?
Suppose that you a part of a team that has been analyzing data from an impact evaluation that a one-group pretest-posttest design. Measures of social support were obtained before and after the intervention using the same instrument. They are continuous. The pre-intervention social support variable appears in the dataset as SSPRE, and the post-intervention appears as SSPOST. A paired t-test was conducted and you have been presented with the output appearing in Appendix 3. How would you quantify the estimated effect of the intervention in terms of a mean difference, and do the statistical tests provide support for the effectiveness of this intervention?
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