
Internal and External Validity: Internal and External Validity: Links:
Expecially study the examples of internal validity in the Psych 404 page.: Scenarios: Internal and External Validity. Identify the problems of Internal or external validity in each of these cases. Explain just how the problems would affect the results.
Scenario 1: After years of grading his Introductory American Government exams, Professor Casper Curmudgeon discovered a pattern: Students who received low grades (C or less) on his midterm exams consistently improved their scores – by an average of half a letter grade – on their final exams. At first he thought this was due to sample mortality (weaker students dropping out of the course), but the effect remained when he calculated the averages for only for those students who took both tests. He also discovered that students who received good grades (B+ or better) on the midterm had their grades drop half a letter grade on the final. "Grades," Curmudgeon concluded, "are powerful motivating force." Students who received good grades on the midterm, he reasoned, slack off in their studies while those who receive low grades try harder. From then on he never gave a grade higher than a B on his midterms. Scenario 2: In 1980 the National Highway Transportation Safety Administration conducted an experiment to evaluate the effectiveness of a new highmounted rear window brake light on passenger cars. Working with a national rental car agency, they randomly installed rear brake lights on half the agency's fleet of cars. At the end of a twoyear trial it was discovered that the cars with the new lights experienced 35% fewer rearend crashes and 25% fewer fatal accidents than the cars without the lights. If installed on all automobiles, they reasoned, thousands of lives would be saved. Subsequently, all automobile manufacturers were required to install the lights. But after 5 years, when 90% of all automobiles had the lights, the traffic fatality and rear –end collision rates had dropped only 2% (per million miles traveled). What went wrong? Scenario 3: The Normal Police
department instituted an intersection safety program. At the
beginning of the month, the department identifies the intersections with the
most traffic accidents and then implements an intensive police patrol at those
intersections. The new patrol has been an enormous success, said the
police chief, and has resulted in an average reduction of 35% in accidents at
those intersections. Scenario 4: Cutting taxes is not the way to economic prosperity. The 1980 tax reductions instituted under President Reagan led to an immediate recession, rising crime and poverty rates and huge budget deficits. The 1992 tax increases passed under the Clinton administration were followed by eight years of economic growth, steady reductions in crime and poverty, and the first budget surpluses in decades. Scenario 5: Clearly this shows that the death penalty, rather than deterring crime actually causes more of it. Bivariate Analysis.
Multiple Regression:
Multiple Regression Terms.
N = 41 states; ^{ a}p<.01; ^{b}p<.05 NOTE:
RC = scores on NAEP Reading Comprehension Test, fourthgraders; Constant (or intercept)
The estimated
value of the dependent variable when all the independent variable equal zero.
Generally, this is not a meaningful number. Example: not shown in this
table. t (or tstatistic or tratio). The unstandardized coefficient divided by the standard error. The unstandardized coefficient is regarded as significant if it is twice the standard error, if the t is larger than 2. Example: SL books; SL service; and PL all have t statistics greater than 2.
p The probability that the coefficient (either a standardized or unstandardized coefficient or a correlation coefficient) is due to the small number of cases. A p value less than .05 is generally regarded as significant. SL: Software and school expenditures have no significant effect on reading scores.
beta (standardardized
regression coefficient).
What the
correlation coefficient would be if all the variables in the equation were held
constant. Compare this with the correlation coefficient to see if the original
relationship was spurious. Example: SL Servicee has the strongest
independent effect on readings scores; the effect is negative; all other things
being equal, states with more library services have lower reading scores.
*50 state data
N = 41 states; ^{ a}p<.01; ^{b}p<.05
NOTE: RC = scores on NAEP Reading Comprehension Test,
fourthgraders; Stephen D. Krashen, "School Libraries, Public Libraries,
and the NAEP Reading Scores" SLMQ Volume 23, Number 4, Summer 1995
(article online) N= 245 cases (convicted defendants) Table
3. MultipleRegression Analysis
The following results are based on a survey of 350 radio
newspeople working at US commercial radio stations.
_________________________________________ Table 3. Predictors of Radio News Salaries
_________________________________________ Beta r Market size (smaller) .61** .61** Years in news .42** .57** News staff size .35** .60** Years at station .10* .46** Position .08* .07 Education level .05 .06 Gender (being female) .01 .10 Race (being minority) .00 .02 Jobs held .02 .17** Age .00 .40** _________________________________________ N = 350 ** p<.001, * p<.05 and Rsquare = .70 _________________________________________ _________________________________________
Table 1. Predictors of TV News Salaries ________________________________________ Beta r Years in news .54** .54** Position .37** .38** News staff size .30** .40** Age .22** .52** Market rank (smaller) .21** .39** Gender (being female) .07 .15** Race (being minority) .05 .04 Job satisfaction (lower) .04 .15** Education level .02 .06* Jobs held .02 .28** Years at station .00 .37** N = 1,550 (TV newspeople) Betas (regression coefficients) 15 are significant (p<.001); 611, nonsignificant (p>.05). Correlations are significant at p<.01 except Education (p<.05) & Race (nonsignificant at p>.05). _________________________________________
