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Generic Lipitor (Atorvastatin, Lipitor® equivalent)
Lipitor is a prescription medication used along with an overall diet plan in order to lower the patient's level of cholesterol and reduce the risk of heart attack. It has been proven to help reduce patients' LDL cholesterol and triglyceride levels significantly, as well as help in maintaining the low levels in the long term. Lipitor belongs to a class of medications known as statins, which work by blocking an enzyme in the liver that is used in the production of LDL ("bad") cholesterol. The body then produces less LDL, and the level of LDL cholesterol in the blood decreases.
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20mg
| Quantity | Price | Price per pill | Returning customer price | Bonus | |
|---|---|---|---|---|---|
| 90 | € 63.19 | € 0.70 | € 56.80 | ---- | Add to cart |
Drug Medical Information
AGE AND BEHAVIOR: RESEARCH METHODS - COHORT AND TIME OF MEASUREMENT CONFOUNDED - TIME-SEQUENTIAL STRATEGY
Experimental Design. This design includes the time lag analysis. The time-sequential experiment asks the same three questions as does the cross-sequential experiment, but the questions refer to different effects. (1) Are the age-cohort groups different? Here, scores B + a are compared to C 4- b, D + c...G-f f (and, if possible, to the scores of an eighth group tested at Time 1 added to g). (2) Are the scores of Time 2 different from those of Time 1? The operations are similar to those of the cross-sequential design: I is compared to II (except that score A is not in the average while the score of the eighth group at Time 1 is). (3) Do scores change more from Time 1 to Time 2 for some groups than others? B — a is compared to C — b, D — c and so on.
Interpretation. Again, taking the study by Schaie and Labouvie as a prototype, it had been assumed in this design that the group difference
(B + a versus C + b, etc.) is an age difference, not cohort. Also, it had been assumed that the Time 1 versus Time 2 scores (or I versus II) is a time of measurement effect, not that of the confounded cohort effect. Thus, with these assumptions, only age and time of measurement are factors—cohort is not regarded as a factor.
Note that here, as in the cross-sequential design, the opposite assumptions could have been made, viz., age (and not cohort) is the relevant variable in cross-sequential comparisons, and cohort (and not age) is relevant in the time-sequential comparisons. Similarly, age (and not time of measurement) is involved in Time 1 versus Time 2 comparisons in cross-sequential designs, and cohort (and not time of measurement effects) in time-sequential designs. A justification for the Schaie and Labouvie assumptions is that only one assumption is necessary in each strategy—age is assumed not a factor in the cross-sequential design and cohort is assumed not a factor in the time-sequential design. Otherwise, more than one assumption in each design would be required and this is less parsimonious. Accepting this justification, it remains true nevertheless that analyzing and comparing the results of cross-sequential and time-sequential analyses tells us nothing more than is implicit in the assumptions that are made. The above assumptions preclude teasing apart age and cohort effects, a main reason for the designs in the first place.
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