The Olinger Group

These methods test for, or identify, underlying dimensional structures in data at various levels of measurement.  R methods estimate dimensional structure in variables measuring attributes, behaviors, and attitudes.  This technique is especially useful for reducing data collection costs while maintaining measurement validity when multiple items are measuring the same thing are fail to discriminate.  It is also a theoretical alternative to ridge regression for reducing bias from multicollinearity in estimating regression models.

• Longitudinal Analysis: The simplest form of longitudinal analysis is the tracking study which measures and reports the same thing at two or more points in time.  With only two points in time, analysis depends on conventional statistical difference tests.  This remains true when data are collected at arbitrary points in time that do not meet the assumptions of time series modeling.
• Cohort Analysis is an aggregate method that describes cases in terms of the time of a common critical event (birth cohorts group individuals based on date of birth).  Cohorts are assumed to share attitudes and behaviors based on this commonality.  Cohort theories assume developmental models in which each cohort manifests similar characteristics as they reach the same time since entry.  Cohorts may also differ from one another in predictable ways based on their experience of differing contexts or events.
• Time Series and Dynamic Modeling are the primary means for modeling longitudinal study measuring change over many different points of time, tends to involve a slightly smaller sample.
• Interrupted Times Series (ITS) expands the time series model to evaluate the impact of interruptions or events, like ad campaigns or the introduction of a competitor, on the time series.  ITS models identify and describe how events impact the time series; whether the impacts are immediate or gradual, persistent or decaying, and their magnitudes.  ITS is especially useful for analyzing quasi-experimental research on naturally occurring interventions.
• Pooled Time Series expands the basic time series models to include a cross section of units of analysis, for example locations or organization.  This increases overall sample size and degrees of freedom, allowing for the estimation of more parameters with desirable properties.  Estimates from the various pooled times series models are robust.
• Panel Analysis temporal analysis that uses many respondents in the sample and only a few points of time measurement.  This is different from time series because it is a cross sectional method requiring a large sample size but with multiple observations in time for each case.  It can provide desirable estimates of change over time.  It allows us to discover change at the micro level with strong internal validity without the data collection requirements of long times series for each case.
• Event History Analysis offers a number of inter-related methods for dealing with qualitative changes reported in time, such as purchase, repurchase, enroll, defect, cancel, and the like.  Real world events data generally involve left or right censoring, and this too can be accounted for during the modeling process.  This means all cases can be analyzed, not just those for which there are complete data or completion of full cycle or process.