I suggest you to implement all the analysis you cited above and analyze the results choosing the best model on model performance measures, as, for instance, the model $R^2$ value, the AIC (BIC) value, etc.; this should be the proper way to develop a model.
As regards your question particularly, the literature about the topic suggest to develop the model on the basis of the change in percentages of all variables you consider developing the factor model.
In the matter of the transformation of the dependent variable, you should consider the possibility of transforming that in returns to fulfill the assumptions of the linear regression model; indeed, the logarithmic returns are distributed according to the Normal distribution if they are in the range [-0.04; +0.04] according to the Taylor approximation, and it is more likely to produce normally distributed residuals; the values outside this range should be considered as outliers.
The same for independent variable; you should transform those in change in percentage variable, if they're not yet.
Lastly, it is easier to analyze log-log regression model results than level-log or log-level model.
As last hint, I firstly suggest you to develop a model taking into account the linear regression model assumption and testing for the reliability of those hypothesis after running the model (test for homoschedasticity, autocorrelation, multicollinearity,...). Moreover, try to build a reliable dataset; using quarterly data you can have less observation and get biased results. So, if possible, use higher frequency data than yours, as, for instance, monthly, weekly, daily data.
EDIT: since your dataset is pretty small (~96 observations), you should try to get more data to improve your analysis by collecting data previous to the 1990 or later of 2013.