Can NCA predict the presence of the outcome?

Can NCA solve the problem of measurement error?

Can NCA handle outliers?

Can NCA prove causality?

Can NCA solve the problem of convenience sampling for statistical inference?

Can NCA predict the presence of the outcome?

No. NCA only can predict the absence of an outcome, not the presence of the outcome. NCA focuses on single conditions that each will prevent the outcome to occur when the conditions is absent or has a low level. Traditional sufficiency methods such as Multiple Regression, Structural Equation Modeling, Partial Least Squares, as well as methods like QCA consider the complex causal structures that produce the outcome. These methods must be used to predict the presence of the outcome from a set of conditions.

Can NCA solve the problem of measurement error?

No. Just like other data analysis approaches NCA presumes that the data to be analysed are valid, reliable and meaningful. If this assumption is not correct the results of the NCA analysis can be flawed. NCA is not sensitive for measurement error of observations (far) below the ceiling line, but sensitive for measurement error of observations around the ceiling line.

Is NCA immune for outliers?

No. Just like other data analysis approaches, NCA may be sensitive for outliers. NCA has a specific outlier analysis approach. In NCA an outlier is defined as case that -if removed from the dataset- has a large influence on the effect size. Two types of outliers exist: ceiling outlliers are cases that define the ceiling and scope outliers that define the scope. If the outlier is caused by measurement error that cannot be corrected or by sampling error because the case does not belong to the theoretical domain, the outliers is removed from the data set. If there is a outlier without known reason the outlier is usually kept in the data set. 

Can NCA prove causality?

No. Just like other data analysis techniques, NCA alone cannot prove causality. It depends largely on the research design and the available theory whether or not it is plausible that the condition is a necessary cause.

Can NCA solve the problem of convenience sampling for statistical inference?

No. Just like other quantitative data analysis approaches NCA presumes that the sample is a probability sample (e.g., random sample) from the population. If this assumption is not true and the sample is not prepresentative for the population, the results of the NCA analysis (and any other data analysis approach for statistical inference) can be flawed.