It is not an ideal situation if you depend on one coder. The team should have the opportunity to go to and fro the data, the codes and the memos. Too small a number of coders can be counterbalanced by memo writing and by having team sessions both on memos and codes. Memos should also be written on team discussions.

The issue is whether another coder would have applied the same codes in the same way. When you are using what is know as "manifest content" coding, then you have procedures such as inter-rater reliability to ensure that the codes are being used in a reliable fashion.

In contrast to that approach, you often are coding for more "latent content." When you are using this more interpretive framework, there is more subjectivity involved, and inter-rater reliability would not be relevant.

So, the answer to your questions depends on the type of coding you are doing and the purpose your coding is designed to accomplish. 

Depends on what is your paradigm. If you follow feminist methodology, the data is believed to be a co-construction of the researcher and the researched, the data analysis would be coder specific. You cannot have a standardized analysis framework.

Thanx

One specific bias of having only one coder in qualitative data analysis may include the researcher looking to validate a specific theme or themes that were expected; and/or failing or refusin to recognize possible themes from other perspectives.  However, in the write up of such single researcher conducted qualitative research, “trustworthiness” of the study, limitations, and so forth, must be discussed.  Trustworthiness can be promoted by the researcher recording and addressing personal bias throughout the construction of the study. (Including prior to designing the research method, and writing of research questions as well as interview questions, during data collection, (interviews or archives) and during and after analyzing data (each round of coding). However managing biases during coding will be important when discussing the “credibility and dependability” of the study.  Objectivity is key here. There should be a transparent explanation as to how the researcher arrived at the codes they have chosen, and such processes should be described in great detail for readers.  

Taking reflective notes, visiting the data numerous times, over a period of time, can yield different perspectives or insights, and perhaps lessen bias. But- a qualitative researcher is also the tool for extracting qualitative data, especially if interviews are used, and the researcher becomes part of the “construction of knowledge process”. The researchers own knowledge or expertise in the subject area can be both beneficial and a liability; because it helps us in understanding the data, but at the same time, we must be careful to let the data speak for itself (and not make assumptions).

Ensuring saturation or sufficiency of data may aid in making themes more prominent. Triangulation of data with other sources (records, maps, cultural customs) can be useful adjuncts when attempting to evaluate the coded data within its proper context.  When possible, follow-up interviews can be used if needed for clarification from participants. All though there are many things to consider for making a study as strong as possible, within its specific methodological limitations, I would not let a single researcher design deter you. As long as the researcher provides clarity and transparency from beginning to end, (including coding processes) and explains  high-quality research methods used  to readers, valuable information can be yielded from such studies. Best of Luck.