QUALITATIVE
Our qualitative research experts are highly experienced with qualitative analysis software, including NVivo 11, ATLAS.ti, MAXQDA, and Dedoose. As full service dissertation consultants, we help our clients to comprehensively analyse their qualitative data utilizing a number of different methodologies, namely:

Phenomenology: Deriving an understanding of essential meanings as perceived by participants, and constructed through interpretation of their lived experiences.

Case studies: Deriving an understanding of complex processes as they naturally occur within specified bounded systems or groups.

Grounded theory: Constructing a theoretical model that explains phenomena of interest, based on the direct experiences and perspectives of participants.

Ethnography: Developing an in-depth understanding of complex social and/or cultural phenomena within specific settings or groups, through direct immersion in and interaction with the setting or group of interest.

General qualitative inquiry: Developing greater flexibility in terms of sample size and data collection procedures, and can focus solely on interviews or use multiple forms of data to identify categories during the analysis process.

QUANTITATIVE
At Nfojo Research, we can assist you with developing a testing plan and performing your full analysis for each of the below research designs–and we can also help you determine if additional testing is needed to guarantee compelling findings and faster approval. Our expert statisticians are proficient with virtually every statistical method and test across a broad range of statistical software packages, including SPSS, SAS, STATA, R, LISREL/AMOS/EQS, and many others.

Descriptive: Providing a basic summary of the sample and dataset.

Correlational: Employing independent and dependent variables for a more sophisticated research design and analysis.

Causal-comparative: Comparing groups to determine whether an independent variable affects the dependent variable (or outcome) for these groups in terms of effects, causes, and consequences.

Quasi-experimental: Conducting experiments and analysing the collected data (rather than working with a pre-existing set of circumstances, as in the above designs).

Experimental: Using random assignment to determine the experimental and control groups, in order to prevent any other possible factors impacting any differences between the intervention and/or variables being tested.