Critical History

Critical histories can be applied to policies, practices, or institutions (among other phenomena). They can be constructed from archival and textual data, and/or from microhistories (a focus on a single person or place). Critical histories require the researcher to apply a critical theoretical framework (such as CRT, feminism, critical democracy, etc.) to analyze the data collected.



Examples:
Gilroy, P. & McNamara, O. (2009). A critical history of research assessment in the UK and its post-1992 impact on education. Journal of Education for Teaching: International Research and Pedagogy, 35(4), 321-335.

Reid, D.K. & Knight, M.G. (2006). Disability justifies exclusion of minority students: A critical history grounded in disability studies. Educational Researcher, 35(6), 18-23.

Levels of Evidence

It's useful to consider the varying levels of evidence when thinking about the strength of research. The levels in the figure below come from the medical literature - and tend to privilege a positivist research perspective. Notice that the methodological design of the research plays a role in its level - and  more generalizable, quantitative results are privileged. This is one perspective, and it is contested, especially in the social sciences!
From Pinto, L., Spares, S. & Driscoll, L. (2012). 95 Strategies for Remodeling Instruction. Thousand Oaks, CA: Sage.

Saturation

Conclusions in qualitative research are drawn from patterns a researcher identifies in the data, or conclusions can uncover conceptual (NOT statistical) relationships. As such, we look for points of saturation to know when we have collected and analyzed enough data – and this can determine the sample size and the point at which analysis should end.

Data saturation is the point where new data and theirsorting only confirm the categories (often numbering between three and six orso), themes, and conclusions already reached. Onwuegbuzie and Leech (2007) also discuss theoretical saturation (Strauss & Corbin, 1990), and informational redundancy (Lincoln & Guba, 1985) as specific areas of saturation. There are various strategies for determining when saturation is reached, but researchers should consider a codebook to track themes and findings.

For more information, see the links above, and

Onwuegbuzie, A., & Leech, N. L. (2007). A Call for Qualitative Power Analyses. Quality & Quantity, 41,105–121. DOI 10.1007/s11135-005-1098-1

Some Sampling Methods Summarized

This is a general overview - please refer to other sources for details. The summary here is based on:

Onwuegbuzie, A., & Leech, N. L. (2007). A Call for Qualitative Power Analyses. Quality & Quantity, 41,105–121. DOI 10.1007/s11135-005-1098-1

 

Random Sampling

Sampling type	Description	Notes
Simple Random Sampling	Participants selected so every person in the population has the same chance of selection, and selection is independent (the selection of 1 doesn’t affect the selection of others). Random means that each person in the population is assigned a number, and the selection is based on a random table of numbers. This is different from the “conversational” use of the term random.	Requires an accurate list of the entire population – so if the population was “science teachers in the GTA,” the researcher would have to have a list of ALL science teachers including contact information, and draw from that
Stratified Random Sampling	Similar to above, but population is divided into homogenous sub-populations (e.g., males and females) and the sub-populations are randomized and selected	See above
Cluster Random Sampling	As above, but groups or clusters are randomly selected	As above
Systematic Random Sampling	As above, but the researcher selects every kth sampling frame member, where k represents the population size divided by the desired sample size	As above
Multi-stage Random Sampling	The researcher samples in two or more stages because either the population is relatively large or its members cannot easily be identified

 

 

Non-Random Sampling

Sampling type	Description	Notes
Purposive Maximum Variation Sampling	A wide range of individuals, groups, or settings is purposively selected so that different and divergent perspectives are represented	The researcher has to have expert knowledge of and access to the population
Purposive Homogeneous Sampling	sampling individuals, groups, or settings because they all possess similar characteristics or attributes
Purposive Critical Case Sampling	individuals, groups, or settings are selected that bring to the fore the phenomenon of interest such that the researcher can learn more about the phenomenon than would have been learned without including these critical cases	As above
Theory-based sampling	individuals, groups, or settings are selected because they help the researcher to develop or expand a theory
Confirming and Disconfirming Case Samples	This is often applied at the end of data collection based on what the individual cases said	Occurs at the end of the research process, in combination with another sampling method
Snowball Sampling	Asking participants who have already been selected for the study to recruit other participants.	Occurs during data collection
Extreme Case Samples	an outlying case or one with more extreme characteristics is studied
Intensity Sampling	researcher studies individuals, groups, or settings that experience the phenomenon intensely but not extremely
Typical Case Sampling	researcher should consult several experts in the field of study in order to obtain a consensus as to what example(s) is typical of the phenomenon	Requires access to “experts” for consensus
Politically Important Sampling	researcher selects informants to be included/excluded because they connect with politically sensitive issue
Random Purposeful Sampling	the researcher chooses cases at random (see above for clarification on the formal definition of “random”) from the sampling frame consisting of a purposefully selected sample. The researcher first obtains a list of individuals of interest for study using one of the 15 other methods of purposeful sampling, and then randomly selects cases
Stratified Purposeful Sampling	As above, but the selection is stratified (see above for the definition of stratified)
Criterion Sampling	individuals, groups, or settings are selected that meet criteria central to the research
Opportunistic Sampling	the researcher capitalizes on opportunities during data collection stage to select cases. Cases could represent typical, negative, critical, or extreme cases
Mixed Purposeful Sampling	mixing of more than one sampling strategy (e.g., one extreme case sample and another critical case sample). Results can be compared to triangulate data
Convenience Sampling	selecting individuals or groups that happen to be available and are willing to participate at the time
Quota Sampling	Cases selected based on specific characteristics and quotas	A main limitation is that only those accessible at the time of selection have a chance of being selected

 

 

 

 

 

How many? Sampling in qualitative research

Onwuegbuzie and Leech (2007) stress that though qualitative research typically relies on small samples, the sample size is important because it determines the extent to which the researcher can make generalizations. Sample sizes in qualitative research should small enough so that the researcher can extract thick, rich data, but also large enough that saturation (data, theoretical saturation, and informational redundancy) are achieved (Lincoln & Guba, 1985; Onwuegbuzie & Leech, 2007).

Mason (2010) cites Guest, Bunce And Johnson ‘s (2006) finding that only 7 sources provided guidelines for qualitative sample sizes. They are:

Source	Methodology	Sample Size
Morse, J.M. (1994). Designing funded qualitative research. In Norman K. Denzin & Yvonna S. Lincoln (Eds.), Handbook of qualitative research (2nd ed., pp.220-35). Thousand Oaks, CA: Sage	Ethnography and ethnoscience	30-50 interviews
Bernard, H.R. (2000). Social research methods. Thousand Oaks, CA: Sage	Ethnography and ethnoscience	30-60 interviews
Creswell, J. (1998). Qualitative inquiry and research design: Choosing among five traditions. Thousand Oaks, CA: Sage.	Grounded theory	20-30 interviews
Morse (1994)	Grounded theory	30-50 interviews
Cresswell (1998)	Phenomenology	5-25 interviews
Morse (1994)	Phenomenology	At least 6 interviews
Bertaux, D. (1981). From the life-history approach to the transformation of sociological practice. In D. Bertaux (Ed.), Biography and society: The life history approach in the social sciences (pp.29-45). London: Sage.	All qualitative	At lease 15

Sources cited:

Guest, G., Bunce, A., & Johnson, L. (2006). "Howmany interviews are enough? An experiment with data saturation andvariability". Field Methods, 18(1), 59-82.

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Beverly Hills, CA: Sage.

Mason, M. (2010). Sample Size and Saturation in PhD Studies Using Qualitative Interviews. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 11(3), Art. 8, http://nbnresolving.de/urn:nbn:de:0114-fqs100387

Onwuegbuzie, A., & Leech, N. L. (2007). Sampling designs in qualitative research: Making the sampling process more public. The Qualitative Report, 12(2), 238-254. Retrieved [Insert date], from http://www.nova.edu/ssss/QR/QR12-2/onwuegbuzie1.pdf