Strengths

The ability to express relationships numericallycan be useful if the researcher doesn’t need toexplore the reasons for behaviour. If you simplywant to know how many murders there are peryear quantitative data satisfies this purpose.

Quantitative data lends itself to statisticalcomparisons. This is useful for things likehypothesis testing or cross-culturalcomparisons (such as crime rates in differentcountries).

Matveev (2002) notes the ability to control theconditions under which data is collected (suchas using standardised questionnaires) makesquantitative data more reliable. Where thesame questions are asked of different groups -or the same group at different times - we canbe sure that any differences in their answersare not down to being asked differentquestions.

The ability to replicate quantitative researchalso means it is likely to be highly reliable andwhere the researcher has no direct, necessaryand personal involvement with the generationof data it makes it less-likely for personal biasesto intrude into the research process.

Kealey and Protheroe (1996), for example,suggest the ability to “eliminate or minimizesubjective judgments” is a major contributoryfactor to increased data reliability.

M9. Quantitative and Qualitative DataQuantitative data involves expressing data statistically or numerically, such as counting thenumber of crimes each year. Such data is usually expressed in one of three ways:

▪ Numbers (sometimes called raw numbers). For example, the total number of people who live inpoverty.▪ Percentages: the number per 100 in a population. For example, 30% of British voters regularlyvote Conservative.▪ Rates: the number per 1000 in a population. For example, if the birth rate in Britain was 1 (it’snot...) for every 1000 people one baby is born each year.

Research data is often expressed as a rate or percentage because it allows accurate comparisonsbetween and within groups and societies. For example, comparing unemployment between Britainand America as a raw number wouldn’t tell us very much, since the population of America isroughly 5 times larger. Expressing unemployment as a percentage or rate on the other handallows us to compare "like with like”.

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Kruger (2003)Longitudinal studies, where the same groupmay be researched at different times to trackchanges in their behaviour, can exploit thiscomparative feature of quantitative data.

Quantitative methods and data “allow us tosummarize vast sources of information andmake comparisons across categories andover time”.

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Limitations

Although the ability to quantify the social worldcan be a significant advantage for socialresearchers one of the major criticisms ofquantification relates to the validity of the datacollected. This can be exampled in a number ofways:

1. In order to quantify behaviour people arefrequently placed in artificial, unrealistic,situations where realism is sacrificed forcontrol. In their everyday lives, for example,people rarely encounter situations wherethey're asked to respond to a list of questionsasked by a stranger. A validity issue here iswhether a researcher can capture people’s“real beliefs” when they place them in anartificial situation.

2. Quantitative methods only capture arelatively narrow range of data about people'sbehaviour - what Day (1998) calls the:

▪ Who?▪ What?▪ When?▪ Where?

3. Following from the above, quantitative datatells us very little about “The Why?” - Thereasons for people’s behaviour. This is partly aproblem of lack of depth: the more complex thebehaviour, the more difficult it is to quantify.

This leads to a further criticism that quantitativemethods focus on relatively superficial aspectswhile failing to address the complexitiesinvolved in even very simple forms ofbehaviour. As Kruger (2003) argues, it’s“difficult to get the real meaning of an issue bylooking at numbers”.

For McCullough (1988) a significantmethodological limitation is the fact “Issues areonly measured if they are known prior to thebeginning of the survey”

In other words, in order to quantify behaviourthe researcher must decide, in advance of theirresearch, what is and what is not significant inrelation to the behaviour being studied.

For this reason there’s little or no opportunity todevelop the research outside of the originalparameters decided by the researcher.

Although, as a general principle, quantitativedata is usually considered both “highly reliable”

and “more reliable” than qualitative data, this isnot necessarily the case: reliability is not anautomatic quality of any one particular researchmethod.

In 2014, for example, the status of Official UKPolice-Recorded Crime Statistics wasdowngraded to “unreliable” by the UK StatisticsAuthority based on "accumulating evidence thatthe underlying data on crimes recorded by thepolice may be unreliable”.Sorokin (1956), partly tongue-in-cheek, coinedthe term “Quantophrenia” to refer to a“psychological compulsion to grasp for thenumeric” - a “condition” that leads to the use ofquantification for its own sake, regardless ofwhether or not it tells us anything useful orinteresting about the behaviour beingquantified.

As Eberstadt (2006) puts it, the “victims” of thiscondition “obsess over numbers as descriptors,no matter how dubious their basis orquestionable their provenance”.

Harvey (2002)“Many apparently quantitative data dependcritically on the way in which they werecollected, who collected them, where theywere collected, when they were collected andfrom whom they were collected”.

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Strengths

Where a research objective is to understandthe meaning of people’s behaviour, they mustbe allowed the scope to talk freely. Qualitativedata encourages this because a researcherdoesn't impose their interpretation on asituation (by asking direct, quantifiable,questions for example).

Qualitative data provides greater depth anddetail about behaviour since, as Day (1998)suggests, they are concerned with discovering“the Why?” - the reasons for such behaviour;because qualitative methods draw-out thecomplex reasons for social behaviour it followsthey are likely to involve digging more deeplyinto people’s beliefs and behaviours.

Qualitative methods avoid the problem of theresearcher pre-judging what is and what is notsignificant data prior to starting their research.Where the research objective is to describe ordraw out people’s opinions and reasons therespondent, rather than the researcher, iseffectively the driving-force: they lead and theresearcher follows. Respondents may talkabout things they see as significant and takethe research into directions and places theresearcher had not originally considered.

Many qualitative methods demand theresearcher establish a close relationship withrespondents (which doesn’t mean they have tolike them, only understand them). Thedevelopment of a research rapport has acouple of advantages:

1. Everyone involved in the research is free tosuggest new ideas and directions - the role ofthe respondent isn’t limited to answeringquestions.

2. Where the atmosphere is more-relaxed andless clinical the researcher is more likely to getrespondents to open-up about their thoughtsand feelings - something that may improveresearch validity.

In this respect although qualitative methodsdon't have a monopoly on validity (any poorly-designed research can lack validity regardlessof the methods used) but when dealing with thecomplexities of human behaviour it is muchmore likely research methods that dig into thiscomplexity will produce highly-valid data: theywill measure what they claim to measure.

Qualitative methods allow researchers greaterfreedom to study people in their “everyday” or“normal” settings and this gives a greaterchance of collecting rich data about whatpeople “really believe” or how they “reallybehave”. As Matveev (2002) suggests,qualitative methods produce data that allow theresearcher to gain a “more realistic feel of theworld that cannot be experienced in thenumerical data and statistical analysis used inquantitative research”.

Qualitative data tries to capture the quality of people’s behaviour and such data says somethingabout how people experience the social world. It can be used to understand the meanings theygive to behaviour.

Boyle (1977), for example, studied the behaviour of a juvenile gang from the viewpoint of itsmembers while Goffman (1961) tried to understand the experiences of patients in an Americanmental institution.

Both were trying to capture and express how people feel about and react to different situations.

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Limitations

Where qualitative research generally focuseson the intensive study of relatively smallgroups, opportunities to generalise researchdata may be limited.

For similar reasons it’s difficult to comparequalitative research across time and spacebecause researchers are unlikely to becomparing "like with like".

Qualitative data also tends to be structured inways that make the research difficult toreplicate - a consequence, Cassell and Symon(1994) argue, of the fact that where researchevolves to take account of the input of differentrespondents the original research objectivesmay change.

In general this means qualitative researchgenerally produce data with lower levels ofreliability. While all data, quantitative as well asqualitative, requires interpretation by theresearcher, qualitative methods - fromparticipant observation to unstructuredinterviews - tend to produce vast amounts ofdata across a wide range of issues. This raisestwo potential reliability issues:

1. Which data to keep and which to discard?

2. Different researchers looking at the samedata may arrive at different conclusions basedon the data they choose to use

Levy (2006), however, argues reliabilityevidenced through the ability to replicateresearch is not a useful test for qualitativeresearch methods. She suggests the concept oftrustworthiness might be a more usefulmeasure of the internal reliability of qualitativemethods: “In qualitative research, as there areno numerical measures. It is up to theresearcher to provide evidence of reliability bycarefully documenting the data collection andanalysis process, hence “trustworthiness” isused to assess how reliable the results are.Can we trust that the results are a ‘true’reflection of our subject?”.

Qualitative methods require different skills fromthe researcher to those required of aquantitative researcher and this meansqualitative data may be harder to collect. Insomething like observational research, forexample, the researcher needs to be able toconvincingly and consistently “play a role”within the group they are studying - a verydifferent set of skills to those needed to delivera questionnaire or structured interview.

Quantitative and Qualitative

Although we’ve considered quantitative and qualitative data as separate entities, there areoccasions when a researcher may want to combine quantitative and qualitative types of data,such as collecting quantitative data about educational achievement or the number of peoplewho visit their doctor each year alongside qualitative data that seeks to explore the satisfactionlevels of pupils or patients.

This technique is called methodological triangulation and is one that can also be used toimprove both research validity - by creating a more-accurate measurement of something - andreliability by using the strengths of one type of data (the ability to quantify behaviour, forexample) to offset the weaknesses of the other.

Alternatively, quantitative data is sometimes collected before starting qualitative research. Aresearcher looking at reasons for school truancy, for example, may firstly carry-out aquantitative analysis to discover whether or not pupils are actually absent from the classroom. Aquantitative enabling study can be used to establish whether or not there is anything for theresearcher to qualitatively investigate…