Quantitative Qualitative fundamental Web viewQuantitative /Qualitative research fundamental ... after admission to the study, ... specific for measuring fatigue in the MG [Myasthenia

Introduction to Quantitative research and data management

Robin Beaumont [email protected]

02/01/2012

Quantitative Research

Contents

1. Quantitative research 3

2. Theory, Methods and Tools 5

2.1 Methods 5

2.2 Operationalisation 6

2.3 Sample size 8

2.4 Tools 8

3. Data 9

Exercise 4. 9

3.1 The Statisticians View of Data 9

Exercise 5. 10

3.2 The Social Scientists View of Data 11

Exercise 6. 11

Exercise 7. 12

3.3 Concepts 12

Exercise 8. 13

4. Hierarchy of datatypes 14

5. Ranking Data 15

5.1 Magnitude and Ranking 15

5.2 Rating Scales 15

6. Collecting and organising your data 16

7. Multiple Choice Questions (MCQs) 17

8. Summary 18

9. References 19

11. FAQs (Frequently asked Questions) 21

11.1 If you repeat the same research what does it mean? 21

11.2 Qualitative and quantitative research ask different questions - wrong 21

11.3 Qualitative research does not use hypotheses - wrong 21

11.4 Qualitative research is high in validity whereas quantitative research is high in reliability - wrong 22

11.5 You can combine the two philosophical approaches - not really 22

11.6 There is one hierarchy of Evidence - no 22

11.7 The basis of knowledge is belief or the scientific method! 24

11.8 Quantum theory validates the world 2 viewpoint - no! 24

12. References 25

Robin Beaumont [email protected] /tt/file_convert/5a7073667f8b9ac0538c0272/document.docx page 2


1. The elements of Quantitative researchThere are many ways of carrying out research and depending upon your discipline the methods and tools you use will vary greatly. In the scientific disciplines a quantitative approach is usually adopted (see the appendix to this chapter for a description of the philosophical aspects to this approach.)

All quantitative research has the following elements:

Experimenter / Investigator / ResearcherSubject / Client / Volunteer / Cohort / Group / Sample Data (Primary or Secondary data) Measuring Instrument (e.g. thermometer, ergometer, ECG, questionnaire)

In addition the following may also be present:

Treatment(s) / Intervention / Test group(s)Controls / placebo / comparator

Depending upon the degree of rigor required the above basic elements are developed in differing ways, for example knowing the effectiveness of the treatment, having based our judgement on historical results, will dictate the number of subjects required in subsequent studies to obtain valid results.

An actual example will help demonstrate some of these elements.




The UK Medical Research Council (MRC) randomised trial of Streptomycin for TB 1949-1950

Streptomycin was a antibiotic which demonstrated some effectiveness in the treatment of TB in a small group of patients. Subsequently the committee decided that there should be a series of multi-centre trials at tuberculosis units throughout the UK. The initial trials involved patients with the most serious forms of the disease - miliary and meningitic (both previously almost uniformly fatal), and very advanced pulmonary tuberculosis (with a high but not uniform mortality). Any recovery of patients with miliary or meningitic tuberculosis proved convincingly the worth of the new drug in those conditions (MRC 1948b). In contrast, patients often recovered from pulmonary tuberculosis, even from very advanced disease.

It was decided to limit the patients participating in the trial to those aged between 15 and 30 with "acute progressive bilateral pulmonary tuberculosis of presumably recent origin, bacteriologically proved ...". Both the streptomycin and control group would receive the standard treatment for this type of disease - bed rest. As there were more patients with pulmonary tuberculosis than there were hospital or sanatorium beds to accommodate them, those allocated to bed rest alone in the trial received priority for admission. If streptomycin proved valuable these patients would receive it later, when supplies improved. Meanwhile, they would avoid any unknown ill effects of the new drug.

When a consultant physician identified a potentially eligible patient, the patient’s details were sent to Marc Daniels at the national coordinating centre for the trial. If the patient was judged to meet the eligibility criteria, admission was arranged to the next available hospital bed in the nearest participating centre. Each gender in each centre was allotted a numbered series of envelopes, bearing only the name of the hospital. Each envelope contained a card indicating ‘S(treptomycin)’ or ‘C(ontrol)’. The numerical order of the envelopes was based on a series of random numbers. When a patient was approved for the trial the next envelope for that centre and for gender was opened. Streptomycin and control patients were usually admitted to different wards but otherwise treated exactly the same.

Neither group of patients knew that they were in a trial, which remained confidential throughout its 15 month duration. Progress was assessed with monthly chest X-rays, graded by three specialists who remained ignorant of (blind to) the identities of the allocation of patients to streptomycin with bed rest or bed rest alone. Any difference of opinion, usually slight, was resolved by discussion. Monthly direct smear and culture of sputum was also reported by bacteriologists who also remained blind to the treatment group. Fever, weight and sedimentation rates were also recorded regularly.

The results of the trial showed that streptomycin was helpful. During the first 6 months after admission to the study, there were 4 deaths among 55 patients who had been allocated streptomycin, compared with 15 among 52 patients allocated to bed rest alone, and this difference was reflected in radiological and other improvements. During the subsequent 6 months, the radiological and mortality differences were less marked (there were 8 more deaths in the streptomycin group and 9 more in the groups treated with bed rest alone).

http://www.jameslindlibrary.org/illustrating/records/streptomycin-treatment-of-tuberculous-meningitis/key_passages

Theory

Methods Tools


2. Theory, Methods and ToolsThe research method (the process), the Tools used and the philosophical foundations (World View) are distinct but related aspects of any quantitative research so let's consider each of these separately.

2.1 MethodsThe overriding aim of quantitative research is to achieve the highest possible level of Objectivity, which is achieved using the following:

achieved by minimising the dangerous possibility of by using

Blinding techniques subject /researcher interaction along with the following methods:

standardisation would be the aimed for and achieved by appropriate Sampling, Randomisation, Grouping and scale development (all forming part of the research protocol). You would aim for a method that would allow replication.Comparisons - the design would usually include one or more comparator group(s). Such comparator groups are usually real that is a control, placebo or alternative intervention(s) but might be theoretical that is statistically defined. Measurement - taking measurement would allow the development of hypotheses estimating the values of possible variables (relating to the objective real world). You would define objective valid reliable measures by a process of operationalisation producing validated standardised scales the result of which would be data

consisting of numbers. A specific sample size will be stated to ensure a level of certainty for any measure obtained. The data would be interpreted using formal statistical theory involving probability theory and making inferences from your sample to a defined population (the process of generalisation). Interpretation - The possibility of Individual interpretation of the



results would be minimised. Others following the study protocol and analysing the data would come to the same conclusions. Documentation - you would produce a protocol defining the process, again to aid replication, and also report your results in a standard way using validated techniques and layout.

Another way of looking at the above description is to consider the world view 1 gold standard research approach, the RCT (Randomised Control Trial), which can be expressed in the form of a flow diagram (Schulz, Altman & Moher 2010)

Sampling - we attempt to ensure that our sample is a representative sample (usually random) from some theoretical population we are interested in generalising our findings to by using mathematical procedures (inferential statistics).

In quantitative research a representative sample is essential for a large number of statistical procedures to produce valid results and if it is not the results might simply be garbage. Depending upon the research design various eligibility criteria might be applied to allow more control (i.e. a more homogenous sample), for example in the previous discussion concerning power the researcher selected, newly diagnosed hypertensive males (aged between 50 to 55 yrs).

Random allocation - A second level of control is achieved by Randomisation which usually means random allocation to a particular group. This ensures that each subject entering the trial has equal chances of being in any of the treatment groups. One group may be a placebo or control group which is another important aspect as it allows the measurement or any effect/change over and about that brought about by any specific effects the other groups are subjected to. For an excellent introduction to the placebo effect I recommend you read Ben Goldacres' book Bad science which includes a whole chapter on the subject.

Where it is not possible to randomise (often due to spurious ethical reasons) the alternative might be to use some type of matching where subjects in the intervention group are matched on important characteristics that have been shown to effect the outcome either prospectively (case control) or retrospectively (historical control). Another alternative in some situations is a crossover design where the subjects receive multiple treatments.

The above simple design can be adapted and expanded in numerous imaginative ways, cluster designs (where the randomisation is at the group level) and stepped wedge designs to name but a few.

Blinding - Blinding in this context means that the person does not know which group they below to and is another important aspect of many trials. Blinding can be at one of three levels, the lowest level is that of the subject only not knowing, the second level is that of the subject and immediate researcher not knowing and the third level is where the data analysis is also carried out blind.

Where it is necessary to make compromises regarding randomisation and/or blinding the quantitative researcher has at their disposal alternative designs such as the clinical controlled trial and the pragmatic controlled trial. If it is not possible to apply an intervention of some sort then a observational study can be undertaken where group(s) are usually repeatedly measured over time.

Hierarchies and networks of data - it is possible to analyse complex structures such as patients within wards within hospitals within districts and ascertaining the effects at each level using advanced statistical techniques (i.e. multilevel modelling or other similar techniques).

The mindmap opposite shows some of the possible methods used in quantitative research.



Exercise 1

I have attempted to only give you a very brief introduction to methods used by quantitative researchers to find out more please glance over Gerard E. Dallal's excellent article entitles Some Aspects of Study Design at http://www.jerrydallal.com/LHSP/STUDY.HTM

I have mentioned two very important aspects above operationalisation and sample calculations. We will now take a quick look at each.

2.2 OperationalisationOperationalisation is the process of taking a concept and making it into one or more measurements. Some concepts are uni-dimensional that is only require one scale to adequately measure them such as temperature or height while others are more complex, being multidimensional, such as intelligence, empathy, anxiety and happiness often requiring a battery of measures along different dimensions to adequately measure and capture them. From a world one viewpoint this process of scale development and validation is one of the great achievements of quantitative research.

.......................................................


http://www.jerrydallal.com/LHSP/STUDY.HTM


2.3 Sample sizeSample size considerations are vitally important in quantitative research as with too smaller sample the measure you obtain will vary too widely to produce and useful results and too larger sample may be economically wasteful. Sample size requirements are calculated by measuring a statistical concept call power, where we usually aim for a power level of at least .8

Let's consider an example. A fictitious researcher wishes to investigate the effects of a new blood pressure reducing drug which has shown in a previous study with male Scottish, newly diagnosed hypertensive subjects (age range 50 to 55 yrs) to reduce the diastolic blood pressure by 10 mmHg (SD=6.5) the current researcher wishes to see if it has a similar effect upon a similar sample in London she carries out the necessary calculations (using the Gpower program) and obtains the graph shown below. We can see that to achieve a power of at least .8 she needs to recruit a minimum sample of 15

subjects (i.e. 8 in the treatment group and 8 controls) furthermore given this information it is pointless increasing the sample size greatly as information redundancy would result.

While this is a fictitious example with possibly values to suggest a smaller sample size that would be required in reality it is important as it demonstrates that it is not necessary to have large samples for quantitative research. In fact one of the main areas of statistical research in the 20th century was small sample theory notably by William Gossett who

developed the t tests specifically for small sample sizes.

2.4 ToolsThe questionnaire is one of the most common tools used in research and the quantitative researcher develops and uses it in a specific way, to provide numeric data, for example:

Questionnaires with pre-defined responses that could be coded numerically. I.e. Likert scales etc.

Text or 'soft' data would be translated (i.e. coded) to numeric values. For example a sentence might be coded as 1 to 10 on an aggression scale. Similarly diary entries might be coded in particular ways.

Instruments such as questionnaires and other measuring devices would be reliable with the same input value (regardless of the researcher) producing the same result (code) each time.



3. Quantitative researchWhen you carry out any quantitative research the you are taking on a particular world viewpoint, often called a "World view I" this viewpoint asserts the belief that there exists a real objective reality. This idea goes back to Plato (truth by reflective thought) and Aristotle (knowledge through observation and classification). This is the traditional, taken-for-granted view of the world embodied in the RCT (Randomised Control Trial) design that is the bedrock of all medical research.

The following table adapted from Littlejohn (p13 5th Ed.) lists the five premises that embodies this World View I

Premise/assumption

Description

Synchrony Stability over time exists in contrast to change (diachrony). It is therefore valid to develop Causal explanations etc.

Objective measures It is possible to objectively measure an independent reality.

Independent reality There does exists a single reality. The reality is objective and not value-laden. Our experiences are just reflections/interpretations of it. Because our perceptions are merely reflections of this reality we should mistrust concepts such as “subjectivity”, “consciousness” and creative reflection in helping to understand this reality.

Dualism Objects (i.e. the world) and symbols (i.e. language) are separate. Language is just a tool for description, and the world would exist without it.

Correspondence Language corresponds to reality (but provides an imperfect reflection of it).

Most people will think the above aspects represent the truth and are just common sense. The important thing to realise is that they are basically a list of

Assumptions

These assumptions which you could call beliefs imply commitment, and those of you who subscribe to the above beliefs/assumptions, will have arguments to validate them and feel threatened when these are attacked, probably psychologists would call it cognitive dissonance.

Exercise 2

a) How strong would you say your desire is to defend the above viewpoint?

b) What type of arguments would you use to defend them?

I bet you had strong views and used arguments of a 'scientific' / logical nature to defend them.

What is important to realise that logical / scientific arguments depend upon the above assumptions themselves to work. Take for example the argument you may have for the above being the true state of affairs, this probably included stating that it has been proved in some way, however proof in this sense depends upon - Synchrony so your argument requires the assumptions itself!

It is not my desire to discuss the proof or repudiation of the above assumptions, doing so would distract from the important task of using this world view. If you would like to consider other world views (such as the qualitative approach) you can find details on my website.



Exercise 3

By considering the questions asked, knowledge aspects and the method used decide which of the following represents a researcher with a World I view:

Research The questions asked

The types of knowledge used along with the values ascribed to them.

The method use

1. A researcher wants to find out about patient satisfaction at a local hospital outpatients department. She develops a questionnaire consisting of 20 questions each of which consists of a number of predefined responses. The questionnaire is given to 500 random subjects and the data analysed using a statistical programme (SPSS). The results are published along with a set of recommendations for Hospital Outpatients Departments in general.

2. A researcher wants to find out about patient satisfaction at a local hospital outpatients department. She spends a few days in the department asking treated patients to tell her what they think of the department and their experience. She records the interviews and then quotes (sometimes at length) the parts she feels are most relevant in a document describing individual patients experiences. The document is structured into a number of themes that she feels those interviewed felt to be most important.

3. A researcher wants to find out about patient satisfaction at a local hospital outpatients department. She sits quietly observing what is happening in the department (unknown to both staff and patients) and records the number of times patients return to reception to enquire about their waiting time and the number of patients that leave before being seen by a medical member of staff.

4. A researcher wants to find out about patient satisfaction at a local hospital outpatients department. She develops a web blog and publicises it within the department. After 6 months, when she has over 1,000 entries, she pulls out 'themes' she has identified in the postings and produces a report.

5. A researcher wants to learn more about patient's experiences at a local hospital outpatients department. She has a background in nursing and has arranged with a senior manager to shadow a nurse for the week on the assumption that she is interested in seeing the variety of procedures the nurse's carry out. During her time observing she makes notes about how the patients and nurses interact and how a satisfactory outcome is negotiated between them. She writes up her experience after a great deal of reflection.



4. DataData is everywhere. In fact nearly everything one can think of has a set of characteristics ('data') which enables us to recognise and remember it. Data can vary from the particular colour of a dandelions flower to the resting heart rate of a subject undergoing an exercise trial. While it is obvious that the colour and heart rate differ radically, one could not sensibly compare a colour with a heart rate, it is more difficult to define how exactly they do differ but in this document we will present a classification which does achieve this, so how does data differ, and why is it inappropriate to compare colour of dandelions to heart rates.

Often a set of Data is collected to form a Dataset. A dataset consists of values for one or more characteristics over a number of objects (i.e. patients) A simple dataset may consist of peoples shoe size.

If a particular characteristic can take more than one value it is known as a variable e.g. shoe size, height, IQ etc. A characteristic that can take only one value is known as a constant. However note that a characteristic may be a variable in one situation (e.g. species for a group of farm animals or age of patients) and a constant in another (e.g. species for a group of children, age for a group of children in their first year at primary school in the UK). We frequently say that we have collected a number of variables rather than a dataset.

Exercise 1

In the following dataset which could be classified as a variable and which a constant?

A persons star sign, shoe size and age can therefore all be classified as variable data for a group of people in which there might exist more than one different value for each variable. Instinctively we feel that each of these variables are different as we did with comparing the Heart rate to the Dandelions flower colour in the previous paragraph. What exactly is the difference between them? To help answer this question we will consider two different ways data has been classified, one from the viewpoint of the statistician and the other from the social scientists perspective.

4.1 The Statisticians View of Data

Statisticians classify data into two broad types qualitative and quantitative, each of these is further sub-divided into two further types resulting in four basic types; Nominal, Ordinal, Discrete and Continuous. It is important to give you a warning concerning the use of the term qualitative:

Qualitative data and Qualitative research methods are completely different things

Eye colour is an example of qualitative data. This is because with qualitative data the 'values' are essentially words used to specify categories. In contrast, with quantitative data, the values are numerical attributes which the data possesses


Data

qualitative

quantitative

Nominal

Ordinal

Discrete

Continuous

Age Gender

18 mal


itself. Qualitative data are often given numerical codes, but any arithmetic done with the codes will be meaningless, as will become obvious when one returns to the words behind the codes. For example, type of dwelling ('semi-detached', 'detached', 'terrace', etc.) is qualitative (nominal). The 'values' might be coded '1', '2', '3', etc., but although we can add '1' and '2', adding 'detached' and 'semi-detached' has no meaning, and certainly does not result in 'terrace'! This scale only allows us to state which particular category a data value belongs in (e.g. red blue or green for eye colour) and count (enumerate) how many there may be in each category for a particular data set. Nominal data is therefore also often called categorical or enumerate data. The count for a particular category is often referred to as the frequency.

The term 'ordinal' is used when it is possible to order the various categories to create a scale. For example, the responses to a question asking 'How often do you have problems getting to sleep?' might be labelled 'every night' , 'most nights', 'some times', 'rarely' and 'never'. The correspondence between these words, although clearly graded in order, is questionable in terms of the relative distance between each, for example is 'most nights' a similar distance away from 'some times' as 'rarely' is to it in the opposite direction. Much effort is made to make such ordinal data possess a scale that approximates equal distances such as the common set of responses ('strongly disagree', 'disagree', 'neutral', 'agree', 'strongly agree') to attitudinal questions such as, 'I find statistics boring?'. It should be noted that even when efforts are made to make the scale have similar intervals it does not make sense to perform mathematical operations on them such as 'disagree' + neutral = 'agree' etc.

In medicine there are several systems for classifying the extent or stage of cancer. The two most common are the Stage I, II, III, IV system (in contrast the American Joint Committee on Cancer (AJCC) uses five stages from 0 to IV) and the TNM (Tumour, Node, metastases) system. These staging systems provide estimates of the stage of disease and chances of survival. For more information see both http://www.cancerstaging.org/cstage/index.html and http://www.oncologychannel.com/coloncancer/staging.shtml

The only valid type of mathematical operation that can be carried out on ordinal data is to order it and count how many observations exist at each point in the scale. The process of ordering data is called Ranking which we will discuss latter.

Both Nominal and Ordinal data is sometimes just referred to as qualitative data again:

Qualitative data and Qualitative research methods are completely different things

Exercise 2

Which of the following variables are Nominal and which are Ordinal?

1. List of patron saints (Broadcaster = St. Gabriel; Invalids= St. Stephan; Bee keepers= St. Ambrose etc.)

2. Hair style

3. Learning style (such as 'deep', and 'superficial')

4. Shoe size (1 to 14)

5. Species of Ant

6. The Seven Dwarves (Bashful, Doc, Dopey, Grumpy, Happy, Sleepy, Sneezy)

7. Eye response (No eye opening, Eye opening to pain, Eye opening to speech, Eyes open spontaneously)

8. Sleeve length of glove( Shoulder length, Above elbow, Elbow, Mid-forearm)

9. Ivy League Universities (Brown, Columbia, Cornell, Dartmouth, Harvard, Pennsylvania, Princeton, Yale)

10. Deadly sins (Pride, Greed, Lust, Envy, Gluttony, Anger, Sloth)

11. Star (Zodiac) sign

12. Pencil Hardness

13. Iceberg size( Growler, Bergy bit, Small, medium, Large, Very large)

The other major subdivision of data used by statisticians is that dividing Quantitative data. Quantitative data, also called numerical data can be either discrete (for example, the number of children in a family) or continuous ( for example, the height of an experimental subject in cm). What is the difference?

Discrete data can only have values that are separated by impossible values, e.g. you cannot have half a child. Another example is Shoe, or for that matter most readymade clothes, sizes.


http://www.oncologychannel.com/coloncancer/staging.shtml

http://www.cancerstaging.org/cstage/index.html


Continuous data can take any value within a range, e.g. While a particular height might be 217 cm it could easily be 217.34 or even 2.17.345635 depending upon the accuracy of the measuring mechanism.

While often people are told that discrete data consists of whole numbers (integers) this is not always the case. Take for example, the case of the 'number of questions' answered correctly in a test on spelling' . This will be represented by 'whole' numbers, however you can represent the same data as a 'proportion of correct answers', for it is clear that 21 correct answers out of 30 is a discrete value that may nevertheless be represented as 0.7 when it is the proportion of correct answers that interests us.

While statisticians spend a great deal of time differentiating between discrete and continuous data, for our purposes continuous data is often treated as discrete data and it does not really cause too many problems.

In contrast to the above classification social scientists classify data in a slightly different way which will be discussed next.

4.2 The Social Scientists View of DataWhile social scientists use the Nominal and Ordinal classification they prefer to adopt another type of classification for the terms discrete and continuous. The third level of measurement they describe is one which possesses those characteristics described above for ordinal data but in addition has equal sized intervals e.g.: Fahrenheit, Celsius, bank balance. It is interval measurement data.

The most complex type of scale involves all of the above characteristics and in addition possesses an absolute zero point e.g. height, weight, distance, Kelvin's. This, ratio measurement type, is the most complex of the measurement scale types.

Do not worry too much if you find it difficult to differentiate between Interval and ratio data as it does not matter too much, in contrast the important thing is to be able to differentiate between Nominal, Ordinal, and (Interval/Ratio) data. Since Stevens 1951, suggested this classification several authors have criticised the degree of prominence it has achieved concerning which types of statistics are considered appropriate for each type (Gaito 1980).

The chart below provides the above information in summary form. An easy way to remember the data types is the word 'Noir'.

Measurement type (e.g.) Name Order Equal Intervals

Absolute zero

Nominal

(star sign)

X

Ordinal (fitness rating scale)

X X

Interval (Fahrenheit)

X X X

Ratio (weight)

X X X X

Exercise 3

Produce a list of about 20 characteristics about yourself and categorise them into the appropriate measurement scale type.


Tip: Memorise this chart and you can't go wrong


Exercise 3

As the Dean of Students for your Medical School, you must prepare each student’s class ranking in clinical work. The ranking, which will be used for internship and other recommendations, comes from a combination of grades for clinical clerkships in five departments. Each grade is given an equal weight in the student’s “class standing.” The five clinical departments express their individuality by using different scales for giving grades. The scales are as follows:

Internal Medicine: A,B,C,D,E, (with A = highest and E = lowest)

Obstetrics-Gynecology: A+, A, A−, B+, B, B−, C+, C, C−, D, and E

Pediatrics: Numerical examination grade from 100 (perfect) to 0 (terrible)

Psychiatry: Superior, Satisfactory, Fail

Surgery: High honors, Honors, Pass, Fail

How would you combine these non-commensurate scaling systems to form a composite score for class standing? Taken from Feinstein, 2002 page. 65

4.3 ConceptsResearchers frequently wish to investigate some abstract concept (e.g. health, patience etc) which means they need to develop one or more measures which they may combine in an attempt to measure it.

Two such examples are intelligence and physical exhaustion. A standard method of measuring intelligence is to use the Wechsler Adult Intelligence Scale (WAIS) intelligence scale to obtain an intelligence quotient (IQ). The WAIS consists of 10 measures, which are themselves grouped together to form sub-scales before being combined.

From:

http://en.wikipedia.org/wiki/Wechsler_Adult_Intelligence_Scale

Similarly the concept of physical exhaustion can be measured by considering both the Borg scale, and Vo2Max reflecting both the psychological sensation and the physiological manifestation of physical exhaustion. The process by which a researcher chooses/devises one or more scales (also called constructs) to measure some possibly abstract, concept is known as operationalisation and how well s/he does this often provides the litmus test for the research. There is always much debate concerning the validity of combining or alternatively keeping separate the various sub scales. As you can imagine there are numerous statistical techniques to help you make a decision.

Obviously the measurement of ‘health’ is a major area of concern. Many scales have been developed, a typical examples are the Duke health profile (17 items!) and the Robin Beaumont [email protected] /tt/file_convert/5a7073667f8b9ac0538c0272/document.docx page 14

Constructs assessed by the MHP-H

Adult Health History (individual ratings of general health and the

presence of chronic illness)

1. Severity of typical illness

2. Presence of a chronic illness

3. Impairment due to chronic illness

4. Overall health (over adult life)

5. Recent health (over past six months)

Health Habits (sums of 24 individual ratings of positive and

negative health habits)

6. Positive habits

7. Negative habits

Health Care Utilization (frequency of utilizing four different types

http://en.wikipedia.org/wiki/Wechsler_Adult_Intelligence_Scale

http://upload.wikimedia.org/wikipedia/en/d/d2/Wechsler_Adult_Intelligence_Scale_subscores_and_subtests.png


Nottingham health profile. Specialist measures have also been developed such as the Multidimensional [psychological] Health Profile (Karoly, Ruehlman, & Lanyon, 2005). According to Lanyon, Maxwell & Karoly 2007 “This instrument was designed to alert health care personnel to potential [psychological] problems that should be addressed in more detail, and consists of two sections: Psychosocial Functioning (MHP-P), and Health-related [psychological] Functioning (MHP-H).”

Exercise 3

Consider the concept of fatigue? Do you think many papers have been published discussing how you might measure it? Do you think there are different varieties?

Within the healthcare setting which conditions do you think might benefit from such a measure?

Do you think when measuring it you would need to develop one or more sub-scales, that is do you think it might have different aspects?

Please don’t turn the page until you have carried out the above exercise.

A though

literature review of various measures of fatigue as used in chronic exercise research can be found in Puetz, O'Connor, Dishman, 2006.


Pathological fatigue

Quoting Kittiwatanapaisan 2003

Pathological fatigue, in contrast to normal fatigue, does not subside with rest and is characterized by a feeling of tiredness before activity, lack of energy to complete tasks, exhaustion after usual activity, or all of the above. Fatigue has been found to correlate with physical and psychological parameters in patients with multiple sclerosis, chronic fatigue syndrome, HIV infection, and AIDS (Breitbart, McDonald, Rosenfeld, Monkman, & Passik, 1998; Ford, Trigwell, & Johnson, 1998; O'Dell, Meighen, & Riggs, 1996; Packer, Foster, & Brouwer, 1997; Vercoulen et al., 1997; Walker, McGown, Jantos, & Anson, 1997). . . . .

Fatigue has been measured with various instruments, mainly, visual analog scales and questionnaires with Likert-scale format, including the Checklist of Individual Strength-Fatigue (van der Werf et al., 1998; Vercoulen et al., 1996, 1997), the Fatigue Severity Scale (Packer, Sauriol, & Brouwer, 1994), the Chalder Fatigue Scale (Ford et al., 1998), the Fatigue Assessment Inventory (O'Dell et al., 1996), the Piper Fatigue Scale (Cupler, Otero, Hench, Luciano, & Dalakas, 1996; O'Dell et al., 1996), and the Multidimensional Assessment of Fatigue (Schwartz, Coulthard-Morris, & Zeng, 1996). However, these instruments are not specific for measuring fatigue in the MG [Myasthenia Gravis] patient population.

Grohar-Murray, Sears, Hubsky, and Becker (1994) combined and modified two unpublished questionnaires, which were used to measure fatigue in multiple sclerosis patients, for use with MG [Myasthenia Gravis] patients. . . . . .

Abstract references:

Breitbart, W., McDonald, M.V., Rosenfeld, B., Monkman, N.D., & Passik, S. (1998). Fatigue in ambulatory AIDS patients. Journal of Pain and Symptom Management, 15, 159-167.

Chalder, T., Berelowitz, G., Pawlikowska, T., Watts, L., Wessely, S., Wright, D., et al. (1993). Development of fatigue scale. Journal of Psychosomatic Research, 37, 147-153.

Cupler, E.J., Otero, C., Hench, K., Luciano, C., & Dalakas, M.C. (1996). Acetylcholine receptor antibodies as a marker of treatable fatigue in HIV-1 infected individuals. Muscle & Nerve, 19, 1186-1188.

Dzurec, L.C., Hoover, P.M., & Fields, J. (2002). Acknowledging unexplained fatigue of tired women. Journal of Nursing Scholarship, 31(1), 41-46.

Ford, H., Trigwell, P., & Johnson, M. (1998). The nature of fatigue in multiple sclerosis. Journal of Psychosomatic Research, 45, 33-38

Grohar-Murray, M.E., Becker, A., Ricci, M., Polak, M., & Danehy, S. (1994). Fatigue characteristics, severity, and impact on the functional status in myasthenia gravis. Unpublished manuscript.


5. Hierarchy of datatypesIt is importance to realise that the various datatypes (or measurement scales if you prefer the term) represent a hierarchy of complexity. The most complex data it that which has Interval/Ratio characteristics and because of this hierarchy of complexity if you convert Interval/Ratio data to Ordinal data you will loose some of the information within it. This is related to the process of Ranking which is discussed in the next section.



6. Ranking Data

The process of ordering data and assigning a numerical value is called Ranking. Let's take an example by considering the following numbers: 5, 3, 8, 1, 10

Ranking them from smallest to largest and assigning a value to each ‘the rank’ would produce the following result:

What do we do if we have the situation of tied scores (ties) i.e. two, or more, with the same value?

Score (ordered)

Rank

10 1

8 2

5 3

3 5

3 5

3 5

1 7

Example: Consider the following numbers 5, 3, 8, 3, 1, 3, 10

Placing them in order of magnitude: 10, 8, 5, 3, 3, 3, 1, We note that there are three 3s. These are equivalent to the ranked scores or the 4th, 5th and 6th score. We therefore allocate the average of these ranks (i.e. 4 + 5 + 6 / 3 = 5) to each of them.

6.1 Magnitude and RankingNow considering the following example instead of one set of data consider the two given below. Notice that increasing the magnitude of the lowest and highest scores has not affect on their rankings. Therefore by ranking our data we have lost the importance of magnitude in the original dataset.

6.2 Rating ScalesRating scales are just a way of obtaining ordinal data by asking subjects to rate



from say 1 to 10 a particular response. For example it might be to ask them 'how exhausted they are' (known as the Borg scale), or how much pain they have etc. Psychologists and statisticians argue if the data collected can be considered ordinal or does possess the additional characteristics to be interval or ratio. Rating scales are also sometimes represented graphically as shown above.



7. Collecting and organising your data



8. Multiple Choice Questions (MCQs)

1. I suggest two reasons why I feel people fall foul at the first hurdle of learning statistics. Which of the following are they? (two correct choices)

a. 'user friendly' introductions under emphasising basic concepts

b. 'user friendly' introductions incorrectly explaining basic concepts

c. statistics presented as a poorly defined subjective discipline

d. over emphasis on the use of computers

e. statistics presented as a clear cut subject with clearly defined rules

2. Which of the following is an example of nominal data? (one correct choice)

a. Number of people on a course

b. Cancer staging scale

c. List of different species of bird visiting a garden over the past week

d. Popularity rating of UK top ten television programmes

e. Heart rate

3. Which of the following are examples of Interval/Ratio data? (two correct choices)





e. Heart rate

4. Which of the following are examples of Ordinal data? (two correct choices)







e. Heart rate

5. Which of the following is the correct listing of data from the simplest to the most complex? (one correct choice)

a. Nominal -> Ordinal -> Interval -> Transcendental

b. Nominal -> Ordinal -> Interval -> Ratio

c. Qualitative -> Ordinal -> Interval -> Discrete

d. Qualitative -> Ordinal -> Interval -> Ratio

e. Nominal -> Ordinal -> Interval -> Quantitative

6. Which of the following is an incorrect statement about Ranking a dataset? (one correct choice)

a. You can rank any dataset as long it is not Nominal

b. Each value in a dataset should only occur once

c. The process of ranking a dataset involves ordering it and then assigning a 'rank' value to each score from 1 to the number of scores in the dataset.

d. When ranking a dataset tied scores receive the average of the rank value given to the ties.

e. The result of ranking a dataset means that you lose the effect of magnitude if the data were Interval/Ratio

9. SummaryIn this chapter we have looked at data and how it is classified from two perspectives, that of the statistician and also the Social scientist. We started by discussing the simplest type of data, Nominal data which can only be categorised and counted. Next came data where it was possible to order the various categories to produce a scale, although the relative distance between the points was not specified. A large number of examples of this type of Ordinal data were given from attitudinal question scoring to Cancer staging scores.

The more complex data, termed by statisticians 'Quantitative' data possessed the additional characteristic of magnitude, and the social scientists termed such data Interval/Ratio. Such data was what most people would traditionally class as real data possessing a scale with equal intervals.

The process of ranking data was described and the effect that the process had upon a original dataset of Interval/Ratio data was discussed. The effects of this process highlighted the importance of bearing in mind the hierarchy of data types



(measurement scales) and how possibly valuable information about data may be lost by converting data from a higher level scale to that of a lower level one.

The chapter ended with a set of Multiple Choice Questions (MCQs) to help you review what you have learnt. As a final revision exercise you should return to the learning outcomes at the beginning of the before moving on to the next chapter.

10. ReferencesFeinstein RA 2002 Principles of Medical Statistics Chapman Hall [Author died at the age of 75 in 2001]

Gaito J 1980 Measurement Scales and Statistics: Resurgence of an Old Misconception. Psychological Bulletin. Vol 87(7), 564-567

Gigerenzer G Swinjtink Z Porter T Daston L Beatty J Kruger L 1989 The empire of chance. Cambridge University Press

Gonick L Smith W 1993 The cartoon guide to Statistics. Harper Resource

Grant S, Aitchison T, Henderson, E Christie J, Zare S, McMurray J, Dargie H 1999 A comparison of the reproducibility and the sensitivity to change of visual analogue scales, borg scales, and likert scales in normal subjects during submaximal exercise. doi:10.1378/chest.116.5.1208

Hawkins A Jolliffe F Glickman L 1992 Teaching statistical concepts. Longman

Howell D 1992 Statistical methods for psychologists Duxbury (chapman & hall in UK)

Karoly, P., Ruehlman, L. S., & Lanyon, R. I. 2005 The assessment of adult health care orientations: Development and preliminary validation of the Multidimensional Health Profile-Health Functioning (MHP-H) in a national sample. Journal of Clinical Psychology in Medical Settings, 12, 79–91.

Kittiwatanapaisan W, Gauthier D K, Williams A M, Shin J O. 2003 Fatigue in Myasthenia Gravis patients. Journal of Neuroscience Nursing. April, 2003 Retrieved from: http://www.entrepreneur.com/tradejournals/article/102271373_4.html on 15/02/2010 17:04

Lanyon R · Barbara M. Maxwell B M, Karoly P, Ruehlman S L. 2007 Concurrent Validity of the Multidimensional Health Profile—Health Functioning Scales (MHP-H) in the Pre-operative Assessment of Applicants for Gastric Bypass Surgery. Journal of Clinical Psychology in Medical Settings, 14:41–49.

Mood A M Graybill F A Boes C D 1974 Introduction to the theory of statistics (3rd ed.) McGraw Hill

Puetz, T W; O'Connor, P J; Dishman, R K. 2006 Effects of chronic exercise on feelings of energy and fatigue: A quantitative synthesis. Psychological Bulletin. Vol 132(6), 66-876

Stevens S S 1951 Mathematics, measurement, and psycho physics. In S S Stevens (Ed.) Handbook of experimental psychology (pp. 1 - 49) New York Wiley


http://www.entrepreneur.com/tradejournals/article/102271373_4.html

http://dx.doi.org/10.1378%2Fchest.116.5.1208

http://en.wikipedia.org/wiki/Digital_object_identifier

11.

Quantitative /Qualitative research fundamental propositions

12. FAQs (Frequently asked Questions)12.1 If you repeat the same research what does it mean?In a previous version of this document exercise 4 repeated the research

"2. A researcher wants to find out about patient satisfaction at a local hospital outpatients department. She spends a few days in the department asking treated patients to tell her what they think of the department and their experience. She records the interviews and then quotes (sometimes at length) the parts she feels are most relevant in a document describing individual patients experiences."

The idea behind the two identical pieces of research was to get you thinking about the consequences of the assumptions of world view 2.

In littlejohn you will read about phenomenology - basically gaining knowledge through personal experience/ interpretation, for example if you want to understand blindness you wouldn't read about it but cover your eyes and force yourself to be blind for a time. You would then reflect upon it interpreting your personal experience.

Along with this concept comes the idea that it is not possible to generalise as we all have unique experiences which are constantly developing. In music the first time you hear a chord has a different impact and significance to a repetition of it, and even more repetitions have yet other effects which the minimalist composers have investigated. For a researcher who espouses world 2 beliefs the same research repeated offers unique insights each time. This is in complete opposition to the world I viewpoint where replication / reproducibility is considered a good thing.

So if you hold world I beliefs both research exercises are the same (replication), but if you hold world II beliefs they are not.

Notice that the difference relates to the researcher not the research method etc.

12.2 Qualitative and quantitative research ask different questions - wrongConsider the following from a past student:

"Statement: Questions such as "How many parents would consult their general practitioner when their child has a rash?" or "What proportion of common cold suffers take over the counter preparations?" clearly need answering through quantitative methods. However, questions like "Why do parents seek medical opinion when a child has a rash?" and "What stops people with common cold seeing their general practitioner?" cannot be answered by these methods. Hence, qualitative techniques have to be employed."

The above shows many misunderstandings. The writer is confusing method adopted with philosophical approach. A person with qualitative beliefs would apply those techniques in any situation; similarly a researcher with quantitative beliefs would apply those in any situation. Think of the different and (opposing approaches to measuring cognition - from personal biographies to complex experimental designs).

12.3 Qualitative research does not use hypotheses - wrongConsider the following from a research methods book:

"Quantitative research begins with an idea (hypothesis), which then, through measurement, generates data and, by deduction, allows a conclusion to be drawn. Qualitative research, in contrast, begins with an intention to explore a particular area, collects "data" (observations and interviews), and generates ideas and hypotheses from these data largely through inductive reasoning."

This is not true - Look at possible shadowing exercise (a qualitative technique) - you may decide to enter it with a clean slate (e.g. the Glazzer and Strauss grounded theory approach ), alternatively, knowing about the particular environment your entering you may have researched the literature in that area (i.e. women working in engineering etc) and have already decided to take a feminist lens to the day. Similarly at other times those with a world view 1 set of beliefs may well start with a hypothesis (as recommended by Fisher and Neymenn Pearson etc) however there are techniques which encourage the researcher to interact and explore the data such as Structural equation modelling, longitudinal designs and some newer, beyond significance testing techniques.

So both qualitative and quantitative research approaches may use hypotheses or not.

Robin Beaumont [email protected] http://www.robin-beaumont.co.uk/virtualclassroom/chap5/s5/comm_theories/qual_quan1.pdf Page 24 of 28


12.4 Qualitative research is high in validity whereas quantitative research is high in reliability - wrongAgain this is not true - I do not want to get into a discussion about the many verities of validity but once you look into the literature you can see this is more to do with the process of 'operationalisation' and how this is handled in a quantitative research design (i.e. world 1 view assumptions).

Why do people make the above mistakes, I think one of the common causes is the table that is often reproduced below - most of these dichotomies do not exist.

Qualitative versus quantitative research:

Misleading WRONG table

Qualitative Quantitative

Social theory Action Structure

Methods Observation, interview Experiment, survey

Question What is X How many Xs?

Reasoning Inductive Deductive

Sampling method Theoretical Statistical

Strength Validity Reliability

Basically - a misleading inappropriate table.

12.5 You can combine the two philosophical approaches - not reallyYou can combine both qualitative and quantitative research - no, how can you be a catholic and a muslim at the same time? Look at the table which lays out the propositions of world view I and II in the fundamental proposition document - they contradict each other!

When you do combine the two you always make a value judgement - for example you use qualitative interviews to help inform the development of a quantitative questionnaire - so you are espousing world 1 beliefs really. etc.

12.6 There is one hierarchy of Evidence - no

World I viewpoint

Sackett's hierarchy of evidence has provoked much unresolved controversy about the kind of evidence that is actually most relevant to practice; for example, controlled trials often restrict the kind of patients recruited, whereas cohort studies better reflect normal patterns of patient management.

The hierarchy of levels of evidence is briefly:

1. A

B

C

Systematic reviews/ meta-analyses

RCTs

Experimental designs

2. A

B

Cohort control studies

Case-control studies

3. A

B

C

Consensus conference

Expert opinion

Observational study



D

E

Other types of study eg. Interview based, local audit

Quasi-experimental, qualitative design

4. Personal communication

Possible World II viewpoint from a phenomenological perspective?

1. A

B

C

Self reflection - autobiographies, collected journals, life histories etc

Letters to friends etc

Public interviews etc

2. A Shared documents

3. A

B

C

D

E

Consensus conference

....

....

.....

RCTs, Experimental designs

4. Systematic reviews/ meta-analyses

Basically one table is a mirror reflection of the other!!


Observer

Model Reality


12.7 The basis of knowledge is belief or the scientific method!Much of what you read about qualitative and quantitative methods does not taken into account the philosophical considerations and is not worth the paper it is written on (!) and you will soon be able to critique such superficial dribble. Obviously your own viewpoint means that you either adhere to one or the other - one is right because you have proved (i.e. believe in) it. Religion - a good example of a world II viewpoint states that the basis of knowledge/wisdom is belief and this in essence reflects the world 3 view of the nature of knowledge. For those with a world one viewpoint the foundation of knowledge is seen as the scientific method offering a very different approach.

The philosophical study of the nature of knowledge is called epistemology so we can say that those with a world 1 viewpoint posses a very different epistemological viewpoint to those espousing a world 1 viewpoint which is demonstrated above in the two different hierarchy of evidence tables.

12.8 Quantum theory validates the world 2 viewpoint - no!Much has been written about various descriptions in quantum mechanics to suggest that there is no one single external reality. Common arguments present such findings as:

Viewing an experiment changes its outcomeA thing can be in two places/states at the same time. See Schrödinger's cat http://en.wikipedia.org/wiki/Schr%C3%B6dinger%27s_cat

This is not the case and no respectable scientist would subscribe to the use of these subatomic mathematical modelling constructs to large atomic structures such as a cat! Two excellent sources of information are; Fashionable Nonsense by Sokal & Bricmont 1998, and the road to reality by Penrose 2005.

I think the stumbling block is that people see the mathematical model whether it be Newtonian or Quantum Physics as reality rather than just a possible imprecise model of it, Models only ever offer a lens through which to analyse it (note the singular it).

Exercise 4

The following abstract is taken from a confused student; consider an appropriate answer, highlighting his main misunderstandings

"Quantitative studies are where responses are predefined for analysis purposes I understand, and although it does have limitations I can see the value of it Qualitative research adds depth. Where I have great difficulty is the analysis of notes taken by an observer of the perceived interactions of others.

Not having been involved with qualitative studies I find it hard to comprehend how such research is undertaken so that there is a useful outcome. Surely if subjects are made aware that they being observed they will change their attitudes/ actions and therefore influence the outcome of the study. This could be good or bad. There are number of factors that could influence this e.g bad mood, dislike of the observer etc. I know that I am influenced when making a phone call to a bank/ utility service, and I’m told that the call may be recorded for training purposes; I make a effort to be pleasant. Surely this invalids the research."


http://en.wikipedia.org/wiki/Schr%C3%B6dinger's_cat


13. References

Sandelowski M 1995 Sample size in qualitative research. Res Nurs Health. Apr;18(2):179-83.

Schulz KF,Altman DG, Moher D, 2010 CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. BMJ 2010; 340 doi: 10.1136/bmj.c332 (Published 23 March 2010)

New ones

EBM triangles: http://cardio.nl/ACS/StudiesRichtlijnenProtocollen.html


Documents

Quantitative Qualitative fundamental Web viewQuantitative /Qualitative research fundamental ... after admission to the study, ... specific for measuring fatigue in the MG [Myasthenia