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N360 RESEARCH > Chapter 8 > Flashcards

Chapter 8 Flashcards

1
Q

Are certain variables dependent and others independent? Or does it depend on context?

A

o It’s not about the variable, but rather how it’s used that determines if it’s indep or dep

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2
Q

Are the variables explicitely stated in the research process in Ql and Qn research?

A

• In Ql, research variables often clearly ID’d in the open-ended research questions.
In contrast, in Qn, should clearly describe the variables even if they are not explicitly labelled as such b/d data collection methods are spec aimed at measuring study variables as objectively as possible
(I don’t understand the difference here…p 149 end of par 4)

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3
Q

Variables can be defined at 2 levels. what are they?

A

Operational + theoretical

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4
Q

Theoretical def’n

A

one that is described and understood conceptually. Not always clearly measureable. (ex: stress defined as perception of threat and that he/she can no way to manage that threat)

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5
Q

Operational def’n

A

o one that is defined in specific concrete terms; allows us to see how we would measure the var (ex: stress defined as heart rate or rating of threat on 4 point scale)

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6
Q

Do variables in Qn need a theoretical or an operational def’n?

A

• Many variables in healthcare are abstract and need both theoretical + operational def/n if they are going to be used in Qn research
(needs operational def’n but not necessarily theoretical)

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7
Q

Can most variables be operationalized?

A

• Almost any var can be operationalized, but the correctness + accuracy of that operationalization must be evaluated

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8
Q

Operationalization =

A

translation from theoretical and concrete measure or set of measures

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9
Q

What is an error?

A

• When variable is not measured with 100% accuracy, we say there is error in measurement.
Error refers to the difference between what is true and the answer we obtained from our data collection (ex: difference in results from observing + recording 1000 people’s genders and knowing the true genders)

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10
Q

Does Ql operationalize?

A

Ql doesn’t operationalize variables b/c does not presume to know enough about the var of interest to assign concrete measures. It DOES translate specific experiences or observations into theoretical concepts or descriptions of variables during data analysis

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11
Q

Are both Ql and Qn open to errors? In which kind is there more opporunity for error?

A

Both kinds of research are open to errors in translation!

In Ql, you are translating specific experiences or observations into theoretical concepts or descriptions of variables during data analysis. Therefore, Ql is open to errors in interpretation (ex: may fail to take into account all of the themes a person is expressing).
• Errors of measurement can be even greater in Qn b/c are operationalizing…can occur in both the translation of theoretical to oper + in the operationalized measurement

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12
Q

What kind of var is quite easy to operationalize?

A

demographic

• Eg: Education can be operationalized as 1) if student has RN or LPN diploma 2) total number of yrs in post-secondary

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13
Q

What is the primary putpose of Ql research r/t to operationalization

A

• Primary purpose of many Ql is to dev clear theoretical def’n of var so might eventually be operationally defined + concretely measured
(I feel like many Ql researchers would disagree with this!)

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14
Q

Does Ql seek an absolute truth?

A

• Ql assume truth is moving target – we can come closer to finding a meaning of variable but it is always highly context-laden + therefore evolving

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15
Q

Data collection methods for Ql research include?

A

DCMS for Ql rely on sharing through verbal, visual, written, music, within life activities (such as cooking a meal). Includes interviews, journaling, observation art analysis

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16
Q

Two categories of interviews for Ql research

A

unstructured + those that used groups

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17
Q

What do unstructured interviews look like?

A

1) Q’s asked in informal + open fashion w/o previously established set of categories or assumed answers.
- Assume is dependent on interactions between interviewer+ participant.
- Researcher may bracket + put aside own knowledge/beliefs or may incorporate
- Data collected include not just spoken words but actions, expressions, etc

  • May be carried out through in-depth interviews oral histories, storytelling, life review, etc. Typically recorded + then transcribed verbatim with notes on sighs, tone, etc.
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18
Q

What are the notes taken on interactional dynamics (actions, expressions, body language, etc) during an interview called?

A

Field notes

what the interviewer is seeing during the process…used to enrich the data

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19
Q

How do group interviews for ql research work?

A

a. May take form of focus groups or brainstorming
b. Incl not just member’s responses to the questions but then also responses to other group members
c. May happen informally as come across people in dialogue already – research introduces self + obtains consent to listen in

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20
Q

Pro and cons of group interviews?

A

a. Pros: rich in data and inexpensive

cons: may limit hearing + knowing unique perspectives as limits some individual expression

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21
Q

Are semistructured + structured interviews used in ql research?

A

– assume + control options for answers. Not usually used in Ql.

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22
Q

What can journaling be used for in ql research?

A

Journaling can provide continuous and evolving info from an individual perspective that cannot be collected face to face.

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23
Q

What is a free write?

A

• more limited form of written data in which participant is directly asked to write response or description about phenomenon on the occasion of data collection
(I think it’s considered more limited than journalling or interview perhaps)

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24
Q

How can we study the past in Ql research?

A

• Documents + records may be used in Ql to examine the past

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25
Q

Is data collection in Ql research structured? Objectified? Planned + thought through?

A

• Data collection in Ql is not structured or objectified, but is carefully planned + thought through

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26
Q

Two major ways of introducting errors in Ql research

A

during process of data collection, analysis or both.

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27
Q

What is rigor? 4 aspects?

A

strict process of data collection + analysis as well as a term that reflections the overall quality of that process in ql research. Is reflected in consistency of data analysis and interpretation, the trustworthiness of the data collected, the transferability of the themes, and the credibility of the data.

1) Trustworthiness
2) Confirmability
3) Transferability
4) Credibility

28
Q

Trustworthiness in Ql research
What is it?
2 ways of achieving it?

A
  • Is the honesty of the data collected from or about the participants
  • Methods: establishment of ongoing or meaningful interactions w participants + and use of consistent protocol in data collect
29
Q

Confirmability in Ql research
What is it?
2 ways of achieving it?

A
  • Is the consistency and repeatability of the process of data collection + data analysis
  • Methods: use of computer software + audit trails
30
Q

What are audit trails?

A

• an ongoing documentation regarding the researcher’s decisions about the data coll + analysis processes. Assist’s researcher to be consistent + demonstrate this.

31
Q

How can computer software be used to increase confirmability of Ql research?

A

• Computer software can be used to organize data into themes (this increases confirmability). Can rearrange data but still keep record of how re-categorization evolved

32
Q

TRANSFERABILITY in Ql research
What is it?
2 ways of achieving it?

A
  • Refers to the extent to which the findings of a study are confirmed by or are applicable to a different group or in a different setting from where the data were collected
  • Methods: external checks + seeking disconfirming cases or outliers
33
Q

Is transferability the same as generalizability (seen in Qn research)?

A

Not same as generalizability b/c focus not on predicting outcomes in general pop. Rather is about confirming that what was meaningful in one setting or group is meaningful and accurate in another

34
Q

What are external checks?

A

Used to confirm transferability of Ql research

themes ID’d in one study are relayed to other group to confirm if they agree

35
Q

CREDIBILITY in Ql research methdods.
What is it?
Two ways to ensure it?

A
  • Overlaps with transferability + trustworthiness is also ensured by the processes that ensure these (ex: spending time with participants, maintaining thorough observations…)
  • Refers to the confidence the researcher + user of the research can have in the truth of the findings of the study
  • Methods: member checks + triangulation
36
Q

Member checks. What are they?

A

Used in Ql reserach to inc crediiblity

– data + findings brought back to original participants to seek input + confirmation of accuracy

37
Q

What is triangulation

A

Used in Ql reserach to inc crediiblity
– process of using more than one approach or source to include different views or to look at phenomenon from diff angles. Can be seeking different types of sources for data, more than one investigator, use of several theories, or use of numerous methods in the study

38
Q

Examples of measures used in Qn research

A

physiologic measurements, chemical lab tests, systematic observations + written measures containing carefully defined questions, questionnaires and/or scales

May use systematic observation or instruments

39
Q

T/F All variables will have both a theoretical + operational def’n in Qn research?

A

• All var’s will have operational def’n, but not all will have theoretical (ex” gender, weight, SpO2)

40
Q

Do concrete variables like gender need an operationalized def’n?

A
  • Even concrete var’s need operationalized def’n as something like gender could be measured in many ways (ex: by chromosomes, by physical characteristic, by self report)
  • Physiologic var’s need to specify how they were measured
41
Q

Systematic observation

How is this different from observation in Ql resrach?

A

• diff than Ql observation in that is structured + defined to ensure each measurement is accurate + comparable to earlier or later measures. Has narrow focus…not looking for broad observation.

42
Q

What is an INSTRUMENT in Qn data collection?

A
  • refers to device that specifies (describes) + objectifies (clarifies) the data collecting process. Usually are written and may be given directly to the subject to collect data (ex: Structured Clinical Interview for DSM-IV; written instructions for a focused observation of behaviors indicating pain)
  • Not attempting to expand understanding of a variable but rather count its occurrence or the extent to which it’s present
43
Q

What kinds of questions are used in Qn reserach?

A

semistructured questions (specifically targets factors of interest) + structured questions (provide measureable choices of answers to questions)

44
Q

Examples of instruments?

A

Questionnaires + scales

45
Q

What are scales?

Items?

A

set of written questions or statements that, in combination, are intended to measure a specific variable; questions included here are often called items

o Example of an item would be: how often to you wake up in the night because of your pain? Then will ask other questions about pain. These together comprise a scale.

46
Q

How are items of a scale developed?

A

o Developing the items for a scale may require looking at previous research on the subject, theory related to the concept, experts’ knowledge of the subject (by having them review your list of items) + then testing the items on a small group first

47
Q

How are items on a scale measured?

What is a Likert-type response scale?

A

• Could ask yes/no.

2nd approach is Likert-type response scale = asks for rating of items on continuum that is anchored at either end by opposite responses (ex: asking for frequency, from “always” to “never”. Could also assign #’s to responses…so “daily” is rated as 2, etc). Usually has 4-6 responses. Scores for all items tallied to get total score (such as for overall stress)

48
Q

What is a visual analog scale?

A

straight length of specific length that has extremes to responses at each end but no other responses noted at points along it. Subjects asked to mark on line where they fit. Usually 100mm long, response is scaled on 1-100.

49
Q

2 general errors in Qn research data collection?

A

• 1) in quality of measures used to collect data 2) in implementation o those measures or the data collection process itself – these two areas of error overlap

50
Q

2 terms used to identify the quality of measures used in Qn research?

A

Validity

Reliability

51
Q

What question is asked about reliability? What is it? When does it get harder to ensure?

A

How consistently does the instrument, questionnaire, or procedure measure what it measures?

= measure will give consistent result if actual or “real” measure stays the same. Becomes more difficult to ensure as measurement process becomes complicated.

52
Q

When collecting Qn data by observation, how can you help ensure reliability of the results?

A

o can help ensure reliability by training the observants & having them practice until accuracy is established.

53
Q

What is an interrater reliability score?

A

o If by observation, researcher also often report interrater reliability score (Interrater reliability is present when two or more indep data collectors agree in the results of their data collection process. (ex: if have 97% interrater score, means 97% of the time, two raters got the same score)

54
Q

2 ways of ensuring reliability when using a questionnaire or scale?

A

1) TEST-RETEST RELIABILITY: Have people do questionnaire two times in short time period in which you would not expect their answers to change . Differences seen will likely be d/t lack of reliability of measure, rather than actual differences
2) By calculating an alpha-coefficient = stat that reflects computation of how closely the answers to different questions or items within a scale are

55
Q

How do you interpret an alpha coefficient?

A

a. Alpha coefficient ranges from 0-1.0. If 0, indicates there are absolutely no relationships among the responses to different items on a scale. 1.0 means answers all completely connected. In general, researchers hope for >0.7. (as I understand it, you want this high number because it means your items are in fact very related and therefore applicable to the questions at hand + reflective of the aspects you’re trying to study).
(I think we won’t need to know this..)

56
Q

What is validity w regard to measure in a Qn study?

A

how accurately the measure yields info about the true or real variable being studies. Is valid if it correctly measures what it’s supposed to measure.

57
Q

When does ensuring validity become more difficult in Qn reserach?

A

o As variables become less concrete or open to interpretation, have more issues with validity – ex: a category like “Native American” may be interpreted differently by different people. Or measures used to look at anxiety might actually more accurately be looking at depression.
o If talking about abstract concepts like anxiety, motivation etc must ensure have questions that avoid confusion w other concepts

58
Q

3 types of validity described in a reports:

A

1) Content validity
2) Criterion-related validity
3) Construct validity

59
Q

What is content validity?

Face validity?

A

= are the items or questions on a scale comprehensive + appropriate reflect the concept they are supposed to measure?

a. Will get expert’s on the subject to determine this.
b. Face validity of a measure is one person’s (not necessarily expert) judgement of this

60
Q

What is criterion-related validity?

A
  1. = extent to which results of one measure match those of another measure that is also supposed to reflect the variable under study. Do the results from the scale r/t to a known criterion relevant to the variable?
    a. Ex: could test results from scale on stress levels with getting subjects to simply report stress on scale of 1-100 and see if results line up.
61
Q

Is criterion-related validity predictive or concurrent?

A

a. Can either be predictive or concurrent (as in, refers to how closely the results on the measure in question r/t results on other measures of the same concept in the present or future)
Predictive validity = looks for relationship between the scale being tested + some measure that should be closely that occurs in the future.

62
Q

What is construct validity?

A
  1. = the extent to which a scale or instrument measures what it is supposed to measure. broadest type of validity. Can encompass both content + criterion related validity.
    a. The contruct validity of a scale or insutrment is supported w time if results using the measure support theory about how the construct (variable) being measured is supposed to behave
    b. The goal here is to build evidence that the construct or abstract variable is being measured by the scale
63
Q

Can a scale be reliable but not valid? Can be valid but not reliable?

A

• A scale can be reliable but not valid. It CANNOT be valid and not reliable, however. That is, is can consistently measure something but not the something it’s supposed to measure. If it measures what it’s supposed to measure, it will inherently also be consistent (reliable).
o This also means a scale must be reliable in order for it to be valid.

64
Q

Can a measure that is valid and reliable still lead to error?

A
  • Even a reliable and valid measure can be implemented improperly and lead to error
  • Ex) researcher is using taking blood glucose w reliable measure but fails to understand dietary restrictions or fasting prior to the test
65
Q

Ways that errors can arise through data implementation?

A
  • The order that surveys are completed in could influence how people respond
  • Other examples include sloppy handling of data, or misplacing contact info of participants in a longitudinal study, translating the measure into another language (can be problematic), or format of the measure being changed (ex: a question being read to the research subject rather than them reading it may change the meaning/response)
66
Q

3 COMMON ERRORS IN WRITTEN RESPOTS AND OF DATA COLLECTION METHODS

A

1) Most common error that occurs in written reports of research studies is provision of incomplete info –> Need clear operationalized definitions + enough clear info about the study (such as what options were offered to the subjects) to be confident in the conclusions that are drawn
2) failure to organize info in a way that makes it clear
3) failure to reference the source of measures used in the study – need to reference reports of previous studies that indicated the reliability or validity of the measure

N360 RESEARCH (2 decks)

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