Methods for Data Analysis
Content analysis chapter 29
primary focus recorded information and people's relationship with it/focuses on features of recorded information – adopted as a useful information and library science science research technique
Use of content analysis
describes a message pool – identify relationships between message characteristics
infer or predict outcomes or effects of messages/characteristics of message content link to measures of human response/other measures
manifest and latent content
manifest observable and countable
latent content difficult/impossible to count – level of anxiety for example
content analysis concerned only with manifest content – validity may be questionable
Units of analysis
sampling units/recording units
sample from the overall population
recording units – elements of content that are coded, physical, conceptual, temporal
straightforward or not
Sampling senses – all members included
content analysis – obtain a representative sample which can be generalized to the population
Coding scheme development –instruction book and data collection instrument for use in conducting content analysis
first step identify the critical variables you wish to examine/grounded in prior research and theory step two choose or define indicators for those variables/what you will count and code for, often manifest content indicated presence of latent variables – require attention to validity reliability accuracy precision
indicators and content analysis take two forms definition of content characteristics (features it can be counted) and sets of categories or levels to record codes presenting the content of the message
Categories must be exhaustive – cannot be satisfied by other not present cannot determine
categories much be must be mutually exclusive – no recording should belong in more than one category of a particular variable
Multiple coders and inter-coder agreement
strives for objectivity and replicability
given same content, coding scheme and training, and he coders work should result in the same data. High measures of intercoder agreement indicate reliability of the results of the coding process
Analysis of coded data – numerical data to analyze
Computer supported content analysis
may not be appropriate for every content analysis/some better suited to computer supported analysis
Chapter 30 qualitative analysis of content
Content analysis primarily used in ILS as quantitative research method until recently/current studies use qualitative content analysis defined as
research method for the subjective interpretation of the content of text data through the systematic classification process of coding identifying themes or patterns
An approach of empirical methodical controlled analysis of text within the context of communication, following content analytic rules and step-by-step models, without rash quantification
any qualitative data reduction and sense making effort that takes a volume a qualitative material and attempts to identify core consistencies and meanings
Inductive versus deductive
qualitative content analysis – condense raw data into categories or themes based on valid inference and interpretation new paragraph three approaches to qualitative content analysis
conventional qualitative content analysis – coding categories derived directly in and deductively from the raw data
directed content analysis – coding starts with a theory or relevant research findings, researchers immerse themselves in the data and allow things to emerge
summative content analysis – starts with counting of words or manifest content/extensive analysis to include latent meanings and themes
Process of qualitative content analysis move back and forth between concept development and data collection
Involves a set of said systematic and transparent procedures for processing data
step one prepare the data
step to define the unit of analysis
step three develop categories in the coding scheme
step four tester coding scheme on a sample of text
step five code all the text
step six assess your coding consistency
step seven draw conclusions from the coded data
step eight reports your methods and findings
Computer support for qualitative content analysis
supported by computer programs such as NV IVO, AATLAS.TI
common purpose – assist researchers in organizing managing encoding qualitative data in an efficient manner
text editing, note and memo taking, coding, text retrieval and node/category manipulation
in corpse visual presentation module – see relationships between categories more vividly
recorded coding history to allow researchers to keep track of evolution of their interpretations
Trustworthiness
validity reliability and objective is to be our used to evaluate the quality of research in the conventional cost of the test research paradigm
credibility
adequate representation of constructions of the social world understudy
transferability – extent to which the researchers working hypothesis can be applied to another context
dependability – coherence of the internal process and the way the researcher calls for changing conditions of the phenomena
Qantas qualitative content analysis is a valuable alternative to more traditional quantitative content analysis – working in interpretive paradigm
identify important themes or categories within a body of content and provide a rich description
Chapter 31 discourse analysis
used to uncover other meanings that we negotiate in our everyday in professional interactions but are rarely made explicit within those interactions – analysis of discourse
Methods and tools – no analytic method at least is a term is understood elsewhere in social psychology – broad theoretical framework – attention on the constructive and functional dimensions of discourse
Instead of using scales or counting occurrences includes the works of theorists within ILS – suggest a number states there will be taken during discourse analysis (not set in stone/sometimes overlapping/completely absent)
First step construct your research question
step two select a sample of the course to study
next step collect records and documents
ready to code data – identifying themes within categories of emerge and take shape as you examine the tax
success predicted on close reading and rereading of the text
looking for nuance contradictions areas of vagueness or ambivalence next
final step validate your findings
Discourse analysis – strong proof/potential to explore passive inquiry to provide greater understanding/required rigor clarity and self inquiry
Chapter 32 analytic induction
induction is one of the main forms of scientific logic, best understood in contrast with other main form of scientific logic: deduction
deductive reasoning – logical process of arriving at specific facts it must be true given a broader sense that seem to be true
induction – logical process of arriving at a general conclusion by a examining set of specific backs
analytic induction – specific form of inductive reasoning to analyze qualitative data
Process of analytic induction
first formulate a rough description of the phenomena you want to explain
next develop a hypothetical explanation of the phenomena
derive your initial definition and hypothesis continue choosing cases and studying them to see if emergency of information is consistent with hypothesis
examination of cases
Challenges in using analytic induction
when to stop examining more cases/no were all negative cases may be lurking
analytic induction has been used relatively infrequently especially in ILS
Analytic induction is an analysis methods not been used in ILS studies yet – holds promise for application to research questions/often working with qualitative data
Chapter 33 descriptive statistics
describing a phenomena with statistics
variables and their levels of measurement
measures of central tendency
mean
median
mode
Choose which method of central tendency to use
Measures of dispersion
range and interquartile range
variance and standard deviation
confidence intervals
Summarize with simple statistics – include at least one measure of central tendency and one manner measure of dispersion to interpret findings to draw valid conclusions from the data
Friday basic charts and graphs for frequency distributions
Descriptive statistics are the most essential view of your study findings critical components of any report of any research
for each variable of interest you should report with a measure of dig central tendency (mean median or mode)
measure of dispersion (standard deviation, range or interquartile range)
starting point for understanding your findings
Chapter 34 frequencies cross tabulation and the chai – square statistic
frequency distributions
counts can be organized and displayed in a table referred to as the frequency distribution
two-way frequency distributions: cross tabulation tables – displays frequency of categories of a single variable
chai square statistic – test with the frequency distribution is likely to occur by chance
Visualizations of your data set
pie charts
bar charts
histograms
line graphs
box plots
lying with graphs
Worthwhile to report descriptive statistics on his findings included frequency distributions on key category of variables
reported in tables – frequency and relative frequency in each cell
row and column totals should be displayed
chai square statistics used to test statistical significance possible relationship between variables in a two-way table
graphical display of the frequency
clearly understand the study results and communicate to the audience
Chapter 35 analyzing sequence he of events
behaviors happen over time
coding the data
approaches to sequential event analysis
Mark of models: examining sequences step by step
optimal matching approaches: comparing whole sequences
visualizing sequences of events
Advantages and event to disadvantages to each – may go beyond your level of expertise
before making a decision about which approach to use in your study, examine that analysis of sequences of search moves
Sequence of events, not enough to know which occur most frequently – need to know the sequence of those actions and which sequences occur most frequently
step-by-step approach using simple first order Markov models and transition matrix, look for frequently occurring longer sequences, pattern analysis, similarity between sequences through optimal matching algorithms
Chapter 36 correlation
statistical analysis method that helps to examine the relationship between two variables
proportion of the variability in one variable explained in the variability of the other variable
if perfectly correlated explains variability
two variables not correlated – none of one is explained by the other
linearly really related and data are ordinal interval or ratio data
Selecting the appropriate correlation statistic
some cautions for interpreting the correlation coefficient
Many research questions are oriented to discovering whether there is a relationship between two variables, how strong the relationship is
whether one variable in some cases causes the other
correlation cannot directly address – provide evidence of a relationship between the two
Chapter 37 comparing means: T tests and analysis of variance
compare different groups in terms of the mean ( average)
two statistical techniques – the t-test and an alley analysis of variance ANOBA
comparing two groups the t-test – probability a difference between two means
interpreting the results of the t-test
statistically significant results
nonsignificant results
Comparing multiple groups
more than two groups to compare
interpreting the results
Many research questions in information and library science of all making comparisons – comparing the effectiveness/performance
calculated amine for each group or entity you want to compare
variability across different samples and within each sample
likelihood indifference hold true across entire population
examine the size of the difference
Chapter 38 putting it all together conclusion
starting over from the beginning
some examples of how study can be put together financial winding your way through this book
providing evidence to promote progress in chapter information and library science
Friday, April 14, 2017
Unit 7 - Data Analysis Techniques / Connaway & Powell, Chapter 9
Analysis of Data
"A knowledge of basic statistics is imperative to research producer and consumer in library science
statistical analysis is concerned with development and application of methods and techniques for organizing and analyzing data quantitatively so that the reliability of conclusions based on the data may be evaluated objectively in terms of probability" – page 261
Types of statistics – theoretical/applied
Role of statistics – four basic purposes
1. – statistics show central point around which a mass of data revolves
2. – show how broad or diverse the spread can be in a mass of data
3. – reveal how closely or distantly certain features within the mass of data are related/relationship?
4. – degree to which the facts have been incurred by mere chance/probability question mark influenced by some factor other than pure chance
Cautions – cannot interpret
necessary for studies involving quantitative data/crucial at the sampling and analysis stage
study should not be dictated by the statistical techniques to follow
Steps involved in statistical analysis
establishment of categories take place before the data are gathered
four basic rules or guidelines
1. set of categories or values – derived from a single classificatory principle
2. each set of category should be exhaustive – possible to place every observation in one of categories of the set
3. each set category should be mutually exclusive – not possible to place a specific observation correctly and more than one category
4. based on sound knowledge of the site of subject matter and anticipation of likely responses
Coding the data
categories have been established and data assigned to them – necessary to convert the new data responses to numerical codes – tabulated or tallied
original responses numerical? – Do not need to be assigned new codes
Important consideration: Reliability / poorly worded question near item may not produce enough relevant information for the purposes of the study
asked more than one question
problems with the categories may lead to lack of reliability
important that coders are adequately trained and important to verify or check the accuracy of their work
researchers transcribed observations or scores for each individual or case
from the data collection to coding or transfer sheets
analyze the data manually
if large mass of data/statistical techniques to be employed were relatively complex and time-consuming to conduct – use a computer to analyze the data
After data has been prepared and saved to electronic file – analyzed using a software program
Analyzing the data date – descriptive statistics
descriptive statistics – predominant type of data analysis employed by researchers in library and information science
six basic functions
1. indicate how many persons objects scores or whatever achieved each value – frequency distributions/reported in tables
2. difficult to grasp the overall meaning of a frequency distribution tables? Pictorial representations – pretrip variety of characteristics of cases or individuals
indent paragraphs charts pie charts histograms frequency polygons
graphs are useful with a large number of cases
3. – typical – recent measures of central tendency
mean median mode
4. how widely the cases in a group very
dispersion or variability
mean aviation – arithmetic mean of the absolute difference of each score from that mean
standard deviation – square root of the arithmetic mean of the square deviations from the mean
variance – the mean squared aviation
standard deviation is one of the most frequently used measures of dispersion – most difficult to comprehend
5. measure the relationship – correlation or associational statistics
cross tabulation or by via direct frequency – products of tables in which two variables have been cross classified – table consists of rows and columns of one variable labels for the rows of another variable
finally compare percentage differences
6. basic function – descriptive statistics can perform is to describe the difference between two or more groups of individuals
special case – showing the relationship between variables
measures of central tendency
Analyzing the data – inferential statistics
"A knowledge of basic statistics is imperative to research producer and consumer in library science
statistical analysis is concerned with development and application of methods and techniques for organizing and analyzing data quantitatively so that the reliability of conclusions based on the data may be evaluated objectively in terms of probability" – page 261
Types of statistics – theoretical/applied
- theoretical equal mathematical aspects of statistics
- applied involves the practical application of statistics
Role of statistics – four basic purposes
1. – statistics show central point around which a mass of data revolves
2. – show how broad or diverse the spread can be in a mass of data
3. – reveal how closely or distantly certain features within the mass of data are related/relationship?
4. – degree to which the facts have been incurred by mere chance/probability question mark influenced by some factor other than pure chance
Cautions – cannot interpret
necessary for studies involving quantitative data/crucial at the sampling and analysis stage
study should not be dictated by the statistical techniques to follow
Steps involved in statistical analysis
establishment of categories take place before the data are gathered
four basic rules or guidelines
1. set of categories or values – derived from a single classificatory principle
2. each set of category should be exhaustive – possible to place every observation in one of categories of the set
3. each set category should be mutually exclusive – not possible to place a specific observation correctly and more than one category
4. based on sound knowledge of the site of subject matter and anticipation of likely responses
Coding the data
categories have been established and data assigned to them – necessary to convert the new data responses to numerical codes – tabulated or tallied
original responses numerical? – Do not need to be assigned new codes
Important consideration: Reliability / poorly worded question near item may not produce enough relevant information for the purposes of the study
asked more than one question
problems with the categories may lead to lack of reliability
important that coders are adequately trained and important to verify or check the accuracy of their work
researchers transcribed observations or scores for each individual or case
from the data collection to coding or transfer sheets
analyze the data manually
if large mass of data/statistical techniques to be employed were relatively complex and time-consuming to conduct – use a computer to analyze the data
- Optical scanning for producing data flow files
- direct data entry may not be the most common technique for creating data files
- computer program displays each question on the screen
- prompts the researcher to input the response directly on the screen – and into the computer file
- database management program to control data entry
- statistical programs now enable downloading a computer datafile rent tearing data directly into the program using the data editor
- program prompts the person entry data for each response
- checks the response to ensure its valid
- phase response of the property to file
After data has been prepared and saved to electronic file – analyzed using a software program
Analyzing the data date – descriptive statistics
descriptive statistics – predominant type of data analysis employed by researchers in library and information science
six basic functions
1. indicate how many persons objects scores or whatever achieved each value – frequency distributions/reported in tables
2. difficult to grasp the overall meaning of a frequency distribution tables? Pictorial representations – pretrip variety of characteristics of cases or individuals
indent paragraphs charts pie charts histograms frequency polygons
graphs are useful with a large number of cases
3. – typical – recent measures of central tendency
mean median mode
4. how widely the cases in a group very
dispersion or variability
mean aviation – arithmetic mean of the absolute difference of each score from that mean
standard deviation – square root of the arithmetic mean of the square deviations from the mean
variance – the mean squared aviation
standard deviation is one of the most frequently used measures of dispersion – most difficult to comprehend
5. measure the relationship – correlation or associational statistics
cross tabulation or by via direct frequency – products of tables in which two variables have been cross classified – table consists of rows and columns of one variable labels for the rows of another variable
finally compare percentage differences
6. basic function – descriptive statistics can perform is to describe the difference between two or more groups of individuals
special case – showing the relationship between variables
measures of central tendency
Analyzing the data – inferential statistics
Sunday, April 9, 2017
Unit 6 Data Collection Methods / Wildemuth 18-28
Wildemuth Chapters 18-28 Methods for Data Collection
Chapter 18 – transaction logs
Chapter 19 – think aloud protocols
Chapter 20 - direct observation
Chapter 21 - participant observation
Chapter 22 - research diaries
Chapter 23 - unstructured interviews
Chapter 24 - semi structured interviews
Chapter 25 - focus groups
Chapter 26 - survey research
Chapter 27 - measuring cognitive and affective variables
Chapter 28 - developing new measures
Chapter 18 – transaction logs
Chapter 19 – think aloud protocols
Chapter 20 - direct observation
Chapter 21 - participant observation
Chapter 22 - research diaries
Chapter 23 - unstructured interviews
Chapter 24 - semi structured interviews
Chapter 25 - focus groups
Chapter 26 - survey research
Chapter 27 - measuring cognitive and affective variables
Chapter 28 - developing new measures
Unit 6 Data Collection Techniques / Connaway & Powell, Chapter 5 Data Collection Techniques
Connaway & Powell, Chapter 5 Data Collection Techniques
Wildemuth Chapters 18-28 Methods for Data Collection
There are different types of questionnaires
factual
opinion
information
self perception
many more
Interviews –
structured
unstructured
semi structured
Considerations for good design of questionnaires and interviews
Scaled responses:
Qualities of good interviewing and good interviewers
Difference between individual interviews and focus group interviews
When appropriate to use questionnaires rather than interviews
appropriate to use interviews rather than questionnaires
research study
Different types of observational research
Usability testing at an introductory level
Transaction logs and their analysis
Think aloud protocols
specific examples
Research diaries/specific examples
Advantages and disadvantages of each technique
Measure cognitive and affective variables
describe specific examples of written and edited introductory level
Wildemuth Chapters 18-28 Methods for Data Collection
There are different types of questionnaires
factual
opinion
information
self perception
many more
Interviews –
structured
unstructured
semi structured
Considerations for good design of questionnaires and interviews
Scaled responses:
Qualities of good interviewing and good interviewers
Difference between individual interviews and focus group interviews
When appropriate to use questionnaires rather than interviews
appropriate to use interviews rather than questionnaires
research study
Different types of observational research
Usability testing at an introductory level
Transaction logs and their analysis
Think aloud protocols
specific examples
Research diaries/specific examples
Advantages and disadvantages of each technique
Measure cognitive and affective variables
describe specific examples of written and edited introductory level
Tuesday, March 28, 2017
Unit 5 Research Designs and Sampling, Chapter 13 Sampling for Extensive Studies
Wildemuth chapter 13
sampling for extensive studies – conclusion to draw from your research will hold apply to a particular set of people or organizations
a sample was collected to study and plan to generalize your findings to all the people organizations representative sample
practice of sampling is intended to achieve greater efficiency/if done properly results in a more accurate findings because you can control your data collection procedures and other aspects of the procedure preferably
– Design your sampling procedures carefully/the selection of particular people organizations to participate will these does set of rules that reply to a carefully defined population
Went to selected a probability sample from the population of interest it needs to be reviewed subsequently
probability sampling: concepts and definitions: the population is the collection of elements but which we wish to make an inference – an element is an object in which a measurement is taken
the sampling frame is a list of all agile eligible elements in the population
Probability sample has two specific characteristics first every element of the fibrillation of interest has a known nonzero probability of being selected into the sample
the second important characteristic elements be selected randomly at some point in the process
does not mean haphazard selection Newmont line selection based on the theory of probability that will help you avoid bias in your sample
Procedures for selecting a probability sample
first define your population of interest then define more carefully those who have you something within the past year three times within the year different definition I fear population of interest will depend on your goal
Compromise the definition of the study population to make your sampling plan possible to carry out
define your target population and actual study population
Unit of analysis – could be individuals or could be groups or organizations that unit of analysis you are making clear which elements are eligible for inclusion
Identify or construct the sampling frame – list of elements in the study of population
matches the definition of your study population – missing some portion?
May be complete the contact information or inaccurate or duplications this list is flawed – you will be implicitly redefining your population
Finally select particular animal elements from the sampling frame – identifying specific people – probability sample you use
simple random sampling single sampling frame all the elements in it will have equal chance of being selected, generate a set of random numbers, work through the random numbers and to get the sample size you need
Systematic sampling
extremely tedious and error-prone instead use systematic sampling – random numbers chosen to select the first element from their every and element is selected and varies based on the size of the sample
should not be used if the frames order has some type of pattern it could introduce bias into the sample
Stratified sampling
population is divided into strata 1st/elements a random list sampled from each stratum – may be possible to use a smaller's sample size take your research more efficient
Define the strata – selective variable to divide your population
second decide in the number of strata
finally decide in the sample size
Cluster sampling
sampling units are groups begin the sampling procedure by randomly selecting a sample of clusters
in single stage cluster sampling you would then include in your sample all the elements within the selected clusters
minimize the cost of collecting data from a geographically widespread sample
Nonprobability sampling: concepts and definitions
Not possible to select a random sample from the population which interested? Not possible to identify create a valid sampling frame? Some type of nonprobability sample will need to be defined
Quota sampling
quota for each level of each characteristic/recruiting people until you have met your quota – resemble stratified sampling/no random selection of elements
Purposeful purposive sampling
Judgment sample relies on expert judgment of the person selecting the sample
based on their individual characteristics, a sample that is representative of the population in terms of both central tendency in range of characteristics – include both a typical staff member and more extreme cases/potential for bias
Snowball sampling
particularly sensitive or when eligible belt members may be particularly difficult to identify – ask each participant for suggestions for additional people
forward to five more people
Convenient sampling
try to recruit people because they are available
characteristics of the people who are eligible for study participation
Sample size
as efficient as possible – recruit and collect data from a small sample as possible
don't use a standard method parens (local tradition, at least enough, data availability, intuition, experience, negotiations) – confidence interval around the estimate – area can tolerate any parameter estimates – no fix criteria for setting acceptable confidence interval balance the importance of the accuracy
Special case usability testing special case five or six users can identify 80% of the usability problems homes there are – larger sample size significant portion of the usability problems
The effects of nonresponse in a sample
some of the participants you selected will not accept your invitation response rates only 63% – oversample to increase the number/will not necessary help avoided by a sample
Use appropriate data/use wording/well-designed questionnaires are study procedures = gift certificates cash other items
sampling for extensive studies – conclusion to draw from your research will hold apply to a particular set of people or organizations
a sample was collected to study and plan to generalize your findings to all the people organizations representative sample
practice of sampling is intended to achieve greater efficiency/if done properly results in a more accurate findings because you can control your data collection procedures and other aspects of the procedure preferably
– Design your sampling procedures carefully/the selection of particular people organizations to participate will these does set of rules that reply to a carefully defined population
Went to selected a probability sample from the population of interest it needs to be reviewed subsequently
probability sampling: concepts and definitions: the population is the collection of elements but which we wish to make an inference – an element is an object in which a measurement is taken
the sampling frame is a list of all agile eligible elements in the population
Probability sample has two specific characteristics first every element of the fibrillation of interest has a known nonzero probability of being selected into the sample
the second important characteristic elements be selected randomly at some point in the process
does not mean haphazard selection Newmont line selection based on the theory of probability that will help you avoid bias in your sample
Procedures for selecting a probability sample
first define your population of interest then define more carefully those who have you something within the past year three times within the year different definition I fear population of interest will depend on your goal
Compromise the definition of the study population to make your sampling plan possible to carry out
define your target population and actual study population
Unit of analysis – could be individuals or could be groups or organizations that unit of analysis you are making clear which elements are eligible for inclusion
Identify or construct the sampling frame – list of elements in the study of population
matches the definition of your study population – missing some portion?
May be complete the contact information or inaccurate or duplications this list is flawed – you will be implicitly redefining your population
Finally select particular animal elements from the sampling frame – identifying specific people – probability sample you use
simple random sampling single sampling frame all the elements in it will have equal chance of being selected, generate a set of random numbers, work through the random numbers and to get the sample size you need
Systematic sampling
extremely tedious and error-prone instead use systematic sampling – random numbers chosen to select the first element from their every and element is selected and varies based on the size of the sample
should not be used if the frames order has some type of pattern it could introduce bias into the sample
Stratified sampling
population is divided into strata 1st/elements a random list sampled from each stratum – may be possible to use a smaller's sample size take your research more efficient
Define the strata – selective variable to divide your population
second decide in the number of strata
finally decide in the sample size
Cluster sampling
sampling units are groups begin the sampling procedure by randomly selecting a sample of clusters
in single stage cluster sampling you would then include in your sample all the elements within the selected clusters
minimize the cost of collecting data from a geographically widespread sample
Nonprobability sampling: concepts and definitions
Not possible to select a random sample from the population which interested? Not possible to identify create a valid sampling frame? Some type of nonprobability sample will need to be defined
Quota sampling
quota for each level of each characteristic/recruiting people until you have met your quota – resemble stratified sampling/no random selection of elements
Purposeful purposive sampling
Judgment sample relies on expert judgment of the person selecting the sample
based on their individual characteristics, a sample that is representative of the population in terms of both central tendency in range of characteristics – include both a typical staff member and more extreme cases/potential for bias
Snowball sampling
particularly sensitive or when eligible belt members may be particularly difficult to identify – ask each participant for suggestions for additional people
forward to five more people
Convenient sampling
try to recruit people because they are available
characteristics of the people who are eligible for study participation
Sample size
as efficient as possible – recruit and collect data from a small sample as possible
don't use a standard method parens (local tradition, at least enough, data availability, intuition, experience, negotiations) – confidence interval around the estimate – area can tolerate any parameter estimates – no fix criteria for setting acceptable confidence interval balance the importance of the accuracy
Special case usability testing special case five or six users can identify 80% of the usability problems homes there are – larger sample size significant portion of the usability problems
The effects of nonresponse in a sample
some of the participants you selected will not accept your invitation response rates only 63% – oversample to increase the number/will not necessary help avoided by a sample
Use appropriate data/use wording/well-designed questionnaires are study procedures = gift certificates cash other items
Unit 5 Research Designs and Sampling, Chapter 12 experimental studies
Wildemuth chapter 12
variables are manipulated and their effects upon other variables observed – basic definition provides a strong framework for understanding primary characteristics of a to experiment
1st some variables called independent variables are manipulated – you must exert control over their variation to be sure you can understand the effects of their understudy
independent variables are the input to the experiment
2nd of their those effects and other variables (call dependent variables) the dependent variable understood as the output from the experiment
characterized by control – all possibilities for variation are either controlled or they are varied systematically
three experimental designs
pretest protest control group design
posttest only control group design
factorial design
Randomization
the validity of your experiment – threats to internal validity
threats to external validity
Additional issues
experimental setting: lab or field
within versus between subjects designs
ethical issues in experiments
Principles of experimentation are simple: randomly's assigned the subjects to two or more groups and measure the outcomes resulting from the activation experienced age group
move beyond the basic characteristics of peer experiments you find their number of decisions to make about design
crucial that you understand your research question thoroughly and stated clearly
identify which independent variable should be manipulated and which dependency variables should be measured
variables are manipulated and their effects upon other variables observed – basic definition provides a strong framework for understanding primary characteristics of a to experiment
1st some variables called independent variables are manipulated – you must exert control over their variation to be sure you can understand the effects of their understudy
independent variables are the input to the experiment
2nd of their those effects and other variables (call dependent variables) the dependent variable understood as the output from the experiment
characterized by control – all possibilities for variation are either controlled or they are varied systematically
three experimental designs
pretest protest control group design
posttest only control group design
factorial design
Randomization
the validity of your experiment – threats to internal validity
threats to external validity
Additional issues
experimental setting: lab or field
within versus between subjects designs
ethical issues in experiments
Principles of experimentation are simple: randomly's assigned the subjects to two or more groups and measure the outcomes resulting from the activation experienced age group
move beyond the basic characteristics of peer experiments you find their number of decisions to make about design
crucial that you understand your research question thoroughly and stated clearly
identify which independent variable should be manipulated and which dependency variables should be measured
Unit 5 Research Designs and Sampling, Chapter 11 Quasi-experimental Studies
Wildemuth chapter 11
definition used for accessing causal relationships by determining the impact of an intervention – teaching technique, electronic database or collection development Powell's policy or an outcome or effect of interest
Preexperimental designs
true experimental designs
quasiexperimental designs – used in natural setting/some control over the experimental conditions can be exerted you full control is either not possible or not desirable
distinguish first how these differ from those other types of designs – the pre-experiment and the true experiment
Amount of control exerted on next rainiest variables is the primary distinction between cause site experimental studies and true experimental studies
lack of control associated with the absence a random assignment
involve naturally occurring groups
only in true experiments are individual subjects randomly assigned to particular experimental conditions
quasiexperimental methods are designed for settings where utter designs are not feasible
when it's not possible to exert such control – random assignment or some other way/quasiexperimental designs offer an alternative for the researcher
Design lacks complete experimental control/weaker than true experiments
most appropriate approach for many types of studies of interest in information and library science
implemented in a more naturalistic setting – rather more control controlled laboratory setting
increasing ecological validity of the study
attainable in real-world settings
controlled experimentation in a laboratory setting may not translate to success and less controlled natural context
Use of quasiexperimental designs in information library science
Apply to 8 to 10% of research studies in the field of ILS
evidence suggests that two experiments designs are used considerably less frequently than quasiexperimental designs
Typically pragmatic rather than theoretical in nature take the form of applied research
within the framework of action research designs
to actively and directly impact procedures and services with the in a practice setting
research areas include instruction, evaluation, information seeking and professional development
Specific single and multi group designs demonstrate useful designs particularly appropriate for application ILS
Timeseries designs – based on intermittent measurements taken before an exposure to the treatment
within subjects designs
minimum of two data collections points in total encourages use of more than minimum for sufficient opportunities to assess effects of the treatment new paragraph non-equivalent control group design – most likely most frequently apply type also referred to his non-equivalent groups design/pretest posttest not equivalent couple control group design
control comparison study
counterbalance design – multiple treatments are interventions applied to each of the subjects
wrist is design and in interpretation any difference in results can be attributed to the treatment or intervention imposed by the researcher
such as bias and mortality affects – selection bias occurs when the groups being compared are different from each other in some systematic way
history affects – unrelated events associated with the study participants/that is their history during the course of the study
testing affects – pretest of some kind may influence ability ability of your study in two ways first administer close together in time/subject may recall the responses on the pretest to match those or change them for the posttest
second pretest questions may actually condition subjects responses to the intervention causing them to enact interact with it differently than if they had not completed the pretest paren reactive effective testing)
additional threats to be dealt validity
Very useful in situations which make it possible to implement a true experiment/cannot make comparisons/cannot randomly assign research subject to the two groups, plus I experimental design may allow you to address research questions with a balanced combination of rigor and naturalism
definition used for accessing causal relationships by determining the impact of an intervention – teaching technique, electronic database or collection development Powell's policy or an outcome or effect of interest
Preexperimental designs
true experimental designs
quasiexperimental designs – used in natural setting/some control over the experimental conditions can be exerted you full control is either not possible or not desirable
distinguish first how these differ from those other types of designs – the pre-experiment and the true experiment
Amount of control exerted on next rainiest variables is the primary distinction between cause site experimental studies and true experimental studies
lack of control associated with the absence a random assignment
involve naturally occurring groups
only in true experiments are individual subjects randomly assigned to particular experimental conditions
quasiexperimental methods are designed for settings where utter designs are not feasible
when it's not possible to exert such control – random assignment or some other way/quasiexperimental designs offer an alternative for the researcher
Design lacks complete experimental control/weaker than true experiments
most appropriate approach for many types of studies of interest in information and library science
implemented in a more naturalistic setting – rather more control controlled laboratory setting
increasing ecological validity of the study
attainable in real-world settings
controlled experimentation in a laboratory setting may not translate to success and less controlled natural context
Use of quasiexperimental designs in information library science
Apply to 8 to 10% of research studies in the field of ILS
evidence suggests that two experiments designs are used considerably less frequently than quasiexperimental designs
Typically pragmatic rather than theoretical in nature take the form of applied research
within the framework of action research designs
to actively and directly impact procedures and services with the in a practice setting
research areas include instruction, evaluation, information seeking and professional development
Specific single and multi group designs demonstrate useful designs particularly appropriate for application ILS
Timeseries designs – based on intermittent measurements taken before an exposure to the treatment
within subjects designs
minimum of two data collections points in total encourages use of more than minimum for sufficient opportunities to assess effects of the treatment new paragraph non-equivalent control group design – most likely most frequently apply type also referred to his non-equivalent groups design/pretest posttest not equivalent couple control group design
control comparison study
counterbalance design – multiple treatments are interventions applied to each of the subjects
wrist is design and in interpretation any difference in results can be attributed to the treatment or intervention imposed by the researcher
such as bias and mortality affects – selection bias occurs when the groups being compared are different from each other in some systematic way
history affects – unrelated events associated with the study participants/that is their history during the course of the study
testing affects – pretest of some kind may influence ability ability of your study in two ways first administer close together in time/subject may recall the responses on the pretest to match those or change them for the posttest
second pretest questions may actually condition subjects responses to the intervention causing them to enact interact with it differently than if they had not completed the pretest paren reactive effective testing)
additional threats to be dealt validity
Very useful in situations which make it possible to implement a true experiment/cannot make comparisons/cannot randomly assign research subject to the two groups, plus I experimental design may allow you to address research questions with a balanced combination of rigor and naturalism
Unit 5 Research Designs and Sampling, Chapter 10 Delphi Studies
Wildemuth chapter 10
Oracle: something that is foretold by or as if by supernatural means: divination, prophecy, soothsaying, fitness a nation, vision from rosin budgets to the new thesauruses third edition
Introduction definition name for this Oracle because primarily used in forecasting future events based on the opinion of experts
technique for gleaning and refining the subject of input from a group of people, usually experts, in an attempt to achieve consistence consensus about some aspect of the present or the future
Method for structuring a group communication process so that the process is effective in allowing a group of individuals as a whole to deal with the complex problem
some assessment of the group judgment or view/some opportunity for individual to rape revise views and some degree of anonymity for the individual responses
Not used outside the defense community and no till 1964
forecasting future events has been the major application/versatile research tool that can be used in a bright variety of situations
Characteristics:
allows more people to participate in the study that can effectively interact face-to-face – controls the communication flow and helps the group to stay focused
allows participants from a diverse background and with diverse or opposing views to interact without being concerned about reactions
influences of dominant individuals are avoided – strength of personality eloquence and other personal influential factors much less physical visible because of anonymity
group pressure for conformity is avoided – participants exempted from the direct pressure to conform to majority opinions
effects of feelings and information communicated through body language – tone of voice, gestures, or local buyer minimized./Without seeing each other face-to-face not affected by nonverbal communications
timing costs are reduced – frequent face-to-face meetings are costly and time-consuming/less time and costs are needed
Criticism: net
lack of statistical test
lack of democratic demographic description of participants
selection of experts
lack of exploratory explanatory quality
Degree of Anna and Deputy
Things to avoid
imposing your own preconceptions
in a adequately summarizing and presenting group response
not ensuring common interpretation of the evaluation scales
ignoring rather than exploring disagreements – discouraged it dissenters dropout and artificial consensus is generated
underestimating demand nature of a multi-round Delphi study and not properly compensating the participants
ignoring missed understandings that may arise from differences in language and logic – participants come from diverse cultural backgrounds
Most appropriate situations with the truth of the matter cannot be known to direct observation
leverage the expertise of people who are thought about a particular issue or problem – seeking to find consensus
careful selection of panelists and iterative surveying of other opinions with feedback
Oracle: something that is foretold by or as if by supernatural means: divination, prophecy, soothsaying, fitness a nation, vision from rosin budgets to the new thesauruses third edition
Introduction definition name for this Oracle because primarily used in forecasting future events based on the opinion of experts
technique for gleaning and refining the subject of input from a group of people, usually experts, in an attempt to achieve consistence consensus about some aspect of the present or the future
Method for structuring a group communication process so that the process is effective in allowing a group of individuals as a whole to deal with the complex problem
some assessment of the group judgment or view/some opportunity for individual to rape revise views and some degree of anonymity for the individual responses
Not used outside the defense community and no till 1964
forecasting future events has been the major application/versatile research tool that can be used in a bright variety of situations
Characteristics:
allows more people to participate in the study that can effectively interact face-to-face – controls the communication flow and helps the group to stay focused
allows participants from a diverse background and with diverse or opposing views to interact without being concerned about reactions
influences of dominant individuals are avoided – strength of personality eloquence and other personal influential factors much less physical visible because of anonymity
group pressure for conformity is avoided – participants exempted from the direct pressure to conform to majority opinions
effects of feelings and information communicated through body language – tone of voice, gestures, or local buyer minimized./Without seeing each other face-to-face not affected by nonverbal communications
timing costs are reduced – frequent face-to-face meetings are costly and time-consuming/less time and costs are needed
Criticism: net
lack of statistical test
lack of democratic demographic description of participants
selection of experts
lack of exploratory explanatory quality
Degree of Anna and Deputy
Things to avoid
imposing your own preconceptions
in a adequately summarizing and presenting group response
not ensuring common interpretation of the evaluation scales
ignoring rather than exploring disagreements – discouraged it dissenters dropout and artificial consensus is generated
underestimating demand nature of a multi-round Delphi study and not properly compensating the participants
ignoring missed understandings that may arise from differences in language and logic – participants come from diverse cultural backgrounds
Most appropriate situations with the truth of the matter cannot be known to direct observation
leverage the expertise of people who are thought about a particular issue or problem – seeking to find consensus
careful selection of panelists and iterative surveying of other opinions with feedback
Friday, March 17, 2017
Unit 5 Research Designs and Sampling, Chapter 9 Longitudinal Studies
Wildemuth chapter 9
Scope: process that occurs over time, observe it over time to understand more fully
behaviors change as the searchers learn more
cannot be answered with a static description and Association
Longitudinal research refers to a whole family of different research designs including repeated cross-sectional studies, not necessarily including the same study per Spence
Prospective panel designs
a) data are collected for each item or variable for two or more distinct time periods
be) the subjects or cases analyzed are the same or least comparable from once. To the next
C) the analysis involves some comparison of data between or among periods
Two or more participant panels may be included to improve the robustness of the design
data are collected at least three occasions so that trajectory of change can be observed
Advantages: comparing strengths and weaknesses to those of cross-sectional research designs
first: longitudinal research can examine changes or other processes that occur over time within individuals/examine the ways in each participant has changed from one time to the next/can examine the duration of particular episodes or phenomena
Second: basis for drawing conclusions about cause-and-effect/3 important criteria
the two of burials must cover very, this relationship between the must not be attributable to any other cause, and the variable to be believed to be the cause must precede or be simultaneous with the effect
researchers interested in making the strongest case that one variable causes another should consider a large and new to know study design.
Data collection and analysis in longitudinal studies
interviews, questionnaires or other types of measures to asked the participants
collect data from existing records
only rule of thumb is equitable data must be gathered over multiple occasions
make comparisons based on those data
Plan for three times you will collected and how they will be timed
many things, both theoretical and practical. Lifecycle of the process you are trying to observe and how quickly it occurs
Challenges: challenges associated with your sampling attrition from your sample, difficulty of measuring the same variable on each occasion and the effects of extraneous events (including your study procedures) on the phenomenon being absurd
plan for the study will need to conduct when your original plans don't work
anticipate the challenges you might face
Goal is to understand on the individual level phenomenon occurs over time
hold on to all your study participants over the course the study
methods for measuring the variables of interest – can be compared with each other
anticipate extraneous events that could have a biasing effect.
Scope: process that occurs over time, observe it over time to understand more fully
behaviors change as the searchers learn more
cannot be answered with a static description and Association
Longitudinal research refers to a whole family of different research designs including repeated cross-sectional studies, not necessarily including the same study per Spence
Prospective panel designs
a) data are collected for each item or variable for two or more distinct time periods
be) the subjects or cases analyzed are the same or least comparable from once. To the next
C) the analysis involves some comparison of data between or among periods
Two or more participant panels may be included to improve the robustness of the design
data are collected at least three occasions so that trajectory of change can be observed
Advantages: comparing strengths and weaknesses to those of cross-sectional research designs
first: longitudinal research can examine changes or other processes that occur over time within individuals/examine the ways in each participant has changed from one time to the next/can examine the duration of particular episodes or phenomena
Second: basis for drawing conclusions about cause-and-effect/3 important criteria
the two of burials must cover very, this relationship between the must not be attributable to any other cause, and the variable to be believed to be the cause must precede or be simultaneous with the effect
researchers interested in making the strongest case that one variable causes another should consider a large and new to know study design.
Data collection and analysis in longitudinal studies
interviews, questionnaires or other types of measures to asked the participants
collect data from existing records
only rule of thumb is equitable data must be gathered over multiple occasions
make comparisons based on those data
Plan for three times you will collected and how they will be timed
many things, both theoretical and practical. Lifecycle of the process you are trying to observe and how quickly it occurs
Challenges: challenges associated with your sampling attrition from your sample, difficulty of measuring the same variable on each occasion and the effects of extraneous events (including your study procedures) on the phenomenon being absurd
plan for the study will need to conduct when your original plans don't work
anticipate the challenges you might face
Goal is to understand on the individual level phenomenon occurs over time
hold on to all your study participants over the course the study
methods for measuring the variables of interest – can be compared with each other
anticipate extraneous events that could have a biasing effect.
Unit 5 Research Designs and Sampling, Chapter 8 Naturalistic Research
Naturalistic research
if you are data that more closely reflects the real lived experiences of the population of interest – naturalistic research (studies that approximate natural, uncontrived conditions) eliminate new areas of behavior – difficult philosophical issues/practical problems.
Challenge the validity
laboratories studies fail to
gather a detailed unprejudiced record of people's behaviors, beliefs and preferences
explore people's behavior in the context of their own work and life new intensively observed particular elements of context such as setting and artifacts
discover the tactic meanings and understandings that are common in communication and social interaction.
Naturalistic research methods study people in their natural environment – going into the field/replicating elements of the natural environment elsewhere
Basic tension – naturalism and positivism (or rationalism) as modes of inquiry
fundamentally different philosophical approach – direct opposition to traditional scientific norms – causualty, reality, generalizability, and objectivity. Acknowledging that research findings are idiographic, reflecting only one view of one environment
Naturalistic inquiry axioms – practical warnings (complex work processes) alert to political and social issues within organizations/ multiple viewpoints are recognized
Doing naturalistic research
first step move from specific research question to design it helps you answer that question – listen in on a number of reference transactions, extensive notes on whether patrons and librarians said and did.
More naturalistic – enable structs ever be observation of people in their natural environments
they have chosen to come to the library make use of it services a part of ordinary life
Identified patterns and themes – relied on signs and other conventions/watch the students struggle/try to guess how
Worthwhile to document recurrent ways in which discrete activities are produced performed and accomplished by members time and time again
can be used to develop better theories and models of behavior, better programs and systems.
Approaches and trade-offs
organized in three main dimensions degree of naturalism, type of insight, and resources required.
Degree of naturalism
high degree of naturalism – situations and interactions he observed were uncontrived and occurred in the context of participants own work and experience
less naturalistic – directly in the community did not engage
Type of insight
specify the types of insight that are particularly relevant to your interest
architecture: people navigate and orient themselves, design your study, on how and when people get confused
fundraising and marketing: how people talk about the library, they relate to its part of professional, personal and social lives, design your study to focus on people's feelings about the library
choose a naturalistic study in situations where you want to gain insight into people's naturally occurring behavior in natural settings
Resources required
can demand vast amounts of time – rich data to be collected and interpreted
wide array of naturalistic techniques developed
lightweight approaches: (rapid assessment techniques) brief and focused forays
full-blown approaches – engage fully with the chosen domain, direct participation, emphasize open-ended exploration and understanding
Specific techniques
numerous decisions: research design, data collection methods, and data analysis methods.
Field observation: researcher is not attempting to become engaged simply an observer
continuous monitoring or sampling
ethnography
detailed in-depth observation of people's behavior, beliefs and preferences – ongoing basis/daily lives next sentence months or years of fieldwork – time to structure interpret and write about
Contextual inquiry
rapidly gathering – use with an information system design projects/support the design process
spend several hours over the course of one or many days with participant of the system being designed new apprentice to the participants master
participant teaches the researcher about the work processes of interest
Cognitive work analysis
like conceptual inquiry, descriptive approach – help us understand how people actually perform the work
the environment, perceptual, cognitive, and ergonomic attributes of people who typically do the task
provided details of the ways – applied cognitive work analysis in the research (naturalistic study carried out within corporate setting) – served as a framework for data collection and analysis
seven dimensions new the environment new to the work to main new organization new line the task and work to main terms
decision-making terms
strategies that can be used
actors resources and values
Quasi-experiments
fixed research design, similar to an experiment, participants are assigned to conditions – in some systematic way
assigning people randomly to particular conditions is basically anti-naturalistic, quasi-experiments are way to introduce naturalistic elements while maintaining some control
Conclusion reveals the fascinating complexity of human behaviors and enables collection of rich visual verbal and physical data
elusiveness of what you want to measure/seeming infinity of techniques
combine richness of naturalistic techniques, rigor of controlled studies and validated instruments.
if you are data that more closely reflects the real lived experiences of the population of interest – naturalistic research (studies that approximate natural, uncontrived conditions) eliminate new areas of behavior – difficult philosophical issues/practical problems.
Challenge the validity
laboratories studies fail to
gather a detailed unprejudiced record of people's behaviors, beliefs and preferences
explore people's behavior in the context of their own work and life new intensively observed particular elements of context such as setting and artifacts
discover the tactic meanings and understandings that are common in communication and social interaction.
Naturalistic research methods study people in their natural environment – going into the field/replicating elements of the natural environment elsewhere
Basic tension – naturalism and positivism (or rationalism) as modes of inquiry
fundamentally different philosophical approach – direct opposition to traditional scientific norms – causualty, reality, generalizability, and objectivity. Acknowledging that research findings are idiographic, reflecting only one view of one environment
Naturalistic inquiry axioms – practical warnings (complex work processes) alert to political and social issues within organizations/ multiple viewpoints are recognized
Doing naturalistic research
first step move from specific research question to design it helps you answer that question – listen in on a number of reference transactions, extensive notes on whether patrons and librarians said and did.
More naturalistic – enable structs ever be observation of people in their natural environments
they have chosen to come to the library make use of it services a part of ordinary life
Identified patterns and themes – relied on signs and other conventions/watch the students struggle/try to guess how
Worthwhile to document recurrent ways in which discrete activities are produced performed and accomplished by members time and time again
can be used to develop better theories and models of behavior, better programs and systems.
Approaches and trade-offs
organized in three main dimensions degree of naturalism, type of insight, and resources required.
Degree of naturalism
high degree of naturalism – situations and interactions he observed were uncontrived and occurred in the context of participants own work and experience
less naturalistic – directly in the community did not engage
Type of insight
specify the types of insight that are particularly relevant to your interest
architecture: people navigate and orient themselves, design your study, on how and when people get confused
fundraising and marketing: how people talk about the library, they relate to its part of professional, personal and social lives, design your study to focus on people's feelings about the library
choose a naturalistic study in situations where you want to gain insight into people's naturally occurring behavior in natural settings
Resources required
can demand vast amounts of time – rich data to be collected and interpreted
wide array of naturalistic techniques developed
lightweight approaches: (rapid assessment techniques) brief and focused forays
full-blown approaches – engage fully with the chosen domain, direct participation, emphasize open-ended exploration and understanding
Specific techniques
numerous decisions: research design, data collection methods, and data analysis methods.
Field observation: researcher is not attempting to become engaged simply an observer
continuous monitoring or sampling
ethnography
detailed in-depth observation of people's behavior, beliefs and preferences – ongoing basis/daily lives next sentence months or years of fieldwork – time to structure interpret and write about
Contextual inquiry
rapidly gathering – use with an information system design projects/support the design process
spend several hours over the course of one or many days with participant of the system being designed new apprentice to the participants master
participant teaches the researcher about the work processes of interest
Cognitive work analysis
like conceptual inquiry, descriptive approach – help us understand how people actually perform the work
the environment, perceptual, cognitive, and ergonomic attributes of people who typically do the task
provided details of the ways – applied cognitive work analysis in the research (naturalistic study carried out within corporate setting) – served as a framework for data collection and analysis
seven dimensions new the environment new to the work to main new organization new line the task and work to main terms
decision-making terms
strategies that can be used
actors resources and values
Quasi-experiments
fixed research design, similar to an experiment, participants are assigned to conditions – in some systematic way
assigning people randomly to particular conditions is basically anti-naturalistic, quasi-experiments are way to introduce naturalistic elements while maintaining some control
Conclusion reveals the fascinating complexity of human behaviors and enables collection of rich visual verbal and physical data
elusiveness of what you want to measure/seeming infinity of techniques
combine richness of naturalistic techniques, rigor of controlled studies and validated instruments.
Thursday, March 16, 2017
Unit 5 Research Designs and Sampling, Chapter 7 - Case Studies
case studies
number of studies employing case studies dramatically
increased 1980s 1990s
1.
the complexity of the unit is studied
intensively
2.
definition: description of a particular
situation or event
serves as a learning tool providing a formal framework for
discussion
research: case study is defined as a reach search study
focused on a single case or set of cases.
Research approach case study
11 characteristics
1 the phenomena is examined in a natural setting
2 data are collected by multiple means
3 one or a few entities (person, group, or organization) are
examined.
4 the complexity of the unit is studied intensively
5 case studies are more suitable for the exploration,
classification, and hypothesis development stages of the knowledge – building
process; the investigator should have a receptive attitude toward exploration
6 no experimental controls or manipulation are involved
7 the investigator may not specify the set of independent
and dependent variables in advance
8 the results derived depend heavily on the integrative
powers of the investigator
9 changes in site selection and data collection methods
could take place as the investigator develops new hypothesis
10 case research is useful in the study of why and how
questions because these deal with operational links to be traced over time
rather than with frequency or incidence new 11 the focus is on contemporary
events
most often case
studies are qualitative and conducted in the field
don’t ignore multimethod aspects of case study research –
evidence collected in case studies may be qualitative or quantitative or both
combination of both types could contribute to validity of method
research procedures should only be changed after careful
consideration because changing could decrease the rigor of the study
when she case studies
be used
1.
does the phenomena of interest and have to be
studied in a natural setting
2.
does the phenomena of interest focus on
contemporary events new line is a research question aim to answer how and why
questions new I
3.
does the phenomena of interest include a variety
of factors in relationships that can be directly observed
used in exploratory studies – defined phenomena worth studying
example: relationship between information search strategies
and personal development theory
case study of a single person – interviewing and observing
information seeking on the Internet.
Can be used as a pilot study – for trying out particular
data collection methods specific context or become more familiar with the phenomena in a specific context
follow up on exploratory study conducted with another method
examine roles of librarians and remote access – impact on
quality and effectiveness of the research process
open-ended interviews – Case study protocol was used to
follow up – can be used on a preliminary study/weakness is its lack of
generalizability.
Can also be used it is the descriptive research to depict
comprehensively the phenomena of interest
over a seven-year period – case study method allowed an
investigator to describe different aspects of the restructuring, including
chronological, operational, and role-based
Facilitate evaluation research – specific organizational
context – natural cases: is well suited to understanding the interactions
between information technology related innovations and organizational contexts
can be directly applied to the improvement of information
and library practice
Summary case studies useful in many different types of
research: exploratory and confirmatory, descriptive and evaluative
ideal when a how or why question asked about a contemporary
set of events over which researcher has no control
Designing a case study
first step clearly define your research question
theoretical and empirical literature may provide a sketchy
foundation
literature review will be your first step – other critical
steps
identifying your unit of analysis
selecting a case
or cases that will be the focus of your study/planning your
data collection procedures
Identifying the unit of analysis
major entity that you are analyzing in your study – focused
on individuals as a unit of analysis/aggregate entities like groups or
organizations
primary defining characteristic – focuses on a single instance
of the unit of analysis
multiple perspectives by gathering data – multiple units of
analysis – aggregate to understand case that is focus of the study
Selecting a case
strategically select a case or several – theoretical
purposes and the relevance of its case to these
theoretical sampling – replicate or extend an emergent
theory
statistical sampling – focuses on selecting study
participants representative of population
focus on single/compared to – in depth investigation/rich
detail
five possible reasons for selecting a particular case
1.
representative
2.
critical case – essential for testing a well
formulated theory
3.
three extreme or unique case
4.
revelatory case – eliminates previously
inaccessible knowledge
5.
longitudinal study – repeatedly studied at
different points in time
Multiple case studies called comparative case studies –
combination of two or more single case studies
literal replication – similar
theoretical replication – cases that differ
collecting data
multiple methods of data collection – analysis of existing
documentation, archival records, interviews, direct observation, participant
observation and examination of physical artifacts, quantitative methods
(questionnaires)
direct observation most frequently used – equate with field
studies/not limited to direct observation
results combined through triangulation – process of using
multiple perceptions to clarify types of triangulation
1. Data triangulation – combining data from different
sources
2. Investigator triangulation – combining data multiple
researchers
3. Methodological triangulation
– combining data collected via different methods
4. theory triangulation – combining data collected from
multiple theoretical perspectives
data triangulation – data from several sources: cataloging
staff, executive staff, policy and procedures manual, and Web server
transaction log
Methodological l triangulation – direct observation of
workplace, interviews, content analysis and transaction log analysis comparison
of the findings – and share findings are valid
Strengths and weaknesses
of case studies
lack of generalizability of study findings is the weakness –
no basis for generalizing the findings beyond the setting in which he was
conducted
particularization, not generalization – rich nests with
which a particular setting or phenomena can be described
results from case studies are generalizable to theoretical
propositions
theory can be tested through case study research
single case can be used to test a theory against a
particular set of empirical circumstances
cannot be treated as a representative sample from population
four classes of criteria relevant to the quality of a case
study research report
1. resonance criteria – degree to which the report fits,
overlaps with, or reinforces selected theoretical framework
2. rhetorical criteria – deal with the form structure and
presentational characteristics of the report/unity, overall organization,
simplicity or clarity and craftsmanship
3. empowerment criteria – ability to evoke and facilitate
action, empowerment related characteristics include fairness, educative newness
and action ability
4. applicability criteria – feasibility of making inferences
from the case study applying them in the readers context or situation. Transfer
findings from one context to another, the relevant characteristics of both
should be the same
Conclusion
how and why – examining contemporary events in a natural
setting
single case – define a general conceptual category of
property
multiple cases – confirm the definition
strength: flexibility of this research strategy, rich array
of data collection techniques.
Triangulation of multiple data sources and data collection
methods – support theory testing and development, describe the phenomena of
interest. Go to sleep
Sunday, March 12, 2017
Unit 5: Research Method, Design, and Sampling; Ethics; Theory, Connaway & Powell Chapter 4
Survey Research and Sampling
Survey: group of research methods used to determine the present status of a given phenomenon
- to make inferences about a large group of elements by studying relatively small number selected from a larger group
define: to look over/see beyond
Major Differences Survey Types & Other Methods of Research
Contrast experimental -
Types of Survey Studies
Exploratory Surveys
appropriate for data - quantitative in nature, require statistical assistance to extract meaning
descriptive survey - MOST COMMON TYPE OF SURVEY = (SURVEY RESEARCH METHOD)
Basic Purposes of Descriptive Surveys - describe characteristics of population of interest, estimate proportions, make specific predictions, test associational relationships
other factors or variables
Sampling - MOST CRUCIAL STEP
Basic Terms and Concepts
Types of Sampling Methods
Combination of Cluster, Stratified and simple random EXAMPLE
Determining the Sample Size
general rule of thumb, larger the better, less than 100 not likely to represent population
General Criteria
Use of Formulas
Sampling Error
Other Causes of Sampling Error
Non-Sampling Error
Sampling in Library Use
Non-Random Sampling
Random Sampling Over Time
Conclusions
Summary
Survey: group of research methods used to determine the present status of a given phenomenon
- to make inferences about a large group of elements by studying relatively small number selected from a larger group
define: to look over/see beyond
- Observations
- Population
- Data
- Bias
Major Differences Survey Types & Other Methods of Research
- Survey gathers contemporary data
- Historical Research concerned with past data
Contrast experimental -
- doesn't enable researcher to manipulate the independent variable
- less control of research environment
- not considered capable of definitely establishing causal relationship
- less rigorous than experimental
- better suited to study large number of cases
- geographically dispersed
- more appropriate personal factors/exploratory analysis of relationships
Types of Survey Studies
Exploratory Surveys
- Literature surveys
- Experience surveys
- Analysis of "insight-stimulating" examples
appropriate for data - quantitative in nature, require statistical assistance to extract meaning
descriptive survey - MOST COMMON TYPE OF SURVEY = (SURVEY RESEARCH METHOD)
- Other Types of Surveys
- Cross sectional
- Trend study
- Cohort study
- Panel study
- Approximation of a longitudinal study
- Parallel samples study
- Contextual study
- Socio-metric study
- Critical incident study
Basic Purposes of Descriptive Surveys - describe characteristics of population of interest, estimate proportions, make specific predictions, test associational relationships
- can consider but not test causal relationships,
- can test associations relationships
- do tend to
- could conclude
- seems to be a correlation
other factors or variables
- could influence more
- cannot control so could not test
- relationship must make sense conceptionally regardless of methodology/technique
- Formulating Objectives
- Selecting Data Collection Techniques
- Selecting the Sample
- Collecting the Data
- Analyzing and Interpreting the Results
- Survey Research Designs
- Survey Research Costs
- shorten length of data collection
- reduce number of follow ups
- limit pilot or pretest to small no. participants
- shorten time spent developing data collection instruments - adapt to existing instruments
- make instrument as short as possible
- use non-monetary incentives to encourage respondents
- minimize staff costs
- shop around for least expensive supplies and equipment
- reduce number of survey activities
- minimize amount of time each activity takes
Sampling - MOST CRUCIAL STEP
Basic Terms and Concepts
- Universe
- Population
- Population Stratum - subdivision of a population based on specification or characteristic
- Element - individual member
- Census - count of all and determine characteristics
- Sample - selection of units
- Case - individual members (lower case n)
- Sampling Frame - actual list of units from which the sample is selected
Types of Sampling Methods
- Non-probability Sampling
- Accidental Sample
- Quota Sample
- Snowball Sample
- Purposive Sample
- Self-Selected Sample
- Incomplete Sample
- Probability Sampling
- Simple Random Sample SRS
- Selecting the Simple Random Sample
- number sequentially elements in population
- determine how many are to be elected
- select 3 digit numbers from table to give every element a chance of selection
- choose starting point and pattern
- processed in pattern
- select those to skip
- Systematic Sample
- Stratified Random Sample
- Cluster Sample
Combination of Cluster, Stratified and simple random EXAMPLE
- entire 48 continguous state divided into small areas called PRIMARY SAMPLING UNITS (PSU) usually counties, metro areas, tele exchange, A stratified random sample o 75 are selected from total
- PSU is stratified into large, small and smaller cities and/or rural areas. Each unit is a SAMPLE PLACE
- Sample place divided into CHUNKS/BLOCKS, a number are randomly selected
- Chunks broken down into SEGMENTS, containing 4-12 dwelling units, segments randomly drawn from each chunk
- DWELLING UNITS, constitute final sample. city directory can obtain telephone numbers for those chosen
Determining the Sample Size
general rule of thumb, larger the better, less than 100 not likely to represent population
General Criteria
- degree of precision required between sample and population, less accuracy=smaller sample
- variability of the population, greater variability=larger sample
- method of sampling / stratified sampling requires fewer cases than simple or systematic random
- way in which the results are to be analyzed / small=significant limitations on types of statistics
Use of Formulas
Sampling Error
Other Causes of Sampling Error
Non-Sampling Error
Sampling in Library Use
Non-Random Sampling
Random Sampling Over Time
Conclusions
Summary
Unit 5: Research Method, Design, and Sampling; Ethics; Theory, Connaway & Powell Chapter 3
Selecting the Research Method:
Applied Research
Action Research
Evidence Based Research
Evaluative Research
Cost Benefit Analysis
Specific Research Methods:
Applied Research
Action Research
Evidence Based Research
Evaluative Research
Cost Benefit Analysis
- net value
- reducing uncertainty
- buying service elsewhere
- librarian time
- improves performance or saves money
other kinds:
- cost-minimization
- cost-utility
- willingness to pay
- willingness to accept
- cost of time
Qualitative Research
Specific Research Methods:
Survey Research
Experimental Research
Historical Research
Operations Research
- formulate problem
- construct mathematical model to represent study
- derive solution
- test the model and solution
- establish controls over solution
- implementation
Modeling
Systems Analysis
Case Study
Delphi Study
Content Analysis
Bibliometrics
Task Based Research
Comparative Librarianship
Technology Based Research Methods
- incorporate transaction log analysis with other data collection methods
- with user demographic data
- allows for search behaviors to be analyzed in relation to the searchers experience with online systems, educational background, reason for the search, etc.
- require researcher to infer less about nature of search and maintain validity of study
Ethics of Research
- mutually respectful
- win-win relationship
- weigh questionable practices against potential benefits
Guidelines for LIS Professionals
Ethics for Research on the Internet
Scientific and Research Misconduct
Unit 5: Research Method, Design, and Sampling; Ethics; Theory, Role of Theory Topic 13
Patten, Topic 13
The Role of Theory in Research
Theory: unified explanation for discrete observation
example: reinforcement theory : positive reinforcement increases frequency of response
but
intermittent reinforcement is more effective
Test hypothesis by deducing with theory
example: goals determine level of cognitive engagement
exceptions cause reformulating to account for discrepancy
research can induce theory
Qualitative Researchers call it grounded theory (based on observations)/regularly revised as new observations warrant.
Consider research topic to test some aspect of theory, easier to defend, has implications for validating and refining a theory.
no theory is universal, always exceptions - Trends across groups can be examined
The Role of Theory in Research
Theory: unified explanation for discrete observation
example: reinforcement theory : positive reinforcement increases frequency of response
but
intermittent reinforcement is more effective
Test hypothesis by deducing with theory
example: goals determine level of cognitive engagement
exceptions cause reformulating to account for discrepancy
research can induce theory
Qualitative Researchers call it grounded theory (based on observations)/regularly revised as new observations warrant.
Consider research topic to test some aspect of theory, easier to defend, has implications for validating and refining a theory.
no theory is universal, always exceptions - Trends across groups can be examined
Unit 5: Research Method, Design, and Sampling; Ethics; Theory, Ethical Concerns Topic 12
Patten Topic 12
Ethical Considerations in Research
Key to promoting ethical values is Informed Consent to Participants of:
1. general purpose of the research
2. what will be done to them during the research
3. what the potential benefit(s) to them and others might be
4. what the potential for harm to them might be
5. the fact that they may withdraw at any time with penalty
Participation in a Research Study could
Participants must be protected from physical and psychological harm
Participants have a right to privacy
Knowledge of the purpose of the study can make it difficult, balance
Follow with debriefing,
Ethical Considerations in Research
Key to promoting ethical values is Informed Consent to Participants of:
1. general purpose of the research
2. what will be done to them during the research
3. what the potential benefit(s) to them and others might be
4. what the potential for harm to them might be
5. the fact that they may withdraw at any time with penalty
Participation in a Research Study could
- present hazards
- expose anxiety
- mental anguish
- sensitive topics - renew anxiety
Participants must be protected from physical and psychological harm
Participants have a right to privacy
- Confidentiality
- disguised or hidden identity
Knowledge of the purpose of the study can make it difficult, balance
Follow with debriefing,
- review purpose of study
- procedures
- results
- confidential
- answer any questions about the study
Unit 5 Research Method, Design, and Sampling; Ethics; Theory - JML
Eldredge, J. D. (2004). Inventory of research methods for librarianship and informatics. Journal of the Medical Library Association, 92(1), 83-90. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC314107/
Traditionally: case study, program evaluation, and survey research methodologies
Now utilizing wider array of research methods :
Analysis -
detailed examination of anything complex - understand it's nature or essential features
Audit -
review multiple variables, identify strengths and weaknesses, strict criteria formulated in advance, what should be done, protocol or plan compares to actually do.
Autobiography -
written by self about self(whole life, segment or episode)
Bibliomining -
see Data Mining
Biography -
narrative account of notable's life (whole life, segment or episode)
Case Study -
describe / analyze self experiences with a process, group, innovation, technology, project, population program or organization
explicitly state, prior to beginning, questions, propositions, unit(s) of analysis, logic & criteria for interpretation
Citation Analysis -
see Descriptive Survey
Cohort Design - track over time, defined population sharing set of common characteristics - encountering possible intended or not exposure to phenomenon or subsequent observable change in population, consequence of exposure.
Comparative Study -
systematic effort to find similarities and differences between 2 or more observed phenomena
Content Analysis -
maps non-numerical artifacts (text) into matrix of statistically manipulated symbols, to reduce large body of qualitative info to manageable size
Data Mining -
discovery of meaningful patterns from low-level data with automated methods (statistical or AI tools) - in libraries: Bibliomining
Delphi method -
anonymous participants response to questions over iterations to reach quantitative group decisions.
Descriptive Survey -
seeks to ascertain respondents' perspectives or experiences on a subject in predetermined structured way.
Focus Group -
generates data with small group setting, analyzed to help in planning/decisions, evaluate programs, products/services - develop model /theory,
Gap Analysis -
survey to detect discrepancies/gaps customer expectations or org and ability to deliver
History -
seeks to recreate accurate past although their is dispute. reveal cause/effect
Longitudinal Study -
see Cohort Study
Meta-Analysis -
combine identical/comparable data sets, 2 or more studies to create larger pool of results to strengthen conclusion
Narrative Review -
literature review on broadly defined subject, write intro overview, describe current research/controversies (offers concise intro to broad subjects)
Participant Observation -
researcher - community meet to study(understand situation from perspective of participants
Program Evaluation -
systematic assessment of operation / outcome of program/policy, compare to explicit or implicit standards.
Randomized Controlled Trial -
carefully defined and assembled population. complies with predetermined inclusion and exclusion criteria. divided into random control group - std treatment or none
Summing Up -
cluster of methods (book written to describe) where meta-analysis cannot synthesize
Systematic Reviews -
minimize bias, integrating multiple studies / concise summary of best evidence to critically appraise and synthesize relevant studies. quantitative or qualitative, related to summing up
Unobtrusive Observation -
ethical concerns (participants do not know they are being studied)
Traditionally: case study, program evaluation, and survey research methodologies
Now utilizing wider array of research methods :
Analysis -
detailed examination of anything complex - understand it's nature or essential features
Audit -
review multiple variables, identify strengths and weaknesses, strict criteria formulated in advance, what should be done, protocol or plan compares to actually do.
Autobiography -
written by self about self(whole life, segment or episode)
Bibliomining -
see Data Mining
Biography -
narrative account of notable's life (whole life, segment or episode)
Case Study -
describe / analyze self experiences with a process, group, innovation, technology, project, population program or organization
explicitly state, prior to beginning, questions, propositions, unit(s) of analysis, logic & criteria for interpretation
Citation Analysis -
see Descriptive Survey
Cohort Design - track over time, defined population sharing set of common characteristics - encountering possible intended or not exposure to phenomenon or subsequent observable change in population, consequence of exposure.
Comparative Study -
systematic effort to find similarities and differences between 2 or more observed phenomena
Content Analysis -
maps non-numerical artifacts (text) into matrix of statistically manipulated symbols, to reduce large body of qualitative info to manageable size
Data Mining -
discovery of meaningful patterns from low-level data with automated methods (statistical or AI tools) - in libraries: Bibliomining
Delphi method -
anonymous participants response to questions over iterations to reach quantitative group decisions.
Descriptive Survey -
seeks to ascertain respondents' perspectives or experiences on a subject in predetermined structured way.
Focus Group -
generates data with small group setting, analyzed to help in planning/decisions, evaluate programs, products/services - develop model /theory,
Gap Analysis -
survey to detect discrepancies/gaps customer expectations or org and ability to deliver
History -
seeks to recreate accurate past although their is dispute. reveal cause/effect
Longitudinal Study -
see Cohort Study
Meta-Analysis -
combine identical/comparable data sets, 2 or more studies to create larger pool of results to strengthen conclusion
Narrative Review -
literature review on broadly defined subject, write intro overview, describe current research/controversies (offers concise intro to broad subjects)
Participant Observation -
researcher - community meet to study(understand situation from perspective of participants
Program Evaluation -
systematic assessment of operation / outcome of program/policy, compare to explicit or implicit standards.
Randomized Controlled Trial -
carefully defined and assembled population. complies with predetermined inclusion and exclusion criteria. divided into random control group - std treatment or none
Summing Up -
cluster of methods (book written to describe) where meta-analysis cannot synthesize
Systematic Reviews -
minimize bias, integrating multiple studies / concise summary of best evidence to critically appraise and synthesize relevant studies. quantitative or qualitative, related to summing up
Unobtrusive Observation -
ethical concerns (participants do not know they are being studied)
Tuesday, February 28, 2017
Unit 4 Literature Review Patten Topic 17 Prepare to be Critical
Patten, Topic 17 - Preparation to be Critical
First don't assume results of every study to be facts. presume they are flawed, offering degrees of evidence
some are methodologically superior to others
assess quality of each to be cited
look at sampling - weakness limits generalization of the results
measurement instrumentation
various methods of a given variable might lead to different results
look at limitations of sampling choices
applies to experiments only: often inappropriate control conditions - rewards may have effects in the natural environment different from those in a laboratory setting
are limitations discussed in last section?
First don't assume results of every study to be facts. presume they are flawed, offering degrees of evidence
some are methodologically superior to others
assess quality of each to be cited
look at sampling - weakness limits generalization of the results
measurement instrumentation
various methods of a given variable might lead to different results
look at limitations of sampling choices
applies to experiments only: often inappropriate control conditions - rewards may have effects in the natural environment different from those in a laboratory setting
are limitations discussed in last section?
Unit 4 Literature Review Patten Topic 16 Organization
Patten, Topic 16 Organization
First identify the topic, second assert that it is an important problem
ex:
this is risky for this group. evidence shows that its increasing and problems are growing.
Cite Statistics to show imporance
Start with a conceptual definition of a key variable
ex:
a is defined as x. cuurent lits says % to % of d. a is connected to f by x,x,x,
(can cite a published conceptual definition)
if started with a definition, follow with statements and statistics indicate importance
after establish importance
define key terms,
write topic by topic description of relevant research
major / minor subheadings to guide thru a long literature review
Don't write series of abstracts, instead group references together by commonality
First identify the topic, second assert that it is an important problem
ex:
this is risky for this group. evidence shows that its increasing and problems are growing.
Cite Statistics to show imporance
Start with a conceptual definition of a key variable
ex:
a is defined as x. cuurent lits says % to % of d. a is connected to f by x,x,x,
(can cite a published conceptual definition)
if started with a definition, follow with statements and statistics indicate importance
after establish importance
define key terms,
write topic by topic description of relevant research
major / minor subheadings to guide thru a long literature review
Don't write series of abstracts, instead group references together by commonality
Unit 4 Literature Review Patten Topic 15 Locating Literature Electronically
Patten, Topic 15
Journal Articles :
Journal Articles :
- major source of original reports of empirical research
- primary source of information on established and emerging theories
4 major databases in the social and behavioral sciences
- Sociological Abstracts
- PsycINFO
- PsycARTICLES
- ERIC - free, www.ERIC.ed.gov
Define Broadly, then narrow,
changing and to or
changing and to Not
Unit 4 Literature Review Patten, Topic 14 Reasons for Reviewing Literature
PattenTopics14-18andAppendixC
Examine both Theory and Research Literature
Researchers often make suggestions for further research in the last section of their report
**** Could replicate a study
modify replication -
Examine both Theory and Research Literature
Researchers often make suggestions for further research in the last section of their report
**** Could replicate a study
modify replication -
- new population
- improved measurement technique
resolve existing conflict (mentioned in literature)
new research almost always has its origins in existing research
Additional Benefits of Literature Review
- identify measuring tools (successful / flawed / avoided)
- identify dead ends
- style and organization used by others
Well-crafted review shows
- context within which the researcher was working
- justify a study - literature establishes importance of topic
- shows how research flows from previously published research
Unit 4 Literature Review Deakin University Library
The Literature Review (Deakin University)
- reports of primary or original scholarship, and does not report new primary scholarship itself
- empirical, theoretical, critical/analytic, or methodological in nature
- describe, summarise, evaluate, clarify and/or integrate the content of primary reports.
- standard chapter of a thesis or dissertation
provide the background to and justification for the research undertaken
6 Elements:
- a list;
- a search;
- a survey;
- a vehicle for learning;
- a research facilitator
- a report.
Why Do it?
- to identify gaps in the literature
- to avoid reinventing the wheel (at the least this will save time and it can stop you from making the same mistakes as others)
- to carry on from where others have already reached (reviewing the field allows you to build on the platform of existing knowledge and ideas)
- to identify other people working in the same fields (a researcher network is a valuable resource)
- to increase your breadth of knowledge of your subject area
- to identify seminal works in your area
- to provide the intellectual context for your own work, enabling you to position your project relative to other work
- to identify opposing views
- to put your work into perspective
- to demonstrate that you can access previous work in an area
- to identify information and ideas that may be relevant to your project
- to identify methods that could be relevant to your project
Requirements:
- knowledge of the use of indexes and abstracts,
- ability to conduct exhaustive bibliographic searches,
- ability to organize the collected data meaningfully,
- describe, critique and relate each source to the subject of the inquiry,
- present the organised review logically,
- to correctly cite all sources mentioned
Unit 4 Literature Review - University of North Carolina
The Literature Review (University of North Carolina)
personal opinion not required
personal opinion not required
- summary of the sources / organizational pattern
- summary & syntheses
- summary - recap of the important information
- synthesis -- re-organization/reshuffling / new interpretation of old material / combine new&old
- trace intellectual progression of the filed
- major debates
- evaluate the sources
- advise the reader on most pertinent or relevant
NOT an academic research paper (develop a new argument) - likely contains a literature review
literature is a foundation & support for new insight
why?
- handy guide to particular topic / overview / stepping stone
- useful reports - up to date with what is current in the filed
- emphasizes the credibility of the writer
- solid background for investigation
- comprehensive knowledge of the literature of the field is essential to most research papers
- occasionally in humanities
- mostly in the sciences and social sciences
- experiment and lab reports
- a paper in itself
Clarify
- how many sources required
- types of sources
- summarize, synthesize or critique
- evaluate sources
- subheadings / background information// definitions / history
Current Sources!! Find models & narrow your topic
- Find a Focus
- Convey to Reader
- Consider Organization
First: basic categories -
3 basic elements
- introduction or background
- body containing sources
- conclusion / recommendation
Organization
chronology
by publication
by trend
thematic
methodology
current situation
history
methods or standards
questions for further research
Composing
Use evidence
be selective
use quotes sparingly
summarize and synthesize
keep your own voice
be cautious when paraphrasing
Revise Repeatedly
Monday, February 20, 2017
Unit 4 Conducting Literature Review - University of Toronto
The Literature Review: A Few Tips On Conducting It (University of Toronto):
Discursive prose,
- organize into sections
- themes
- trends
- relevant theory
Introduction and conclusion
- scope of coverage
- formulate the question, problem, or concept
- comparisons and relationships.
- paragraph
- introduce the focus of each section
purpose of literature review:
convey:
- established knowledge and ideas
- strengths and weaknesses
- defined by a guiding concept
- your research objective,
- the problem or issue
- argumentative thesis
demonstrate skills:
- information seeking: efficiently identify a set of useful articles and books
- critical appraisal: apply principles of analysis to identify unbiased and valid studies.
also must:
- organized and directly related to thesis or research question
- summarize what is and is not known
- identify controversies
- recommend questions for further research
Answer these questions:
- specific thesis, problem, or research question
- type of literature review
- theory/ methodology/ policy/ quantitative or qualitative
- scope
- journals
- books
- government documents
- popular media
- information seeking wide enough / narrow enough
- number of sources re: appropriate length
- critically analysed with set of concepts and questions
- assess them,
- discussing strengths and weaknesses
- citations
- relevant, appropriate, and useful
Monday, February 13, 2017
Unit 3 Wildemuth Chapter 5 Testing Hypotheses
p. 33
Make a statement assumed to be true, capable of verification
related to variables, not necessarily causative
Causes help us to understand and make sense of world
p. 34
develop based on direct experience
related to another phenomena
relationships hold in other arenas
Are you first to study? no methods established to measure
formulate & test hypotheses
First state it clearly as a question
examine each noun, verb, adjective for ambiguity
Null - no relationship
p. 35
Methods of formulating hypotheses
examples
Make a statement assumed to be true, capable of verification
related to variables, not necessarily causative
Causes help us to understand and make sense of world
p. 34
develop based on direct experience
related to another phenomena
relationships hold in other arenas
Are you first to study? no methods established to measure
formulate & test hypotheses
First state it clearly as a question
examine each noun, verb, adjective for ambiguity
Null - no relationship
p. 35
Methods of formulating hypotheses
examples
- systems of search - manual, assisted, automatic - user's choice
- easier to establish relationship than cause
- click study on peripheral clues
Unit 3 Wildemuth Chapter 3 Practice Based Questions
p. 21
Practice based Questions
Information professionals base decisions on strongest evidence available
Practice based Questions
Information professionals base decisions on strongest evidence available
- use current literature base
- conduct their own research for suitable evidence
- to determine police, allocate resources & manage
- more than react to problems
- proactively question current practices
- seek ways to improve resources & services
- identify & use conclusions of existing studies - decisions on info practice
p. 22
why?
fill in gaps and mend seams of professional body of knowledge in order to advance
Team up with researchers in nearby universities
example: school librarian/social software developer.
Formulate question
important to core activities, of interest in local settings, appropriate level of abstractness
p. 23
watch for political issues if you are involved with stakeholders
improve services you offer
apply research results as strong evidence
link to earlier work on the same question
publish for others
3 examples
- Business students and Business and Economics Library - why increase visibility?
- Transaction log - website structure
- school library / students help
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