test 3 Flashcards
qualitative analysis
the nonnumerical examination and interpretation of observations, for the purpose of discovering underlying meanings and patterns of relationships. This is most typical of field research and historical research.
frequencies
How often does child abuse occur among families in the neighborhood under study? Realize there may be a difference between the frequency and what people are willing to tell you?
magnitudes
what are the levels of abuse? How brutal are they?
structures
what are the different types of abuse: physical,
mental, sexual? Are they related in any particular manner?
Processes
is there any order among the elements of structure? Do abusers begin with mental abuse and move on to physical and sexual abuse, or does the order of elements vary?
causes
what are the causes of child abuse? Is it more common in particular social classes or among different religious or ethnic groups? Does it occur more often during good times or bad?
Consequences
How does child abuse affect the victims, in both the short and the long term? What changes does it cause in the abusers?
cross- case- analysis
an analysis that involves an examination of more than one case; this can be either a variable- oriented or case- oriented analysis.
variable-oriented analysis
an analysis that describes and/or explains a particular variable.
case- oriented analysis
an analysis that aims to understand a particular case or several cases by looking closely at the details of each.
variable oriented analysis
the aim here is to achieve a partial, overall explanation using relatively few variables.
case oriented analysis
would look more closely into a particular case.
grounded theory method
an inductive approach to research, introduced by Barney Glaser and Anselm Strauss, in which theories are generated solely from an examination of data rather than being derived deductively.
constant comparitive method
a component of the Grounded Theory Method in which observations are compared with one another and with the evolving inductive theory.
'’comparing incidents applicable to each category.’’
As Glaser and Strauss researched the reactions of nurses to the possible death of patients in their care, the researchers found that the nurses were assessing the ‘‘social loss’’ attendant upon a patient’s death. Once this concept arose in the analysis of one case, they looked for evidence of the same phenomenon in other cases. When they found the concept arising in the cases of several nurses, they compared the different incidents. This process is similar to conceptualization as described in chapter 5- specifying the nature and dimensions of the many concepts arising from the data
'’Integrating categories and their properties.’’
Here the researcher begins to note relationships among concepts. In the assesment of social loss, for example, Glaser and Strauss found that nurses took special notice of a patient’s age, education, and family responsiblities. For these relationships to emerge, however, it was necessary for the researchers to have noticed all these concepts.
'’Delimiting the theory.’’
Eventually, as the patterns of relationships among concepts become clearer, the researcher can ignore some of the concepts that were initially noted but are evidently irrelevant to te inquiry. In addition to the number of categories being reduced, the theory itself may become simpler. In the examination of social loss, for example, Glaser and Strauss found that the assessment processes could be generalized beyond nurses and dying patients: They seemed to apply to the ways all staff dealt with all patients (dying or not).
'’writing theory.’’
Finally, the researcher must put his or her findings into words to be shared with others. As you may have already experienced for yourself, the act of communicating your understanding of something actually modifies and even improves your own grasp of the topic. In GTM, the writing stage is regarded as a part of the research process.
semiotics
the study of signs and the meanings associated with them. This is commonly associated with content analysis. Commonly associated with content analysis. also known as a search for the meanings intentionally or unintentionally attached to signs.
conversation analysis
a meticulous analysis of the details of conversation, based on a complete transcript that includes pauses, hems, and also haws.
open coding
the initial classification and labeling of concepts in qualitative data analysis. In open coding, the codes are suggested by the researchers’ examination and questioning of the data.
axial coding
a reanlysis of the results of open coding in the Grounded Theory Method, aimed at identifying the important, general concepts.
selective coding
In Grounded Method Theory, this analysis builds on the results of open coding and axial coding to identify the central concept that organizes the other concepts that have been identified in a body of textual materials.
memoing
writing memos that become part of the data for analysis in qualitative research such as grounded theory. Memos can describe and define concepts, deal with methodological issues, or offer initial theoretical formulations.
what uses the constant comparitive method?
grounded theory
code notes
identify the codes you are using with specific definitions
theoretical notes
may cover numerous topics, how concepts relate, deeper/ underlying meanings, etc: as we try to make sense of our data
operational notes
specifically for methodological issues.
what are the fundamental assumptions of conversation analysis
Conversation is a socially constructed activity. Conversations must be understood contextually. Conversational analysis aims to understand the structure and meaning of conversation through excruciatingly accurate transcripts of conversations.
coding units
these are the concepts of interest
coding as a physical act
requires physically coding and reorganizing data
creating codes
prior theory vs grounded theory methods
quantitative analysis
the numerical representation and manipulation of observations for the purpose of describing and explaining the phenomena that those observations reflect.
codebook
the document used in data processing and analysis that tells the location of different data items in a data file. Typically, the codebook identifies the locations of data items and the meaning of the codes used to represent different attributes of variables.
purposes of a codebook
1.primary guide in the coding processes (data entry). 2. guide for locating variables (during analysis)
what are the common elements in codebooks
abbreviated variable name. variable definition. numerical label. variable attributes.
univariate analysis
the analysis of a single variable, for purposes of description. Frequency distributions, averages, and measures of dispersion would be examples of univariate analysis, as distinuished from bivariate and multivariate analysis.
what is a continuous variable in univariate analysis.
a variable whose attributes form a steady progression, such as amount of income.
what is a discrete variable in univariate analysis
a variable whose attributes are seperate from one another, sucha as gender or political affiliation.
frequency distributions
a description onf the number of times the various attributes of a variable are observed in a sample.
mode
an average representing the most frequently observed value or attribute. If a sample contains 1,000 protestants, 275 Catholics, and 33 jews, Protestant is the modal category.
mean
An average computed by summing the values of several observations and dividing by the number of observations. If you now have a grade point average of 4.0 based on 10 courses, and you get an f in this course, your new grade point (mean) average will be 3.6.
average
an ambiguous term generally suggesting typical or normal- a central tendency. The mean, median, and mode are specific examples of mathematical averages.
median
An average representing
the value of the ‘‘middle’’ case in a rank ordered set of observations. if the ages of five men are 16, 17,20, 54,and 88, the median would be 20. ( the mean would be 39).
what would be examples of univariate analysis
Frequency distributions, averages and measures of dispersion would be examples of univariate analysis, as distinguished from bivariate and multivariate analysis.
dispersion
the distribution of values around some central value, such as an average. The range is a simple example of a measure of dispersion. Thus we may report that the mean age of a group is 37.9, and the range is from 12 to 89.
standard deviation
a measure of dispersion around the mean, calculate so that approximately 68 percent of the cases will lie within plus or minus one standard deviation from the mean, 95 percent within two, and 99.9 percent within three standard deviations.
bivariate analysis
the analysis of two variables simultaneously, for the purpose of determining the empirical relationship between them. The construction of a simple percentage table or the computation of a simple correlation coefficient are examples of bivariate analyses.
contingency table
a format for presenting the relationship among variables as percentage distributions.
multivariate analysis
the analysis of the simultaneous relationships among several variables. Examining simultaneously the effects of age, sex, and social class on religiosity would be an example of multivariate analysis.
concept mapping
the graphic display of concepts and their interrelations, useful in the formulation of theory.
descriptive statistics
statistical computations describing either the characteristics of a sample or the relationship among variables in a sample. Descriptive statistics merely summarize a set of sample observations, whereas inferential statistics move beyond the description of specific observations to make inferences about the larger population from which the sample observations were drawn.
proportionate reduction of error
a logical model for asessing the strength of a relationship by asking how much knowing values on one variable would reduce our errors in guessing values on the other. For example, if we know how much education people have, we can improve our ability to estimate how much they earn, thus indicating there is a relationship between the two variables.
regression analysis
a method of data analysis by which the relationships among variables are represented in the form of an equation, called a regression equation.
linear regression analysis
a form of statistical analysis that seeks the equation for the straight line that best describes the relationship between two ratio variables.
multiple regression analysis
a form of statistical analysis that seeks the equation representing the impact of two or more independent variables on a single dependent variable.
partial regression analysis
a form of regression analysis in which the effects of one or more variables are held constant, similar to the logic of the elaboration model.
curvilinear regression analysis
a form of regression analysis that allows relationships among variables to be expressed with curved geometric lines instead of straight ones.
inferential statistics
the body of statistical computations relevant to making inferences from findings based on sample observations to some larger population.
nonsampling error
those imperfections of data quality that are a result of factors other than sampling error. Examples include misunderstandings of questions by respondents and erroneous recordings by interviewers and coders.
statistical significance
a general term referring to the likelihood that relationships observed in a sample could be attributed to sampling error alone.
tests of statistical significance
a class of statistical computations that indicate the likelihood that the relationship observed between variables in a sample can be attributed to sampling error only.
level of significance
in the context of tests of statistical significance, the degree of likelihood that an observed, empirical relationship could be attributable to sampling error. A relationship is significant at the .05 level if the likelihood of its being only a function of sampling error is no greater than 5 out of 100.
path analysis
a form of multivariate analysis in which the causal relationships among variables are presented in a graphic format.
time- series analysis
an analysis of changes in a variable (such as crime rates over time).
factor analysis
a complex algebraic method for determining the general dimensions or factors that exist within a set of concrete observations.
analysis of variance
method of analysis in which cases under study are combined into groups representing an independent variable, and the extent to which the groups differ from one another is analyzed in terms of some dependent variable. Then, the extent to which the groups differ is compared with the standard of random distribution.
discriminant analysis
method of analysis similar to multiple regression, except that the dependent variable can be normal.
log- linear analysis
data analysis technique based on specifying models that describe the interrelationships among variables and then comparing expected and observed table- cell frequencies.
odds ratio
a statistical technique for expressing the relationship between variables by comparing the odds of different occurences.
Geographic Information Systems
analytic technique in which researchers map quantitative data that describe geographic units for a graphic display.
abstract
a summary of a research article. The abstract usually begins the article and states the purpose of the research, the methods used, and the major findings.
research monograph
a book length research report, either published or unpublished. this is distinguished from a textbook, a book of essays, a novel, and so forth.
URL
web address, typically beginning with ‘‘http://’’; stands for ‘‘uniform resource locator’’ or ‘‘universal resource locator.’’
plagarism
presenting someone else’s words or thoughts as though they were your own, constituting intellectual theft.
content analysis
transforms qualitative dataa into code categories. This is analyzed quantitatively (by examining the number of times each code appears).
counting and record keeping
How often does each code category appear? This is your main data source.
qualitative analysis
can be used to supplement and interpret codes.
quantification of data ( a note on detail versus manageability)
provide reader with fullest degree of detail, balanced with presenting data in a manageable form.
sub group comparisons
description of subsets of cases, subjects or respondents
constructing a bivariate table
- determine logical direction of relationship (independent and dependent variable). the
percentaging a table
1.percentage down or across. 2. depends on the location of the independent variable in the table.
don’t know response
omit or keep
marginials
how much of each category we are looking at. specifically for bivariate tables.
quantitative research
data drives your research
manifest
clear overt content. only when you see a certain word.
latent
underlying meanings. up for interpretation.
quantitative data has much greater what?
reliability.
how is the chi square interpreted
P value is the level of statistical significance. P value <0.5 reject null hypotheses. (there is a significant statistical significance). P value > 0.5 no statistical significance.
what kind of list does a case oriented variable have
it has an exhaustive list.
what is the measure of association
the relationship between two variables.
axial coding
narrowing down code categories.
open coding
generate all code categories ie: original coding
selective coding
one selective code that fits together.
qualitative analysis
can go either way. grounded theory or theory that forms your analysis,
predefined coding scheme
does this theory apply in another situation.
quantitative analysis is driven by what?
it is theory driven.
qualitative analysis can be both ________, and ________
inductive, and deductive.
how should you go about reading a journal article.
read the abstract first. a summary of a research article. states the purpose of the research, the methods used, and the major findings. Skim the article, noting section headings and tables and graphs. Read the article in its entirety. review the article.
Evaluating research reports
theoretical orientations. is there a theoretical perspective? are hypotheses linked to theory.
research design
this covers many of the basics of the research process; the goals of the study, the unit of analysis, the incorporation of time.
measuring social research
what are the concepts and variables of interest? How are they being measured.
sampling in reading social research
what was the particular sampling strategy employed. (probability vs non-probability sampling? Can you identify the specific population? what was the size of the sample?
analysis of social research
what is the particular data analysis strategy used by the researcher? Are they appropriately given the data collected?
reporting in evaluation of social research
how does the researcher link the current study to prior research? What is the degree of detail provided by the study. Do conclusions go beyond the scope of research?
