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180 Cards in this Set

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Measure of central tendency for Nominal level of measurement
mode
Measure of central tendency for ordinal level of measurement
mode & median
Measure of central tendency for interval level of measurement
mode, median & mean
Measure of dispersion for Nominal level of measurement
range
Measure of dispersion for ordinal level of measurement
range, interquartile & semiquartile range
Measure of dispersion for interval level of measurement
range, interquartile & semiquartile range, standard deviation
What is a measure of central tendency?
a mathematical property of a distribution, which describes what is typical of that distribution
What are descriptive statistics?
information about the typicality of a distribution as well as the dispersion of values and the shape of the distribution
modal category
the most frequently occurring category within a distribution of values (e.g., males
in the class)
mode
is the frequency associated with that category (e.g., 23 males)
median
the scale value that divides the top 50% of a distribution from the bottom 50% of a distribution
3 methods of finding the median
1. counting until you find the middle
2. create a frequency distribution & find the 50th percentile
3. calculate: use this when there are tied ranks & when scale is continuous
equation to calculate median
median = lower limit + (range * (.5 N - # below lower limit/# cases in the interval))
Tied ranks
two or more of the same value occurring in a distribution.
exact lower limit
in a frequency distribution, the value at the midpoint between the
value of that category and the one below it
arithmetic mean
the arithmetic average of all the scores in a
distribution
deviation score
the distance, either positive or negative, that a value lies above or below the mean of
the distribution. A deviation score is often symbolized as d
advantages of using the mode as a measure of central tendency
1. easy to compute
2. most flexible & can use for nominal
3. least effect of extreme scores
advantages of using the median as a measure of central tendency
1. can be used for O, I, R
2. less sensitive than mode to groupings into class intervals
3. less affected by extreme scores than mean
advantages of using the mean as a measure of central tendency
1. reflects actual value of scores
2. most stable measure of CT
3. can be manipulated algebraically
disadvantages of using the mode as a measure of central tendency
1. there may be more than one
2. sensitive to size and number of class intervals e.g. $0-24K, $25-49 -> changing the size of the interval will move the mode across categories
3. unstable
disadvantages of using the median as a measure of central tendency
1.hard to manipulate
2. less used for inferential statistics
disadvantages of using the mean as a measure of central tendency
1. affected by extreme scores
2. can only be used for I or R scales
What are the relative positions of the mean, median and mode in a positively skewed distribution?
mode, median & mean -> long tail to the right
Sum of the deviation scores
The mean of a distribution is the point at which the sum of the deviation scores from the mean equals 0
range
-the difference in frequency between the most frequent and the least frequent category
-a measure of dispersion
-particularly suited for nominal-level data
interquartile range
-the difference in scale value between the 75th percentile and the 25th percentile
-particularly useful for ordinal data
semi-interquartile range
-1/2 of the interquartile range
-describes the 25% of values closest to the median
-aka quartile deviation
When should you use the interquartile or semi-quartile range?
-when distribution is skewed by extreme scores
-when data are ordinal
-don't use for inferential stats
-use with median to describe the dispersion
Name appropriate measures of dispersion for the NOIR levels.
N - range
O - interquartile/semi-quartile range
I - mean square (variance), SD
R - variance, SD
sum of squares
the sum of the squared deviations of each value in a distribution from its mean
mean square
-the average, or mean, of the sum of squares
-aka variance
standard deviation
-square root of the mean square (variance)
-converts squared units into back into scale units
when to divide by N-1
-when estimating the population variance
Degrees of freedom
-number of independent pieces of data that are available for inference to a population
-in samples, it is symbolized as N – 1.
biased estimator
- variance and standard deviation of a sample
-does not generalize well to a population (i.e., does not estimate well), but correctly describes a sample
why is it important that the mean of a sample is a good estimator of the population mean?
-you don't have to adjust it when estimating for the population, that is, you NEVER divide by N-1
skewness
used to describe distributions that “lean” towards the upper end of a scale (i.e., negative skew) or towards the lower end of a scale (i.e., positive skew)
kurtosis
describes “flatness” or “peakedness” in a distribution
platykurtic
a very flat distribution
leptokurtic
a very peaked distribution
mesokurtic
between platy- and leptokurtic
What are the 4 "moments around the mean?"
-four statistical measures that describe the mathematical characteristics of a distribution

1. average deviation - defines position of the mean
2. variance - spread of values around the mean
3. skewness - sum of cubed deviations
4. kurtosis - (avg dev_^4)
What does negative kurtosis mean?
tall, peaked (leptokurtic) distribution > the more positive, the flatter it is

distributions with negative kurtosis tend to show less variability
What are the three methods of determining the IQR?
1. IQR = Q3-Q1
2. once you know the median, count to the medians of the distribution below and above
3. calculate exact midpoints above & below median using formula
steps of research (3 pts)
1. Pose question;
2. Collect data;
3. Present answer to the question
importance of educational research (3 pts)
1. adding to knowledge (original or replication);
2. improving practice (suggesting improvement, evaluating approaches, building connections with other practitioners/researchers);
3. informing policy
pitfalls in research
1. vague/contradictory findings;
2. questionable data (inappropriate sample, instruments, analysis);
3. unclear intent;
4. lack of full disclosure
6 steps in research process
1. identify problem (specify, justify);
2. review literature (select & summarize sources);
3. specify purpose (identify and narrow purpose statement to research questions);
4. collect data (select sample, obtain permission, get info) ;
5. analyze & interpret data (represent & explain data);
6. report & evaluate research (choose audience, structure & write report)
quantitative research: 6 steps
1.research problem: description of trends, need for explanation of relationship between variables;
2.lit review: suggest research questions & justify need;
3.purpose: specific, narrow, measurable & observable;
4.data collection: numeric, from large N using instruments with present questions & answers;
5.analysis: trnds, comparing groups, relating variables statistically,comparing results to prior predictions and past research;
6.report: standard, fixed structure & evaluation criteria, objective
qualitative research: 6 steps
1.research problem: exploratory, developing detailed understanding of a central phenomenon;
2.lit review: minor role, justifies problem;
3.purpose: general, broad to be inclusive of participants' experiences;
4.data collection: based on words from small number of individuals, protocols developed to record data as study proceeds (e.g., observational, interview, transcribed audio);
5.analysis: data analyzed for description and themes using text analysis and interpreting larger meaning of the findings;
6.report:flexible, emerging structures and evaluative criteria, includes researchers' subjective reflexivity & bias
quantitative research designs (3)
experimental (intervention/group comparison studies),
correlational,
survey design
qualitative research designs (3)
grounded theory designs (based on model developed from interview data),
ethnographic design (one group of individuals in setting where they live & work, examines how they interact),
narrative research (describes lives of individuals)
mixed methods designs
both quant & qual. to provide better understanding of the problem, may be multiphase or combined into a single study;

need to decide on priority given to each type of data, how to integrate data and whether to use theory to guide study
action research designs
like mixed but focus on practical problems in schools & classrooms

systematic procedures used by teachers/others to address improvements in setting, teaching & students' learning
ethical issues in conducting research
institutional review boards - study must conform to guidelines

professional associations - often address ways to give back to participants

ethical data collection - minimize disruption to site (too much time, interference with ongoing work, too late in year) - 'gatekeepers' help, risk to participants

reporting data - report honestly, don't plagiarize, give credit where due
research skills
solving puzzles, attention span, library skills, writing & editing
research problem versus topic
topic is broad subject matter, the problem is narrower issue
research problem versus purpose of study
purpose is major objective of study that addresses the problem
research problem versus research questions
questions narrow the purpose into specific questions to be answered in study
5 questions to assess whether to research a problem
1. Is there a gap in the literature?
2. Does it replicate a past study with a new sample & site?
3. Does the study extend past research?
4. Does the study give voice to marginalized individuals?
5. Does the study inform practice?
(4 pts) When quantitative may be more appropriate
1. measure variables
2.assessing impact of variables on outcome
3.testing theories or broad explanations
4. applying results to large number of people
(4 pts) When qualitative may be more appropriate
1. learning about individuals' views
2. assessing process over time
3. generating theories based on participant perspectives
4. obtaining detailed information about a few people or research sites
(5 pts) Parts of a 'Statement of the Problem' section
1. topic (broad subject matter)
2. research problem
3. justification of importance based on past research
4. gaps in existing knowledge
5. audiences that will benefit from study
narrative hook (first sentence of Statement of Problem)
May contain:
1. statistical data
2. provocative question
3. clear need for research
4 intent or purpose of study

-begin with introduction that is easy to understand and open with statement that draws reader interest
strategies for writing "Statement of the Problem" sections
1. use a template
2. cite literature often
3. reference statistical information and quoates
purpose statement
sentence in study that states overall direction/objective
research question
questions that focus purpose of study into specific areas
hypothesis
statement that narrows purpose statement into specific predictions about relationships among variables
research objective
statement of intent used in quantitative research that specifies goals, often subdivided into major and minor objectives, often appear at end of 'statement of the problem' sections, after lit review
variable
measurable and variable characteristic or attribute of an individual or organization
measurement
researchers records information from individuals - self-report questionnaire, observation and recording of scores
categorical vs. continuous variables
categorical = discrete/nominal (measured as a small number of groups/categories)
continuous = interval/rating/scaled score (measured as a point on a continuum)
construct
attribute or characteristic stated in a specific, applied way (e.g. achievement is construct, GPA is a variable)
4 questions to distinguish between types of variables
What am I trying to explain? (dependent)
What variables influence the outcomes? (independent)
What else do I need to measure to make sure the variables in question influence outcomes? (control and mediating)
What variables influence outcomes bu cannot/will not be measured? (confounding variables)
dependent variable
attribute influenced by the independent variable
independent variable
attribute that influences/affects outcome or dependent variable
types of independent variables (4)
measured, control, treatment, moderating
measured variables
independent variable that is measured in a study, used in experiments and survets e.g. age of a child
control variable
(covariate) IV that is of secondary interest and neutralized through statistical or design procedures, used in experiments, correlational studies e.g. demographic info like age, gender, SES
treatment variable
IV that is manipulated by the researchers, categorical & composed of 2+ groups, used in experiments
moderating variable
IV that is of secondary interest that combines with another IV to influence the DV, measured & observed as it interacts with other variables, used in experiments, e.g. demographics, measured variables like attitude, manipulated variable like classroom instruction
where to locate an independent variable
in purpose statements, research questions & hypotheses: look for variable that influences or predicts an outcome
intervening variable
attribute or characteristic that "stands between" IV and DV and influences DV independently - mediates the effecrs of the IV
confounding variable
aka spurious variables = attributes the research can't directly measure even though the influence the relationship between IV and DV
probable causation
researchers attempt to establish cause-and-effect relationships rather than prove the relationship
a theory in quantatitive research
explains and predicts the probable relationships between IV and DV
quantitative purpose statement
identifies variables, relationship, participants and site for research
basic steps in writing quantitative research questions
-pose question
-begin with how what or why
-specify IV DV mediating& control variables
-describe/compare/relate to indicate connection between variables
-indicate participants and research site
descriptive question
identifies participants' responses to a single variable/questions
relationship question
tries to identify degree and magnitude of relationship between two or more variables
comparison question
ask to find out how 2+ groups on an IV differ in terms or outcome variables
null hypotheses
predicts that there is no relationship between independent and dependent variables. no difference between groups of an IV or DV
alternative hypothesis
not the null hypothesis, suggests other possibilities based on results from past research or explanation from the literature
directional alternative hypothesis
predicts the direction of a change in the total population of people
non-directional alternative hypothesis
predicts a change, a difference or a relationship for variables in a population but does not indicate positive or negative direction
central phenomenon
concept/process explored e.g. ethnic identity of Chinese American immigrants
differentiating between quant and qual
quant,qual: hypotheses vs. research questions , identifies and tries to measure multiple variables vs. central phenomenon, testing theories that predicts results from relating variables vs. non testing theories, close-ended vs. open-ended where phenomenon of interest may change, measures differences and magnitudes of differences or chnge over time vs. looking for views of small group or individuals
emerging process
intent/purpose & questions researched may change based onf feedback/responses from participants
qualitative purpose statements
indicates intent to explore/understand central phenomenon with specific individuals at certain site, typically at end of introduction
strategies for writing central questions
begins with how or what, specifies central phenomenon, identifies participants, mentions research site
sub-questions
refines the central questions (more specific)
issue subquestions
narrow the focus of the central question into specific questions
procedural subquestions
indicate steps to be used in analyzing data (e.g. which question will be answered first, orom identifying categories to tracing a specified process)
distinguishing qual from data collection questions
in data collection, you also ask participants about themselves and ask them to suggest other helpful individuals
research problem versus topic
topic is broad subject matter, the problem is narrower issue
research problem versus purpose of study
purpose is major objective of study that addresses the problem
research problem versus research questions
questions narrow the purpose into specific questions to be answered in study
5 questions to assess whether to research a problem
1. Is there a gap in the literature?
2. Does it replicate a past study with a new sample & site?
3. Does the study extend past research?
4. Does the study give voice to marginalized individuals?
5. Does the study inform practice?
(4 pts) When quantitative may be more appropriate
1. measure variables
2.assessing impact of variables on outcome
3.testing theories or broad explanations
4. applying results to large number of people
(4 pts) When qualitative may be more appropriate
1. learning about individuals' views
2. assessing process over time
3. generating theories based on participant perspectives
4. obtaining detailed information about a few people or research sites
(5 pts) Parts of a 'Statement of the Problem' section
1. topic (broad subject matter)
2. research problem
3. justification of importance based on past research
4. gaps in existing knowledge
5. audiences that will benefit from study
narrative hook (first sentence of Statement of Problem)
May contain:
1. statistical data
2. provocative question
3. clear need for research
4 intent or purpose of study

-begin with introduction that is easy to understand and open with statement that draws reader interest
strategies for writing "Statement of the Problem" sections
1. use a template
2. cite literature often
3. reference statistical information and quoates
Martin Seligman's Measure of Explanatory Style predicted retention rates of college freshmen better than students' SAT scores. Seligman's instrument is said to have ____ validity.
Criterion-related validity
Obtaining the same results on repeated administration of the same instrument is known as:
test-retest reliability
A researcher who is interested in the effects of low birth weight (LBW) on subsequent kindergarten readiness follows a group of LBW children through the first five years of life, collecting various psychological, social, and educational outcomes. She correlates all of these variables with the original birth weight of the child. The measure of birth weight is:
ratio
A researcher is interested in a construct she calls "school satisfaction." To measure this, she asks schoolchildren in grades 5-12 to fill out a 10-item survey about school experiences (e.g., "I feel challenged at school."). Students complete the questions on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). This measure of school satisfaction is:
interval
A researcher is interested in a construct he calls "school satisfaction." To measure this, he asks schoolchildren in grades 5-12 to circle either "yes" or "no" on a list containing 10 adjectives describing school (e.g., interesting, challenging, boring). This measure of school satisfaction is:
nominal
The number of aggressive acts on a playground observed during a school-day recess period is an example of a(n):
operational definition
With increasing sample size a researcher gains:
less sampling error
Choosing a sample by selecting participants because they are available and willing to participate in the study is known as:
convenience sampling
In a study of interscholastic athletics on academic achievement, a researcher first identifies state high school athletic associations in all 50 states. From this list, she develops the list of all secondary schools offering at least 12 interscholastic sports (6 girls, 6 boys). From that list of schools, she randomly selected 435 high schools across the country proportional to the population. (She does this by picking one high school from each congressional district.) From that list, she randomly selects the participants from one boys sport and one girls sport in each school. The result is a mailed survey to over 13,000 high school students participating in interscholastic athletics. This approach to sampling is known as:
multistage cluster sampling.
Choosing a sample by partitioning off the population based on selected characteristics (e.g., race) such that each characteristic is represented proportionately in the population is known as:
stratified sampling
Using some research participants to help you identify other possible participants in the population is known as:
snowball sampling
Choosing every "nth" individual or site in the population until the desired sample size is achieved is known as:
systematic sampling
Choosing a sample such that every member of the population has an equal chance of being selected is known as:
simple random sampling
Nonprobability sampling is characterized by choosing a sample based on:
availability of participants
The difference between the sample estimate and the true population score is the
sampling error
Compared to a summed scale, what is a disadvantage of a single-item score?
may not reliably and accurately reflect an individual's score
Which is the flow of data analysis as described in the text?
inputting, cleaning, assessing missing data, computing descriptive statistics
One way to handle missing data is to eliminate any participant who has at least one missing data point. The main disadvantage to this approach is that it:
reduces the size of the data set
What would be the percentile score of a student whose standardized (z-score) on the SAT is +2?
99
What would be the percentile score of a student whose standardized (z-score) on the SAT is –1?
16
What statistical test would be used to test mathematics achievement scores between students learning from the following three different pedagogical strategies: induction, theory only, theory + examples?
analysis of variance
Type II error
false negative - probability = beta
Type I error
false positive - probability = alpha
Measures of central tendency
mean, median, mode - value or score that represents the entire distribution
Measures of variability (describes the “spread” of the scores
range, SD
Measures of relative standing
percentile rank, calculated score like Z score
types of inferential statistics (5)
t-test, ANOVA, chi-squared, pearson correlation, multiple regression
Hypothesis testing:
A procedure for making decisions about results by comparing an observed value of a sample with a population value to determine if no difference or relationship exists between the values
Confidence interval:
The range of upper and lower statistical values that is consistent with observed data and is likely to contain the actual population mean
effect size
A means for identifying the practical strength of the conclusions about group differences or about the relationship among variables
things to consider when selecting a statistic
scale of measurement for Ivs and DVs, skew/normality of distribution
power
probability of a correct rejection
how many tables when reporting stats
one per test
major steps of discussion
Summarize major results
Explain why they occurred
Explain the implications of the results for the audiences
Advance limitations
Suggest future research
End on positive note
grounded theory research
systematic, qualitative procedure used to generate a theory to explain a process, action or interactions about a substantive topic at a broad conceptual level
types of grounded theory designs (3)
systematic, emerging design, constructivist design
key characteristics of grounded theory research (3)
process approach
theoretical sampling
constant comparative data analysis
core category
theory generation
memos
ethical issues in conducting grounded theory research
-not advancing purpose of the study
-creating power & authority imbalances in interviewing
-not building useful chain of evidence
-not engaging in a study that benefits participants
steps in conducting a grounded theory study
1. decide if GT design is best for research problem
2. identify process to study
3.seek approval & access
4. conduct theoretical sampling
5. code data
6. use selective coding & develop theory
7.validate theory
8. write GT research report
ways to evaluate grounded theory study
1.check for obvious connection between categories and data
2.chck if theory is useful conceptual explanation for process
3. ask if theory provides relevant explanation of actual problbems and basic process
4. check if theory can be modified as conditions change or more data arrives
5.ask if theoretical model was developed
6. ask if there is a central phenomenon or core category
7. ask if model emerges through phases of conding
8. ask if research tries to interrelate categories
systematic design
open coding, axial coding, selective coding
open coding
properties & dimensionalized properties
axial coding
researchselects one open coding category & examines it as the central phenomenon, then relates all other categories to it
selective coding
writing a theory based n the interrelationship of the categories from axial coding
emerging design
grounded theory exists at most abstract conceptial level, theory is not forced into categories, 4 essential critera are fit, work, relevance & modifiability
constructivist design
philosophical position between positivist and postmodern researchers, theorist explains individuals' feelings as they experience a process/phenomenon, mentions beliefs & values of reseach and does not use predetermine categories, narrative is explanatory and probes assumptions and meanings for studied individuals
process approach
sequence of actions and interactions among people and events pertaining to a topic
Theoretical sampling
The researcher chooses forms of data collection that will yield text and images useful in generating a theory
Constant comparative data analysis:
An inductive (from specific to broad) data analysis procedure in grounded theory research of generating and connecting categories by comparing incidents in the data to other incidents, incidents to categories, and categories to other categories
A core category
A category that can become the theme that describes or becomes the main theme of the process
theory generation
creation of an abstract explanation or understanding of a process about a substantive topic grounded in the data
memos
notes written by researcher through research process to elaborate on ideas about the data & coded categories, explores hunches
Why use grounded theory research?
1. to generate a theory
2. to explain a process, action or interaction
3. when you want a step-by-step systematic procedire
4. when you want to stay close to the data
Why is qualitative research called "interpretive" research?
It involves making a personal assessment of descriptions and themes.
Agar (1980) recommends to "read the transcripts in their entirety several times. Immerse yourself in the details, trying to get a sense of the interview as a whole before breaking it into parts." This is a suggestion to engage in:
preliminary exploratory analysis.
A researcher who is segmenting and labeling the text is engaged in:
coding
Sentences or paragraphs that all relate to a single code are called:
test segments
Similar codes aggregated together to form a major idea in the database are called:
themes
In conducting qualitative research on student workload, a researcher will analyze views from students, student life personnel, university professors, and an academic administrator. The purpose of doing this is to develop:
multiple perspectives
Information that disconfirms the themes produced in the study is known as:
contrary evidence
Saturation in qualitative research refers to:
all themes have been identified
Generating a chronology of events or a conceptual model of events is known as:
interconnecting the themes
A written passage summarizing the findings of the data analysis is known in qualitative research as:
narrative discussion
Qualitative researchers include personal reflection on the meaning of the data:
because their views influence their interpretations
The process of corroborating findings from different individuals, data sources, or collection methods is known as:
triangulation
A third-party evaluation of different aspects of the research is known as
external audit