Conducting Research
by Lawrence T. Orcher
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This text is for those doing their first empirical study
Chapters 1-10 go over setting up to prepare a proposal
Model literature reviews through basic stats are included in this book
Chapter 1
Selecting Tentative Topic
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Research is process of systematically collecting and interpreting
information
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Library research – collect/interpret info others wrote
Empirical research – making observations to collect new info (data) which is
analyzed to assist in making interpretations
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Observations can be overt (direct) or indirect (interviews, surveys)
Chapter 1 (cont.)
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Several broad areas of interest identified based on
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Everyday observations
Theories (test a theory)
Beware of availability of participants, ethical considerations (no phys/psych harm),
audience (my approval), personal needs (your interests)
Can replicate published studies
Use demographics to narrow OR broaden a topic
Start searching the literature after broad area identified then may narrow or even
change topic
Chapter 2 - Locating Literature and Refining the Topic
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Desire SCHOLARLY/PEER REVIEWED/REFEREED articles (most recent and are
checked)
Databases of published research available through paid subscriptions (by TC)
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ERIC; Infotrac I – Onefile; Proquest–Education Journals
Use the database thesaurus to better define topic and find related terms
Topic should narrow, become better defined after literature search is completed
Chapter 2 (cont.)
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Goal is 50-100 abstracts to examine at one time
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Use Boolean operators to narrow (AND) or widen (OR) search
Review both negative and positive studies
Focus on recent studies (last 10 years)
Tutorial on APA citations on TC webpage, select Library Homepage, then Tutorials
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Good idea to print the APA Quick Reference Guide
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For federal statistics, searching by topic, go to www.fedstats.gov
Chapter 3–Preparing (writing) a Literature Review
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Lit review establishes need for your study and gives context to it
See table in text for organizing sources
Lit review is an essay about the literature
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Synthesize studies about a topic
Do a topic outline first (see example in text)
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Group sources together under given topic
NOT a source-by-source listing/discussion
Use APA Level 4 subheadings if lit review is long
Review examples in book
Chapter 3 (cont.)
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Critical review is needed…if there is a flaw in a study, point it out (see
book examples of before/after)
Avoid statements of fact/truth/proof…all research is flawed and so use
tentative language (Research implies, suggests, etc.)
Point out gaps in literature, and how your study will address the gaps
Chapter 3 (cont.)
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Placement of surnames for in-text citations
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Use Harvard method (surnames, year)
Author can be subject of sentence (draws attention to who the author is)
Author can be parenthetical (putting the source in parenthesis puts focus on the content of the
sentence…this is preferred)
Discuss relevant theories in detail before studies
Reader should see a connection between lit. rev. and
RQ/purpose/hypotheses…logically RQs come at end
See 3 model lit reviews at end of book
Chapter 4 - Writing Research Hypotheses, Purposes, and
Questions
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These guide your study and identify the variables included
They are based on previous research or theories and logically follow the literature review
Can be given in one of three formats
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Hypothesis – a statement of a predicted outcome from the study
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Purpose – a declarative statement that identifies the variables of interest, what was studied (no expected outcome
is given)
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If cause/effect the independent and dependent variables must be included
Do not give numerical values (i.e. Group X will score 50% higher than Group Y…this means you’d be wrong if it was 49% or
less OR if 51% plus)
Should not be a value judgment…must be something observable
Often this is used instead of a statement of the problem
Question (RQ) – alternate wording of the purpose, given at the end of the lit review (numbered as RQ1,
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RQ2…same for hypotheses H1, H2…)
Chapter 4 (cont.)
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Clarifying the Purpose/Hypotheses/Questions
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Often include information about the population, but not usually about the
methods of measurement
Sometimes have research questions and hypotheses that link to the
research purpose statement
Reviewing the literature helps clarify the RQ etc.
See text examples, especially before/after cases
Chapter 5 - Selecting a Research Approach
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Choose a design (approach) after form RQ/Hyp
Experimental v. Non-Experimental
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Experimental explores cause and effect relationships
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Has at least one independent variable (IV)/cause which is under control of the researcher
Has at least one dependent variable (DV) or outcome/effect
Generally form two groups, one (experimental or treatment group) gets the new treatment and the other
(control group) does not
Random assignment to trtmt/control key element of true experimental designs
Quasi-experimental is used when random assignment is not possible (see Ch. 22)
Chapter 5 (cont.)
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Major types on non-experimental research
 Causal-Comparative (aka ex post facto)
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Used when random assignment to trtmt/control is not possible
Compare existing groups to identify causal sequence
Often matching is used to ensure the comparison groups are equivalent on variables that
might affect the DV
Chapter 5 (cont.)
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Major types on non-experimental research (continued)
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Surveys (provide a description of what exists, aka descriptive research)
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Survey a population (census) or a sample on attitudes/opinions
Mailed questionnaires often used (see App. A for ways to improve response rates…use incentives, keep it short,
preaddressed/stamped envelopes, standard vocab/grammar)
Interviews (more labor intensive/expensive, but get more in-depth data)
Achievement surveys – measure knowledge levels
Groups’ opinions are often compared based on demographic factors (such as age, gender, race, etc.)
Chapter 5 (cont.)
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Major types on non-experimental research (continued)
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Correlational Studies (Appendix B has hints)
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Use a correlation coefficient statistic to assess relatedness of two quantitative variables (ACT
and college GPA)
Coefficient is a numerical measure from 0 to 1 (plus or minus) that tells how related the two
variables are
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Closer to zero, less relationship; further from zero more
Sign of the coefficient tells if the relationship is positive/direct (hi/hi and lo/lo) or negative/inverse (hi/lo
and lo/hi)
Chapter 5 (cont.)
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Major types on non-experimental research (continued)
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Document/Content Analysis (examine existing records to look for
patterns/themes)
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Good samples available
Documents are secondary sources…may not be accurate
Program Evaluation (hybrid of experimental & non)
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Program is a treatment, but usually not random assgnmt
Sometimes focus on implementation process (formative) rather than outcome (summative) –
App. D info
Long timeframe needed
Chapter 5 (cont.)
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Quantitative v. Qualitative (or breadth v. depth)
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Quantitative
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Qualitative
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Numbers based, standardized procedures to obtain data, rather rigid with no personalized interactions with
subjects
Generalization is important
See example list of projects in text
Not structured nor laid out in advance
Direct involvement with participants increases chances of bias, so usually best done by trained researchers
Hints on which to use: Some topics are inherently quantitative (including those w/
hypothesis since need structured study & measuremt); some topics can be studied
with either approach; emerging topics may need qualitative
Chapter 6 - Looking Ahead to Participant Selection
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How many and how selected
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Size
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Smaller numbers often allowed in research class projects
Experimental and qualitative usually smaller samples than quantitative and descriptive
studies
Either way a minimum of 30 and maximum of about 1000
Selection method (most important…representation is essential)
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Sampling methods are common criticisms of research studies
Chapter 6 cont.
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Random sampling (for quantitative research) – all members of the population must be identified
and have an equal chance of being selected
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A random sample is an UNBIASED sample
Use names in a hat (or equivalent w/ table of random #s)
Often hard to identify all population members
Sometimes those selected do not participate (low response rates on mailed questionnaires in particular)
Stratified sampling used to ensure subgroups are represented (usually proportional…using
volunteers w/in strata not ok)
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Chapter 6 (cont.)
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Sampling Methods (continued)
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Convenience sampling (aka accidental sampling)
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Presumed to be biased; using volunteers is a bad idea
Often used to pilot test instruments/materials or for exploratory studies
Findings open to criticism (should be interpreted with caution)
Purposive sampling
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Often used in qualitative to gain depth in understanding a group that has information of interest
(such as women officers in large corporations)
Criteria for inclusion must be clearly identified and based on specific reasons; then select a clear
method to identify participants…do not use convenience OR volunteers
Chapter 6 (cont.)
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Using the literature to plan sampling
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Note how many tend to be used in studies reviewed in the literature and how they were selected
Pay attention to the Limitations sections of published research reports…avoid the sampling errors
identified there; also can identify needed populations to study
Ch. 11/12 will cover this in more detail
Chapter 7 - Looking Ahead to Instrumentation
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Methods of measurement (tests, questionnaires, interview schedules, observation
forms, etc.)
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Can use ones from the literature
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Have established validity
Allows comparisons between studies
Any errors in them will apply to your study
Can modify ones from previous studies
Devising new instruments
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Must be able to defend validity/reliability
Pilot test these
Chapter 7 (cont.)
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Validity – extent to which an instrument measures what it is supposed to (Ch. 13)
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Construct validity (correlate the new measure with some other measure related to it)
Expert judgment (content/facial validity)
Qualitative (Ch. 15) focuses on credibility (member checks, reviews by participants,
triangulation of data sources)
Reliability – extent of consistency in results (Ch. 17)
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Number of items is important
Objectivity of items is necessary
Qualitative (Ch. 15) focuses on dependability (use more than one person to interpret data,
triangulate measures…use interviews plus surveys)
Chapter 8 - Looking Ahead
to Quantitative Data Analysis
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Levels of data (nominal, ordinal, interval, ratio) dictate how statistical analysis is
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done
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Nominal (named categories)
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Still just names even if numbers assigned as name tags (such as 1=male, 2=female); the numbers have no
quantitative meaning
For a single variable, use frequency/percent tables and picture with pie or bar charts (percent=part divided by
whole)
For relationship between two variables, use cross-tabulation
(also called a contingency table)
For inferences from sample to population, use chi-square test
Ordinal (named categories with relative order, such as class rank: freshman, sophomore, junior,
senior)
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Analysis is similar to nominal
Chapter 8 (cont.)
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Interval (continuous, quantitative measures)
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For one variable, use mean and standard deviation and picture with a histogram)
For two variables (both interval) calculate correlation coefficient to see relationship between them; look at
direction (positive/direct v. negative/indirect) and strength (weak, r=0 ….. strong, r=1.0)
For two variables (one nominal and one interval), compare means and standard deviations of the two groups
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For inferences to pop. use t-tests, ANOVA, significance of correlation
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With experiments can compare the net gain/loss from pre to post test for the control v. experimental group
Ratio (continuous, quantitative but with an absolute zero)
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Analysis is similar to interval
Chapter 9 - Looking Ahead
to Qualitative Data Analysis
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Only interviews are covered here
Qualitative has data collection and data analysis intermingling
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Memo writing during data collection (journal of researchers ideas/impressions)
Reflection and reframing of interview questions
Collect until reach data saturation (no new info is being gained with successive
interviews)
Chapter 9 (cont.)
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Two general approaches
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Grounded Theory Approach – inductive reasoning (theories developed based on
data collection)
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Open coding (track themes)
Axial coding (temporal, causal, associational, valence, spatial)
Core categories developed
Describe process used in analysis
Consensual Qualitative – several people review data and come to consensus about
results (auditor checks)
Analysis techniques
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Chapter 9 (cont.)
Enumeration (count times theme/construct is mentioned)
Selecting quotations (to support findings)
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Inter-coder agreement (reliability check)
Diagramming (see text example)
Peer debriefing (other researchers review/concur)
Auditing (independent review)
Member checks (go back to participants for review)
Identify range of responses
Analyze discrepant cases
Chapter 10 – Preparing a Preliminary Research
Proposal
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Proposal is a plan for conducting research (the proposal allows feedback on its accuracy) – see TC
guidelines
Title – brief statement that names the major variables investigated (and subjects sometimes) as
given in the RQ/purpose/hypothesis (see text examples)
Introduction chapter introduces the topic/study by giving the
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Problem area/background info (use limited number of references here)
Definitions (conceptual and operational…see text examples)
Significance of study (why it’s important to do the study)
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Note that qualitative generally has some info about how the researcher is directly related to the problem (personal
experiences and perspectives) since this may influence collection/interpretation of data)
Literature review
RQs/hypotheses
Chapter 10 (cont.)
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Methods is the second chapter and as a minimum covers participant selection and
instrumentation
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Participants – discuss population, how subjects will be selected, number selected, permission,
etc.
Instrumentation – describe measurement tools used and cite sources (see text examples and
TC guidelines)
Procedures section (explains how the research will be carried out)
Data analysis section (explains how the data will be analyzed statistically)
Threats to validity (limitations/problems with the data)
References/Appendices follow the last chapter
Chapter 11 – Participant Selection in Quantitative Research
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Population is the group the researcher is interested in
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Accessible population differs from the entire pop, but must be cautious with generalizations
from an accessible pop.
Sample is a subgroup drawn from a population
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Biased sampling occurs when everyone does not have equal chance of being selected as a
participant
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Bias can be subtle or obvious
Volunteers, convenience sampling most common biases
Need a system to assure equal chance to everyone – called RANDOM SAMPLING
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Even random sampling can have random (chance) errors, but not systematic ones (calculate and report
margins of error)
Larger samples have less error
Allows inferences
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Simple random sampling
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Chapter 11 (cont.)
Names in a hat or using table of random numbers (in book)
Number each pop member with equal digits, then select random starting place in table
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Stratified sampling
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Systematic sampling…select every nth one from list
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Divide population into subgroups (and draw from each group…usually proportional)
Need to make selection through entire list
Beware ordered lists…best to use randomly order lists
Cluster sampling
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Population is in groups, ea group is a cluster (Girl Scouts/troops)
Select enough clusters and select them randomly
Chapter 11 (cont.)
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Sample size – influenced by several things
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Pilot study to help determine return rates
Number of subgroups to be examined (more needed, increases the sample size)
Importance of precise results/need to find a small effect (such as heart attacks…occurrence is
small so need larger sample size)
Statistical significance – larger samples have greater chance of significance
See table of suggested sample sizes in book
Chapter 12 – Participant Selection in Qualitative Research
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Purposive Sampling (not so interested in unbiased samples so can generalize) –
hand pick participants since they possess the needed info
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Criterion sampling – very specific criteria must be met to be included; can purchase lists of
those meeting criteria or get from professional associations
Random purposive sampling – random selection from a list of those who meet the criterion
Typical case sampling – select what are believed to be normal or typical cases (reduces
complexity of data…good for data analysis; beware data don’t apply to most cases)
Chapter 12 (cont.)
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Extreme/deviant sampling – identify the criteria (define them) and take from one or both
extremes
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Useful to refine measurements for later typical case sampling
Intensity sampling – select those with intense feelings or experiences
Maximum variation sampling – making sure participants have full range of characteristics
(age/income/gender/etc.)
Homogeneous sampling – opposite of above; want participants to be similar on characteristics
Opportunistic – individuals selected to participate as the opportunity arises since they may have
info of interest to the study
Chapter 12 (cont.)
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Stratified purposive – identify subgroups then select from them so all subgroups are
represented
Snowball (aka chain/network) sampling (also used in quantitative) – for hard to find
participants…identify one and ask for referral to others, i.e. drug addicts
Combination purposive sampling – combine any of the above
Improving convenience/accidental sampling – if must be used
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Use a larger group selected from multiple locations
Collect demographic data so readers can describe the participants – readers can then judge
representativeness
Chapter 12 (cont.)
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Sample size in qualitative research
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Much smaller than in quantitative (in one published report average N in qualitative was 14
versus 432 in quantitative)
Focus is more on in-depth information gained from participants and requires more time per
participant
Saturation sampling – stop when new insights and info are no longer being provided by
participants (point of redundancy)
Chapter 13 – Instrumentation in Quantitative Research
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General rules for good measurements:
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Standardization – use same methods for administering tests to each subject
Objectivity – Use scoring that does not allow subjective interpretation
Social desirability bias – Eliminate this tendency of people to give what they
believe is an acceptable answer by using anonymity
Chapter 13 (cont.)
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Reliability and Internal Consistency
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Reliability refers to consistency in measurement (getting the same reading with multiple
measures)
Test/retest reliability – administer test to same group twice (a week or two apart) and should
get consistent scores
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Measure this using reliability coefficient (0 to 1.0 score and the closer to 1.0 the more consistent); Look for
0.85+
Guessing/random answers; inconsistent administration of tests; changes in subjects; ALL can affect reliability so
try to limit these
Internal Consistency from one part of the test to another
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Can calculate split-half reliability (odd/even) or Cronbach alpha
On surveys ask positively and negatively worded questions
Chapter 13 (cont.)
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Both test/retest and internal consistency provide info that helps in understanding
how well a test works
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Best to report a couple of reliability measures
Inter-observer Reliability – extent to which 2 observers get the same score,
especially important when scoring is not objective
Chapter 13 (cont.)
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Validity and its relationship to reliability
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Validity is the extent to which an instrument measures what it is intended to measure
Measures can be highly reliable but still not valid
Judgment vailidity
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Content – based on expert opinion of the appropriateness of the contents of a test/scale – does it cover the whole
content
Facial – on its face does it appear to measure what it’s supposed to
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Best to report a couple of reliability measures
Criterion validity – how well measure correlates with some criterion
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Concurrent – criterion occurs at same time as measure
Predictive – criterion occurs at a future time (ACT score & college GPA)
Chapter 13 (cont.)
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Construct validity – cannot observe some things directly (love, depression, anxiety),
thus construct their existence based on observation of behaviors that indicate they
exist
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Construct validity – refers to the extent to which an instrument yields scores that are consistent
with what is known about the construct
Calculate it by correlating scores with other things that indicate the construct ( correl. happiness
scale scores to an anxiety scale you developed, should get negative correl.)
Need to have several measures of validity
Chapter 14 – Writing Objective Instruments
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Often can find and use existing instruments (ETS Test Collection database)
For this program generally develop your own to develop the related skills…text covers three most
common ones
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Develop a plan
Have plan reviewed
Revise plan
Write items based on the plan
Have items reviewed
Revise items, put into instrument form
Pilot test instrument
Revise after pilot test
Chapter 14 (cont.)
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Attitude scales - Attitudes are orientations toward something and they affect actions
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Planning
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Identify the components of the object being studied (i.e. job satisfaction made up of working conditions, pay, benefits,
etc.)
For each component write 3-5 items plus 1-2 on overall attitude (I like my job
Review/feedback at this stage
Writing
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Likert scales are used to measure attitudes by providing descriptive statements and asking whether respondents
agree/disagree (SA to SD)
Each statement has only one point (see text example)
Use favorable/unfavorable (neg/pos) wordings
Review items/revise
Pilot testing
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Use sample similar to popul. to be tested; think aloud/written feedback
Review/revise
Chapter 14 (cont.)
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Observation Checklists – List of behaviors/characteristics for which observations should be made
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Planning
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Writing
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Identify the behaviors/characteristics and context for observing them
Have these reviewed, identify new ones/revise
Each item refers to only one single discrete behavior (no ANDS); see Text
Also often ask about speed, duration, and success of behaviors
Pilot testing
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Have at least two people do observations independently and see if they agree on items checked
Review/revise (may need to clearly define behaviors on checklist)
Chapter 14 (cont.)
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Achievement tests – Usually objectively scored, multiple choice
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Planning
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Writing
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Base test items on objectives – try to cover all or at least most important ones
Have these reviewed/revise
Multiple choice – one correct choice and at least two plausible distractors
Beware of ambiguous items (see example in text)
Review/revise
Pilot testing
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Try to include some high and low achievers
Review carefully and compare performance (of high/low performers)
Revise items
Chapter 14 (cont.)
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Note these are only the beginning steps in writing up instruments
It’s a complex process
You can get a doctorate in tests and measurments, so obviously it is a much more
complex task than we have just reviewed
You will learn a lot by developing your own instrument for your project
Chapter 15 – Instrumentation in Qualitative Research
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Qualitative researchers strive for credibility
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Member checks used to verify findings (feedback from participants on correctness of results)
Prolonged engagement in the field gives credibility
Triangulation of data sources (multiple sources give same results)
Triangulation of instrumentation (multiple instruments used for each participant)
Chapter 15 (cont.)
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Interviews are most common data collection method, particularly for beginning
qualitative researchers
Good source: The Long Interview by Grant McCracken
Pay attention to interviewer selection and behavior so results are not contaminated
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Often match interviewers with participants on characteristics, behaviors, etc. (to gain
access, trust)
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Interviewer self disclosure (interviewer needs to give self-disclosure in both collecting and
analyzing data as well as reporting)
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Chapter 15 (cont.)
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Interview protocols (steps and questions used in the interview)
Simi-structured/open-ended (core questions with some probing allowed)
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Initial questions used to establish rapport
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Can adopt previously used protocols or develop, review, pilot test your own
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Demographic questions should be standardized
Recording responses/note taking – taping is best, but may inhibit responses; make notes
immediately, describe process in reporting
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Chapter 20 – Experimentation and Threats to External
Validity
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Experiments: researchers administer treatment to part of a population to determine
effects on an outcome variable
Experiments explore cause and effect relationships
Independent variable (IV) in an experiment is cause
Dependent variable (DV) in an experiment is the effect
Simple experiments have one IV and one DV; complex ones have several of either
(see text chart)
Chapter 20 (cont.)
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Hawthorne effect – the result changes just because subjects are being observed,
not because of the experimental treatment
Control groups are used to make sure the HE is not occurring
See text examples before and after use of control groups
Chapter 20 (cont.)
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External validity of experiments (generalizability of results to outside the experimental setting)
Threats to external validity
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Selection bias – failure to use true random sampling in choosing participants so they are not representative of the
population
Reactive effects of exper. setting – a lab setting used in an experiment might differ from the field situation (so
different reactions occur in people outside the exp. setting)
Reactive effects of testing/pretest sensitization – subjects in experiment perform differently because sensitive to
what effect you are looking for
Obtrusiveness of measurements – try to use unobtrusive measures so no effect on experiment (or do field
experiments)
Multiple treatment interference – if several different treatments given to same subjects, can interact
Chapter 21 – Threats to Internal Validity and True
Experiments
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Internal validity – Did the independent variable really cause a change in the
dependent variable (see text example)
True experiments have a treatment and control group…random assignment is
essential
Graphically described as:
ROXO
RO O
Chapter 21 (cont.)
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Threats include other things besides IV that might have caused a change in the DV
 History – any external event that might cause a change in the DV (control for this by having a
control group to compare the treated group to)
 Maturation – natural changes that occur in subject (control and treatment groups should
mature at same rate)
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 Instrumentation – refers to possible changes in the instrumentation (how the test was
administered from pre to post testing); control/trtmt groups should have same problems
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Chapter 21 (cont.)
Threats (cont.)
4. Testing (practice effect) – control group should take care of this
 Statistical regression/regression toward the mean – those really high (or low) should move down (or up) on
subsequent tests; random selection and control group take care of this
 Selection – nonequivalent groups compared due to nonrandom assignment (as opposed to random selection)
Can use a Solomon 4-Group design which adds two more groups (one control and one treatmt) that do not have a pretest
(to avoid pretest sensitization)
ROXO
RO O
R XO
R
O
Chapter 21 (cont.)


True experiments have random assignment – it’s what makes groups equivalent
(not the pretest)
Need high external and internal validity to trust results!
Chapter 22 – Pre-Experiments and Quasi-Experiments



Random assignment is the hallmark of true experiments since it controls all threats to internal
validity
Sometimes cannot randomly assign so go to the next best
Pre-experimental designs



Single group pretest-posttest design (O X O) - all threats apply
Single group posttest only (X O) - cannot measure change
Static group design – 2 groups, no pretest, no random assignment (dashed line indicates intact groups used)
X O
O
Chapter 22 (cont.)

Quasi-experimental designs




Non equivalent control group design, 2 intact groups
O X O
(note that equal pretest scores needed)
O
O
Time series design – one group, baseline then treat
O O O O O X O O O O O (observe several, treat, observe)
Equivalent time samples design (X1 trmt and X0 none)
X1O X0O X1O X0O X1O X0O X1O X0O
Quasi- and pre-experimental sometimes used when cannot do true experiments,
but need to be cautious with interpretations
Chapter 16 – Descriptive Statistics for Quantitative Qualitative
Research

Descriptive statistics summarize data and are used to present data



Quantitative studies – all variables are analyzed statistically
Qualitative studies – demographic variables are analyzed statistically
The level of data determines what kinds of statistical analysis is done (nominal, ordinal,
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interval, ratio)


The higher levels of data (interval/ratio) allow
more analysis techniques than lower levels
Use measures of center and variation/spread/dispersion


Center: mean, median, mode
Variation: range, IQR, standard deviation
Chapter 16 (cont).
Nominal data – named categories or labels (gender, race, location)

For univariate analysis:



Report frequency/percent (% calculated with part divided by whole); pie charts
Report most frequently given answer (Mode)
For bivariate analysis:

Report frequency/percent by category using
a contingency (aka crosstabs/crossbreak) table
Chapter 16 (cont).


Ordinal data – named categories that have relative order (places in a race, class rank)
For univariate analysis:




Report frequency/percent; bar/column charts
Report most frequently given answer (Mode) and the middle answer (Median) Note: If odd number of scores the
median is the middle one, if even number of scores, average the two middle answers
Report the range (highest – lowest) and the innerquartile range (IQR)
For bivariate analysis:


Report frequency/percent by category using a contigency (aka crosstabs/crossbreak) table
Compare medians for different groups
Chapter 16 (cont).


Interval and ratio data – quantitative numbers that tell how much with equidistance between
points; ratio also has an absolute zero, so no negative numbers (test scores, age in years,
number of minutes late)
For univariate analysis:




Picture distribution with histograms, stemplots
Report mode and median plus the Mean (mathematical average, calculated: ∑x ÷ N) M=population mean;
m=sample mean or x-bar
Report the range and SD (average distance of all points from the mean using formula to calculate) SEE TEXT
EXAMPLES FOR 3 GROUPS…
larger SD means more variation/spread/dispersion
For bivariate analysis:



Compare distributions
Compare means, SD for different groups
Correlate
Chapter 16 (cont).

Normal distributions – bell shaped and symmetrical




Centerline is the mean (and if normal, also median and mode)
68% of cases within plus or minus 1SD from the mean; 95% of cases within plus or minus
2SD from the mean; 99.7% of cases within plus or minus 3SD from the mean
When have skewed distributions, mean and SD are warped, so use median and range (or IQR)
instead
See IQR example in text
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