Elspeth Slayter, Associate Professor School of Social Work, Salem State University

Administrative matters & check-in Sampling strategies Designing your sampling strategy Critiquing sampling methods Group check-in

Choose intervention thoughtfully – with or without research ImplementEvaluate Research QualitativeQuantitative Program evaluation Process/formativeOutcome/summative

All are types of Research Looking up references, compiling existing information Practice evaluation (a.k.a. program evaluation) Social research that informs social work practice in some way 4

Justify: Theory tells us we need to do this study because… Structure: Overtly named as part of the research design (used to structure study process) Interpretation: Used in discussion of findings (relating findings back to theory) Creating theory and/or grounded theory Testing of a theory

Integrate Synthesize Spread it all out on the dining room table!!! Include ALL points of view, not just those you agree with Literature Review Process of EBP Program Evaluation Social Research

Cross-sectional Cannot determine causation Longitudinal Pre-test to Post-test Post-test only Match the question to the method!

Critical consumption of research AND skills to evaluate practice Learn to critically consume research Learn to develop practice evaluation plans Consider the process of evidence-based practice beyond evidence- supported interventions

Theory = soul Research question = eyes Sample = body Findings = movement of the body Subjects vs. participants vs. respondents People you select based on certain CRITERIA What are your criteria?

Research design 101: Sampling Exposure/treatment group (i.e. sample) Control/comparison group No control or comparison group

Population (a.k.a. Universe) complete listing of a set of elements having a given characteristic(s) of interest Sample Infers population characteristics from a subset of the population Saves money Saves time Can be more accurate – don’t need whole pop

A list of population members May get a complete listing - but often population and sample frame are different Example: DCF Lowell Caseworkers Terminology heads-up: Differences between the sample frame and population are referred to as: “sample frame error” or “sampling frame error”

Not representative/biased in some way Non-response error: created when chosen sample members do not participate Creates two problems: Need larger initial sample size Non-respondents may differ from respondents (“questionnaire freaks?”)

Representativeness – so you can make informed guesses about the population (i.e. so you can generalize to the population) Mirror the population you seek to study as much as possible Chance differences always exist - probability sampling helps

Mathematical likelihood that an event can occur Ranges from 0 to 1 (chance of occurring) Application to sampling: The larger the sample, the more chance there is that you will have a group that is representative of the whole population

Study aim: grades and social adjustment of students in a U.S. 4 th grade public school suburban classroom You walk into the classroom to pick a random sample, you choose the first two rows of students. What is the probability that you have a representative group? What will this do to your results?

Probability: each population element has a known, non-zero chance of being included in the sample Non-probability (a.k.a. accidental): can’t mathematically estimate the probability of a population element being included in the sample

Convenience Purposive/judgment: Often used in qualitative research Hand picked as representative of a population of interest Often a small sample in which researcher tries to represent all segments of population Snowball: Appropriate for small specialized populations Each respondent is asked to identify one or more other population members Drawbacks: Those with more ties to sample members are selected Similar people are more likely to be connected/selected

Rarely, regardless of size, do these samples prove to be representative Not recommended for descriptive or causal research without careful commentary on generalizability. May be better at understanding relationships between variables than at making descriptive estimates

Compare sample characteristics or findings to census/random sample studies Speculate carefully about bias, and how it is likely to have affected results Collect sample where population is likely to be Cultivate diversity in the sample (oversample, quotas) personal, subjective selection Use of “weighting”

Each sample element has a known, non-zero, equal chance of being selected from the sampling frame Examples: Lottery numbers Name in a hat Random digit dialing to approximate random samples Random numbers table

Systematically spreads sample through a list of population members Example: Population = 10,000 people, need sample of 1000, select every 10th name In nearly all practical examples, the procedure results in a sample equivalent to systematic random sampling

Natural sub-groupings Random sample taken from sub-groupings at a fixed rate Examples: Gender 50%/50% or ??? Groups based on known proportions Remember “weighting?”

Find similar studies which are successful and getting sufficiently reliable results Collect sample size large enough so that when divided into groups, each group will have a minimum sample of 100 or so (ideal) Calculate the cost of interview and data analysis per respondent. Divide total budget by this amount to get maximum sample size.

See Padgett

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