Ch. 2: Planning a Study (cont’d)

pp. 46-56

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THE RESEARCH PROPOSAL In all empirical research studies, you

systematically collect and analyze data If your data are qualitative (in form of

words, symbols, etc.) you must use different strategies and data collection techniques than if the data are numbers

Each data form has strengths; your goal is to fit the form of data to a specific research question and situation in a way that utilizes its strengths (p. 46)

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1. When do you focus the research question?

It depends partly on the type of data you plan to use If you plan to gather quantitative data,

you’ll need to have a focused question before you actually collect the data, so you know where to look

If you plan to collect qualitative data, your question will get focused as you actually gather and examine your data

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2. To what universe can you generalize a study’s findings? Only rarely do we want to restrict our

findings to the specific units or cases we actually study; instead, we want to generalize to a broader category of people, organizations, and other units universe: a broad category of cases or units to

which the study findings apply e.g., if we are interested in NYC high school

students, we can’t possibly contact all high school students in the city; rather, we’ll be able to contact only a subset of them, yet, we will still attempt to generalize our findings to “NYC high school students”

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3. Which type of research path do you follow? A path is a metaphor for the sequence of

activities you do Generally, with quantitative data, you

follow a linear path, with qualitative data, a nonlinear path linear path: a relatively fixed sequence of steps

in one forward direction, with little repeating, moving directly to a conclusion

nonlinear path: advancing without fixed order that often requires successive passes through previous steps and moves toward a conclusion indirectly

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4. What do you examine? Research with quantitative data

focuses on variables and the relationships among them Variable: a feature of a case or unit that

represents multiple types, values, or levels Research with qualitative data tends

to examine a limited number of cases in-depth; concerned with discovering “meanings” of different social situations

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Types of variables independent variable: the variable of

factors, forces, or conditions acting on another variable to produce an effect or change in it

dependent: the variable influenced by and changed as an outcome of another variable

intervening: a variable that comes between the independent and dependent variable in a causal relationship

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A 3-variable example from Durkheim’s study of suicide

Theory: married people are less likely to commit suicide than single people because they are more socially integrated

Restated in variable terms: being married (independent) increases social integration (intervening), which in turn reduces the suicide rate (dependent)

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Hypothesis Hypothesis: a statement about the

relationship of two (or more) variables yet to be tested with empirical data

5 features of a causal hypothesis: It has at least 2 variables It specifies how the variables are connected (which

is the cause, which is the effect) It includes a time order assumption It can be restated as a prediction It can be supported or falsified with empirical data

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Null hypothesis: a hypothesis that there is no relationship

between variables, that they do not influence one another If the evidence supports the null hypothesis, you

are forced to conclude your alternative hypothesis is false

But if the evidence rejects the null hypothesis, then the alternative hypothesis remains a possibility

Null hypothesis is used because researchers are very cautious – similar to the idea of innocent until proven guilty

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5. How do you look for patterns in the data?

With quantitative data, you use charts, tables and statistics to see patterns; you connect the patterns with your research question.

With qualitative data, you identify patterns (sequences, cycles, contrasts) in the data (observed events, conversations, situations) as they appear in a specific context.

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6. What type of explanation will you use?

causal explanation: a type of research explanation in which you identify one or more causes for an outcome, and place cause and effect in a larger framework Used in both quantitative and qualitative

research, but more common in quantitative

grounded theory: ideas and themes that are built up from data observation More common in research using qualitative data

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7. What are the units of analysis of your study?

unit of analysis: the case or unit on which you measure variables and gather data

Units of analysis influence how to gather data and the level of analysis

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8. What is the level of analysis of your study?

level of analysis: the level of reality to which explanations refer, micro level to macro level it’s a mix of the number of people, the

expanse of geographic space, the scope of the activity the length of time

Level of analysis influences assumptions, concepts and theories you will use, as well as the appropriate units of analysis

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Warning: Avoid Spuriousness

spuriousness: when two variables appear to be causally connected but in reality, they are not because an unseen third factor is the true cause

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Fig. 2.11 Spuriousness Example

Relationship between Illegal Drugs & Suicide

Illegal drugs

Emotional problems & community

disorder

SuicideObserved association

True cause