APA, Ch. 5, 7, 8, 9 Flashcards by Steven Smith

A sample from a population is called?

Sampling

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Parts of a research paper

Structure-content-citation rules

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The ultimate goal of a sample is to?

Generalize (external validity + represent)

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Is the accuracy with which the results of an investigation maybe generalized to a different group from that one study

External validity

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When an investigator is interested in studying a group of people with particular characteristics of interest, that group is known as a

Population

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We might instead select a subset of the population or universe thought to represent the entire group, a subset known as a

Sample

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Is the degree to which the samples parameters DIFFER from the parameters of the population from which it was selected

Sampling error

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There are two sampling methods

Probability sampling and nonprobability sampling

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Is it generally most preferred by researchers. It involves the selection of elements from a population or universe in accordance with some set of mathematical rules, thereby permitting calculation of the probability of sampling error.

Probability sampling

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Is the most elementary form of probability sampling. Each element in the population or universe is afforded an equal opportunity of being selected to the sample.

SRS

Simple random sampling

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The second variety of probability sampling, like simple random sampling, requires a complete sampling frame, from which every element is selected following a random start

Systematic sampling

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Like the previous two techniques, ______ requires the generation of a complete sampling frame. It’s particular advantage, however, is that it permits the researchers some assurance that elements with particular characteristics are included in the sample.

Organizing the elements in the sampling frame into subsets based on some characteristics of interest, or using one of the previous two techniques to select a proportional representation from each subset to the sample.

Stratified sampling

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Is a probability sampling technique that is particularly useful when dealing with a very large target population or universe when it would be inconvenient or impossible to generate a complete sampling frame of elements.

The choices of elements are continuously narrowed until a complete sampling frame becomes possible, then the final elements are chosen from the sampling frame in accordance with one of the previous three sampling techniques

MCS

Multistage cluster sampling

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While most researchers prefer probability sampling techniques, there are numerous occasions went non-probability must be used

Nonprobability sampling

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How can we improve sampling?

We can replicate (different place, different people, different time)

We can use theory or logic to support the claim

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Based on mathematical rules

Probability sampling

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Uses some form of random selection-requires a complete frame.

Probability sampling

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n = sample size,

Systematic sampling

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Uses proportional reduction Tatian’s of a certain valuable(Gender, ethnicity, or age)

Males = 60%, females = 40%

Stratified sampling

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Separate the population into mutually exclusive sets (strata)

Example = sex-male •female • draw random samples from each stratum by using one of the previous two techniques

Stratified sampling

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Useful for a very large target population-when it seems impossible to generate a complete sampling form

Multistage cluster sampling

MCS

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Not based on probability (no mathematical rules, not random

Nonprobability sampling

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Availability sampling, relies on a available sample

Convenience sampling

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Judgmental sampling, selecting sample based on specific characteristics of interest to the researcher.

Example = topic-combination effectiveness in the successful business.

IBM or Microsoft because of success

Purposive sampling

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Selecting sample according to some quotas-but not randomly. Represents major characteristics of population (ethnicity, gender) by sampling proportional amount of each.

Quota sampling

Network sampling. One person recommends another, who recommends another, who recommends another. We use when = hard-to-reach populations.

Snowball sampling

Less effort, less time, less resources.

Nonprobability sampling

Limitations? Possible to misrepresent population. Cannot estimate the sampling error, which may cause potential problems in generalizing.

Nonprobability sampling limitations

The gap or difference between the nature of the population

Big circle = population small circle = with in population-sample Really big population, really small sample = big gap!

If people in population are similar to each other-possible to select any element that represents the population.

Homogeneity

If people are dissimilar - samples must increase in size to reduce the likelyhood of error

Heterogenous

The variables expected to influence a change in another variable

Independent variable

Those expected to change as a result of the actions of the independent variables

Dependent variables

All other variables that might somehow influence the relationship between the independent and Dependant variables, those extraneous to the relationship, are called

intervening variables

While another group receives imposed treatment, and is referred to as the

Control group

In many cases one or more of the groups receives some level of the independent variable, or some treatment, and is referred to as the

Treatment group

We have two groups, each receiving some form of treatment, whether it be lecture or discussion, and those groups are compared with one another, and are therefore known as

Comparison groups

Prior to the imposition of the independent variable, all the groups were equivalent with regard of the dependent variable. This assumption is referred to as

Group equivalence

Participants selected for an investigation are assigned to a treatment, control, or comparison group based on some randomizing technique. This randomizing technique can be used for the lottery, use of a set of random numbers, or a systematic sampling technique.

Random assignment

Established treatment, control, or comparison groups are evaluated on the dependent variable prior to the introduction of any treatment.

Pretesting

Participants in the treatment, control, or comparison groups are matched on characteristics thought to be important to the D pendant variable.

Matching

All participants in all groups are kept uniform with regard to significant characteristics thought to influence the dependent variable

Constancy matching

Each participant in the treatment, control, or comparison group is matched with participants in the other groups, based on characteristics thought to influence the dependent variable

Pairing

If the research setting has been created by the investigator, who maintains complete control over all that occurs in that setting, the study is known as a

Laboratory experiment

The other setting is the naturally occurring research setting, where the investigator has no opportunity to shape the setting to his or her preference

Field experiment

They examine only one independent variable at a time

Single-factor studies

Many times, however, researchers choose to examine the influence of two or more independent variables, or factors, as they simultaneously influence a dependent variable.

Factorial studies

3×2×3×2

36 cells

Would most likely be used by our instructor in the sample scenario. In this design, a separate group of participants, or subjects, would be used in each of the cells so that comparison could be made between cells.

Between-subject design

In some situations, the liability of the between-subject design can be alleviated by using the same subjects in each of the cells in the design diagram.

With in-subject design

In those situations in which that within-subject design is impractical, but the number of subjects required for a between-subject design proves unrealistic, the researcher may combine the two approaches into a mixed factorial design. In this design, the same subjects are used across the levels of one or more independent variables, while a difference that is used across the levels of the remaining independent variables

Mixed factorial design

Intervening variable

Bad, you control

2×2×3 =

12 2 -independent variable, 2 -independent variable, 3 -independent variable. 2 - levels, 2 - levels, 3 - levels.

Method that can even evaluate the casual relationship between the independent variable and the dependent variable while controlling for other intervening variables

Experiments

A simple relationship between independent variable and Dependant variable

Correlation

Goes one way

Casual relationship

Temporal ordering-causes independent proceeds in effect Dependant in time. Meaningful correlation-theory. No alternative causes-no other explanation for the causes.

Three requirements of causality

Alternative cause, other valuables that might influence or interfere the relationship between the independent and the Dependant variable

Intervening variables

Most of the independent variables in experiments are manipulated by researchers

Manipulation

Receive some level of treatment on independent variable

Treatment group

Receives no treatment on independent variable

Control group

Must ensure that groups are equivalent with regard to the Dependant variables before the treatment.

Group equivalence

How can we check group equivalence?

Random assignment-it usually works. Can use statistical testing to doublecheck. Pretesting-pretest (8), posttest (9), Treatment (post-pre-) versus control (post-pre)

Participants in groups are matched on characteristics that are important to dependent variables

Matching

Cannot manipulate independent variable

Comparison group

Each participant in a group is mashed with another participant in another group. Gender gets balanced

Paring

Two or more independent variables in the same argument

Factorial studies

Different participants in each cell. Comparison between the cells

Between subject design

Everyone goes through all, repeated measures

Within subject design

Combination of the between and with in design

Mixed factorial design

Surveys use self-report technique

Survey methods

Advantages of survey methods

Access to subjective info. Access to broadly distributed population (email)

Disadvantages of survey methods

Requires respondents to recall (people have to think). What participants report and actually do, could be different

Survey designs

``` Cross-sectional study Longitudinal study Trend study Panel study. CLTP ```

One time data collection. (Once and done) response from a single point in time. Example = teacher semester evaluation's.

Cross-sectional study

Asking some questions across a period of time

Longitudinal study

DIFFERENT samples from a population at different time points.

Trend study Circle = population, little circle with in population (May, June, April)

The SAME sample at different time points

Panel study Circle = population, little circle within population (May, April, June, July)

Panel study is highly vulnerable to several threats

Attrition (drop out) Test sensitization History Maturation (tired, changing mind)

Major sections of designing survey instrument

Introduction Instructions Questionnaires At the end...

Brief introduction of the study. (Researcher, purpose, etc.) participants rights-voluntary, right of withdrawal, ambiguous, confidentiality. Time required (10 to 15 minutes of your time)

Introduction

Complete and concise set of instructions how to select items-only one? Multiple? Rank order?

Instructions

Basic guideline = be clear, simple, understandable language, ninth grade level, be concise (simple to the point) lengthy, participants may skip them, be realistic

Questionnaires

Add filter questions if necessary (do you use Twitter?)

Questionnaires

Demographic information, write a thank you!

At the end

How many hours of TV did you watch last year? = Bad, avoid bias wording-''do you read newspapers or just watch TV?'' (Take out word JUST), avoid leading questions-don't you like our product? = Bad, avoid double barreled questions = asking a single question that ask for more than one response.

Things to avoid when designing surveys

Accurate findings about the phenomena under investigation for the particular groups of people studied.

Internal validity

Events which occurred during the study, influence participants behavior with in the study. Changes in the environment.

History

And initial measurement in a research study influences the subsequent measurement. Pretest affects posttest.

Test sensitization

Instruments wearout (out of date)

Instrument Decay

Subjects change their behavior because they know that they are being observed.

Hawthorne effect

Participants are being self-selected because people are self-selected, the study may not be good to represent the whole population. Could occur during the recruitment process.

Self-selection Bias

Natural changes that occur within participants over the course of the study. Tired, sick, bad day, sad, happy. Physiological/psychology.

Maturation

Dropping out from the study. Lost interest, forgot, do not care.

Attrition/mortality

Nonhuman elements. YouTube, blogs, websites.

Data

Participants influence each other. Do not speak about what is going on in the study until it's over.

Inter-participant bias

Problems with researchers methodology

Personal attribute effect, research bias. PA, RB

Problems with researchers methodology

Researchers characteristics influence participants behaviors. People may not be honest with you, your personality, outfit, ethnicity, gender.

Personal attribute effect

Accidentally informs the participants of what he/she expects. Do not say anything about your data.

Researcher bias

Type your pre-a validated questions and instructions.

Script

Hire a researcher (or assistant) who can conduct the study. The hired person doesn't need to know the hypothesis/research questions.

Double-blind study

Relationship between sample and population. Population = college students, sample = group of people that represent population.

External validity

The results of the study can be "generalizable" to population.

Good external validity

Representation

Generalizable

Representation

APA, Ch. 5, 7, 8, 9 Flashcards

(106 cards)