Test #3

Conceptual Replication

A replication study in which researchers examine the same research question (the same conceptual variables) but use different procedures for operationalizing the variables

Quota Sampling

Identify sub groups of interest, establish how many of each sub group desired (quota), then collect that amount for each -ensures that subgroups of interest are included

What happens without sub groups?

No data in important sub groups

Practical Significance

The usefulness or everyday impact of the results

Non-Probability Sampling

-Convenience Sampling -Quota Sampling

Probability Theory

-Involves a study of phenomena characterized by uncertainty -Used to determine how likely outcome are to occur given what we know and don't know (how likely to win the lottery) -determine if studys sample population represents population: 1. Use good sampling techniques 2. Check samples distribution of scores to see if they are normally distributed -we can never be certain - research never 'proves' -> it supports

Probability Sampling

-Simple Random -Stratified Random

Hypothesis Test

-determines the likelihood that the results found (with this sample) were due to simple chance or error -If the likelihood is very low, then the results are considered "real" in that there is an actual effect, relationship, or difference in the population

Null Hypothesis (H0)

-hypothesis in which there is no effect -Any effect, change, or relationship found is just a chance of error

Alternate Hypothesis (Ha)

-typically our hypothesis that says there is an effect, relationship, or difference -Any effect, relationship, or change is real (not chance or error)

Factors that increase power are:

1)Larger Sample Size 2)Less error in research design 3)Larger effect size

What are the 6 steps of hypothesis testing?

1. Define Null and Alternative Hypothesis 2. Choose alpha level (almost always a=0.05) 3. Determine Critical Value(s) for rejecting null 4. Calculate Test Statistic 5. Compare Test Statistic with Critical Values 6. Accept or Reject Null, Interpret results

Calculating a Independent Samples T-Test in formulas:

1. Find SD 2. Find SD²pooled 3. Find SDx-x 4. t = .......

Finding Pearson's r in formulas:

1. Find the Means of each variable 2. Find the SDs of each variable 3. Find the Z-score for all scores 4.Mutiply each pair of X & Y scores for each participant 5. Add up all mutiplied pairs 6. Divide by the number of pairs of scores (N-1) = your r value ***Plotting data can give a good indication of scores

Representative Sample

A sample that closely resembles the population in important ways

Biased Sample

A sample that does not resemble the population in important ways

Sample

A subset of the population that is meant to represent the population (individuals actually involved in our study)

Power

Ability to detect an effect when it does exist in the population

Exact Replication

An attempt to replicate precisely the procedures of a study to see whether the same results are obtained

Perfect Representative Sample

An ideal to strive for that is generally never obtained

What can we do as skeptical scientists?

As Skeptical Scientists, we always start by assuming that our hypothesis is not correct

Convenience Sampling

Choosing members of a population that are convenient (i.e. easy, cheap, quick) to obtain

Probability Distribution

Curve of scores where the percent is the probability of obtaining a score within the range

Type 2 Error

Evidence for a statistically significant result is not found in our sample but there really is an effect on the population. In other words we accept the null when it is false. **increase power of study to decrease type 2 error

Type 1 Error

Evidence for a statistically significant result is found in our sample but there really is no effect on the population. In other words when the null is true but we reject it. ** more stringent p-value p < .001 decreases type 1 error

Non Response Bias

Extent to which those who were selected and did participate differ from those who were selected and did not participate

One Sample T-Test or One Sample Z-Test

Inferential Statistic that compares a sample mean to a known population mean to determine if they are different

Null until suggests Alternative equals ...........

Innocent until proven guilty

When something falls in the region of acceptance.....

It is NOT statically significant, reject the alternative and accept the null.

When something falls in the region of rejection.....

It is statically significant, reject the null and accept the alternative.

Statistical Significance

It is unlikely that the results were due to chance or error such that we instead consider there to be a real effect, relationship, or difference in the population. *****Statistical significance does not necessarily imply that the results are important or practically meaningful

Stratified Random Sampling

Random sampling of each groups and combining this into a bigger sample

Descriptive v.s. Inferential Terms

Sample. Population Statistic. Parameter Mean(M). Mu (μ) Standard Deviation(SD). Sigma (σ)

Pearson's r (correlation coefficient)

Statistic used to describe a linear relationship between variables

Independent Samples T-Test

Statistic used to test whether there is a statistically significant difference between 2 independent groups on their mean value of a variable

Effect Size

Strength/Size of the effect, relationship, or difference

Convenience Sampling Strengths and Weaknesses

Strengths: Convenient Weakness: (Most) Likely a biased sample

Quota Sampling Strengths and Weaknesses

Strengths: Easy, cheap, quick, and may be more representative than convenience sampling Weakness: Most likely a bias sample (to a degree)

Sampling Error

The difference(s) between a sample and it's corresponding population (Generally the larger the sample, the more accurately it represents the population)

Population

The entire group we are interested in understanding

Sampling

The process of selecting individuals to participate in a study

What does p > .05 mean

This is the "alpha level". It is the level of statistical significance. The probability of getting the results by chance is less than 5 in 100.

What are the two types of errors in hypothesis testing?

Type I & Type 2

When do you use a one sample T-TEST?

Used when N < 30 and σ is UNKNOWN

When do you use a one sample Z-TEST?

Used when N > 30 and σ is KNOWN

Critical Value (crit) (Zcrit)

Value of a statistic that defines the region for rejecting the null ***In psych, is often plus or minus 1.96 SD in a normal distribution for a z-tail test

Non-Probability Sampling

We don't know the probability of each member of the population being chosen -(mostly) biased sample

Probability Sampling

We know the probability of each member of the population being chosen -Representative sample

Simple Random Sampling

each member of the population has an equal chance of being selected Strengths: Selection is fair and unbiased Weakness: Can be difficult, expensive, or just plain impossible to do

What is the alpha level (α)?

maximum probability/risk allowed for concluding that an effect, relationship, or difference exists when in fact, it does not

What is often the critical value for a normal distribution z-tail test?

±1.96 SD

When conducting a one sample z-test the crit values are always:

±1.96 SD

In psych what is the alpha level most commonly?

α=.05