Inferential Statistics – Flashcards

Techniques that allow us to study samples and then make generalizations about the population. Inferential statistics are a very crucial part of scientific research in that these techniques are used to test hypotheses

Statistics for determining differences between experimental and control groups in experimental research

Statistics used in descriptive research when comparisons are made between different groups

These statistics enable the researcher to evaluate the effects of an independent variable on a dependent variable

Sample does not represent the population

State the hypothesis (H0) Select the probability level (alpha) Determine the value needed for significance Calculate the test statistic Accept or reject H0

statistical assessment of whether observations reflect a pattern rather than just chance

Techniques which require basic assumptions about the data, for example: normality of distribution homogeneity of variance requirement of interval or ratio data

requires interval or ratio level scores used to compare two mean scores easy to compute pretty good small sample statistic

compares mean scores on two independent samples

compares two mean scores from a repeated measures or matched pairs design most common situation is for comparison of pretest with posttest scores from the same sample

made when the researcher rejects the null hypothesis when in fact the null hypothesis is true probability of committing Type I error is equal to the significance (alpha) level set by the researcher thus, the smaller the alpha level the lower the chance of committing a Type I error

occurs when the researcher accepts the null hypothesis, when in fact it should have been rejected probability is equal to beta (B) which is influenced by several factors inversely related to alpha level increasing sample size will reduce B

the probability of rejecting a false null hypothesis Power = 1 - beta Decreasing probability of making a Type II error increases statistical power

Analysis of Variance; Extension of t-test -requires interval or ratio level scores used for comparing 2 or more mean scores maintains designated alpha level as compared to experimentwise inflation of alpha level with multiple t-tests may also test more than 1 independent variable as well as interaction effect

Extension of independent groups t-test, but may be used for evaluating differences among 2 or more groups

Extension of dependent groups t-test, where each subject is measured on 2 or more occasions a.k.a "within subjects design"

This is an extension of the matched pairs t-test when there are three or more groups or the same as the matched pairs t-test when there are two groups Participants similar in terms of a variable are placed together in a block and then randomly assigned to treatment groups

This is an extension of the one-way ANOVA for testing the effects of 2 or more independent variables as well as interaction effects Two-way ANOVA (e.g., 3 X 2 ANOVA) Three-way ANOVA (e.g., 3 X 3 X 2 ANOVA)

Interval or ratio level scores Random sampling of participants Scores are normally distributed N = 30 considered minimum by some Homogeneity of variance Groups are independent of each other Others

The various ANOVA tests are often referred to as "omnibus" tests because they are used to determine if the means are different but they do not specify the location of the difference if the null hypothesis is rejected, meaning that there is a difference among the mean scores, then the researcher needs to perform additional tests in order to determine which means (groups) are actually different

Duncan Tukey Bonferroni Scheffe

ANOVA design which statistically adjusts the difference among group means to allow for the fact that the groups differ on some other variable frequently used to adjust for inequality of groups at the start of a research study

Considered assumption free statistics Appropriate for nominal and ordinal data or in situations where very small sample sizes (n < 10) would probably not yield a normal distribution of scores Less statistical power than parametric statistics

A nonparametric test used with nominally scaled data which are common with survey research The statistic is used when the researcher is interested in the number of responses, objects, or people that fall in two or more categories

Used to test the hypothesis that the collected data (observed scores) fits an expected distribution

Used to test if there is a significant relationship (association) between two nominally scaled variables In this test we are comparing two or more patterns of frequencies to see if they are independent from each other

used in situations where each participant contributed one score to the data analysis, or in the case of a repeated measures design, one score per cell

used in situations where each participant contributes multiple scores

Canonical correlation Discriminant analysis Factor analysis

Analogous to ANOVA except that there are multiple dependent variables Represents a type of multivariate test

Predicting an unknown score (Y) based on a single predictor variable (X) Y' = bX + c

Involves more than one predictor variable Y' = b1X1 + b2X2 + c

Determines the relationship between one dependent variable and 2 or more predictor variables Used to predict performance on one variable from another Y' = b1X1 + b2X2 + c Standard error of prediction is an index of accuracy of the prediction

alpha = probability of a Type I error rejecting a true null hypothesis this is your significance level beta = probability of a Type II error failing to reject a false null hypothesis Statistical power = 1 - beta

Alpha level Sample size Effect size One-tailed or two-tailed test

Reducing the alpha level (moving from .05 to .01) will reduce the power of a statistical test. This makes it harder to reject the null hypothesis

In general, the larger the sample size the greater the power. This is because the standard error of the mean decreases as the sample size increases

It is easier to reject the null hypothesis using a one-tailed test than a two-tailed test because the critical region is larger

This is an indication of the size of the treatment effect, its meaningfulness With a large effect size, it will be easy to detect differences and statistical power will be high But, if the treatment effect is small, it will be difficult to detect differences and power will be low

ES=(M1-M2)/SD

0.2

0.5

0.8

focus on understanding and explaining meaning of a social phenomena

Subjective Non-numerical Nonstatistical analysis Small Ns Open ended data collection Narrative for results

-Takes place in the natural setting: travel to sites -Researcher is the primary method of data collection -Emergent rather than tightly prefigured -Based upon interpretation Hermeneutics: deciphering meaning -Views social phenomena holistically -Qualitative researchers reflect and are explicit regarding personal assumptions and values -Uses both deductive and inductive logic Inductive: going from specific to large Deductive: Going from broad to specific -Can use multiple methods

Life histories Grounded Theory Study Case Study Phenomenology Study Ethnography Study Basic/Generic

Story of a single individual or groups of single individuals

Discover or invent theory grounded in real-world experiences Middle-range theories: situation related

Exploration of a bounded system (e.g., school) In-depth data collection involving multiple sources of information

Describes the meaning of a lived experience for several individuals about a phenomenon Explores the structures of human consciousness

Interpretation of a cultural or social group Natural setting

Studies that illustrate characteristics of qualitative research

Researcher conceals role

role of researcher is known

observational role is secondary to participant role

researcher observes without participating

technique for analyzing qualitative data