Psych 312 Final Exam
What are the three methods of communication among scientists?
The three methods of communication among scientists are to (1) publish a manuscript, (2) present a poster, and (3) give a talk.
What are the three types of factorial designs?
The three types of factorial designs are: Between-subjects design Within-subjects design Mixed factorial design
What is the process of null hypothesis significance testing (NHST)?
To use NHST, we begin by stating a null hypothesis, which is a statement about a population parameter, such as the population mean, that is assumed to be true but contradicts the research hypothesis. We then state a criterion upon which we will decide to retain or reject the null hypothesis.
Identify graphs used to display group means and correlations.
We can graph a mean for one or more groups using a graph with lines or bars to represent the means. By convention, we use a bar graph when the groups on the x-axis (horizontal axis) are represented on a nominal or ordinal scale; we use a line graph when the groups on the x-axis are represented on an interval or ratio scale. We graph correlations using a scatterplot. To plot a data point, you first move across the x-axis, and then move up or down the y-axis to mark or plot each pair of (x,y) data points. In a scatterplot, the pattern of a positive correlation appears as an ascending line; the pattern of a negative correlation appears as a descending line.
What is a between-subjects design?
(A design in which all factors are between-subjects factors.) Using this design, we manipulate the levels of both factors, cross the levels of each factor to create groups, and randomly assign different participants to each group.
What is a within-subjects design?
(A design in which all factors are within-subjects factors.) Using this design, we manipulate the levels of both factors, cross the levels of each factor to create groups, and control for order effects due to observing the same participants in each group.
What is a mixed factorial design?
(A design with at least one between-subjects factor and one within-subjects factor.) Using this design, we manipulate the levels of both factors, cross the levels of each factor to create groups, randomly assign different participants to each level of the between-subjects factor, and control for order effects due to observing the same participants at each level of the within-subjects factor.
What is a Type II error?
A Type II error is the probability of retaining a null hypothesis that is actually false, meaning that the researcher reports no effect in the population, when in truth there is an effect.
What is a chi-square?
A chi-square goodness-of-fit test is a statistical procedure used to determine whether observed frequencies at each level of one categorical variable are similar to or different from the frequencies we expected at each level of the categorical variable. If frequencies observed fit well with frequencies expected, the decision will be to retain the null hypothesis. If frequencies observed do not fit well with frequencies expected, the decision will be to reject the null hypothesis.
What is a level of significance?
A criterion of judgement upon which a decision is made regarding the value stated in a null hypothesis. The criterion is based on the probability of obtaining a statistic measured in a sample if the value stated in the null hypothesis were true. To establish a criterion for a decision, we state a level of significance for a test. The level of significance for most studies in behavioral science is .05.
How can a factorial design enhance the informativeness of interpretation of data?
A factorial design can enhance the informativeness of interpretation. The factorial design is more informative because it allows us to analyze the effects of two or more factors simultaneously, which leads to the analysis of an effect that is unique to the factorial design: the interaction. In this way, an analysis using a factorial design is more informative than research designs that analyze the effects of only one factor at a time.
What is a factorial design?
A factorial design is a research design in which participants are observed in groups created by combining the levels of two or more factors.
What is a factorial experimental design?
A factorial experimental design is a research design in which groups are created by manipulating the levels of two or more factors, then the same or different participants are observed in each group using experimental procedures of randomization (for a between-subjects factor) and using control for timing and order effects (for a within-subjects factor).
What are the different ways frequencies can be graphed?
A frequency distribution table can be presented graphically by listing the categories, scores, or intervals of scores on the x-axis (the horizontal axis) and the frequency in each category, for each score, or in each interval on the y-axis (the vertical axis) of a graph. The type of graph we use to describe frequency data depends on whether the factors being summarized are continuous or discrete. Continuous data are displayed in a histogram. Discrete and categorical data are displayed in a bar chart or pie chart. To summarize data as percents, a pie chart can be more effective display than a bar chart.
What is a frequency distribution table?
A frequency distribution table, which lists scores or categories in one colin and the corresponding frequencies in a second column, can be used to summarize: (1) the frequency of each individual score or category in a distribution or (2) the frequency of scores falling within defined ranges or intervals in a distribution.
What is a literature review article?
A literature review article is a written, comprehensive report of findings from previously published works about a problem in the form of a synthesis of previous articles or as a meta-analysis. To write the main body of a literature review: Identify the problem and how it will be evaluated. Integrate the literature to identify the state of research. Identify how findings and interpretations in the published literature are related or consistent, or are inconsistent, contradictory, or flawed. Consider the progress made in an area of research and potential next steps toward clarifying the problem.
What is a main effect?
A main effect is a source of variation associated with mean differences across the levels of a single factor. In a two-way factorial design, there are two possible main effects (one for each factor).
What is a test statistic?
A mathematical formula that allows researchers to determine the likelihood of obtaining sample outcomes if the null hypothesis were true. The value of the test statistic can be used to make a decision regarding the null hypothesis. The test statistic also allows researchers to determine the extent to which differences observed between groups can be attributed to the manipulation used to create the different groups. To determine the likelihood or probability of obtaining a sample outcome, if the value stated in the null hypothesis is true, we compute a test statistic. A test statistic is used to find the p value, which is the actual probability of obtaining a sample outcome if the null hypothesis is true.
What is a Type I error?
A type I error is the probability of rejecting a null hypothesis that is actually true. Researchers control for this type of error by stating a level of significance, which is typically set at .05.
What is an interaction?
An interaction is a source of variation associated with the variance of group means across the combination of levels of two factors. In a table summary, an interaction is a measure of how cell means at each level of one factor change across the levels of a second factor.
What is significance?
Describes a decision make concerning a value stated in the null hypothesis. When the null hypothesis is rejected, we reach significance. We reject the null hypothesis when p <_ .05, an effect reached significance. When the null hypothesis is retain, we fail to reach significance. We retain the null hypothesis when p > 0.5, an effect failed to reach significance.
How are descriptive statistics used to describe data?
Descriptive statistics are presented graphically, in tabular form (in tables), or as summary statistics (e.g., mean, median, mode, variance, and standard deviation).
What are descriptive statistics?
Descriptive statistics are procedures used to summarize, organize, and make sense of a set of scores or observations.
What is an effect size?
Effect size is a statistical measure of the size or magnitude of an observed effect in a population, which allows researchers to describe how far scores shifted in a population, or the percent of variance in a dependent variable that can be explained by the levels of a factor.
What are inferential statistics and why are they important?
Inferential statistics are procedures that allow researchers to infer or generalize observations made with samples to the larger population from which they were selected. Inferential statistics allow researchers to use data recorded in a sample to draw conclusions about the larger population of interest. This would not be possible without inferential statistics.
What are two reasons why it is important to summarize data?
It is important to summarize data in order to: (1) clarify what patterns were observed in a data set at a glance and (2) to be concise. It is more meaningful to present data in a way that makes the interpretation of the data clearer. Also, when publishing an article, many journals have limited space, which requires that the exposition of data be concise.
What is a nonparametric test?
Nonparametric tests are significance tests that are used to test hypotheses about data that can have any type of distribution and to analyze data on a nominal or ordinal scale of measurement.
What is a parametric test?
Parametric tests are significance tests that are used to test hypotheses about parameters in a population in which the data are normally distributed and measured on an interval or ratio scale of measurement.
What are the four writing and language guidelines for writing an APA-style manuscript?
The four writing and language guidelines for writing an APA-style manuscript are (1) to be accurate; (2) comprehensive, yet concise; (3) conservative; and (4) appropriate. To be comprehensive, yet concise, apply the following suggestions: abbreviate where appropriate, display data in a figure or table, keep the writing focused, and do not repeat information.
How are main effect and interaction identified in a table summary and a graph?
The group means in a table summary can be graphed to identify a main effect, an interaction, or both. A main effect is evident when changes in a dependent variable vary across the levels of a single factor. An interaction is evident when changes in a dependent variable across the levels of one factor depend on which level of the second factor you analyze. Note that even if a graph shows a possible main effect or interaction, only the test statistic can determine if a main effect or an interaction is significant.
Identify and appropriately use the median to describe data.
The median is the middle value in a distribution of data listed in numeric order. The mean is used to describe data that are skewed and data on an ordinal scale of measurement.
Identify and appropriately use the mode to describe data.
The mode is the value that occurs most often or at the highest frequency in a distribution. The mode is used to describe distributions with one or more modes and categorical data on a nominal scale of measurement.
What is a p value?
The probability of obtaining a sample outcome if the value stated in the null hypothesis is true. The p value is compared to the level of significance to make a decision about a null hypothesis.
Identify and appropriately use the mean to describe data.
The sample mean is the sum of all scores divided by the number of scores summed in a sample. The mean is used to describe data that are normally distributed and on an interval or ratio scale of measurement.
Identify and appropriately use the sample variance and standard deviation to describe data.
The sample variance is a measure of variability for the average squared distance that scores in a sample deviate from the sample mean. The sample variance is associated with n -1 degrees of freedom (df) and is computed by dividing the sum of squares (SS) by df. The larger the sample variance, the farther that scores deviate from the mean, on average. One limitation of sample variance is that the average distance of scores from the mean is squared. To find the deviation or distance of scores from the mean, we take the square root of the variance, called the standard deviation.
What are the three elements of communication?
The three elements of communication are (1) the speaker or author, (2) the audience, and (3) the message. The speaker or author uses first person and third person appropriately; uses past, present, and future tense appropriately, uses an impersonal writing style; reduces biased language; and gives credit where appropriate. The audience includes scientists and professionals, college students, and the general public. The message should be novel (contribute new findings or new ideas), interesting (to the readership of the work), and informative.