Spss fisher's exact test 2x3

Chapter6/Chapter Guides.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Chapter 6 – Selecting and Interpreting Inferential Statistics Study Guide

OBJECTIVES: The student will be able to:

1. Identify the general design classification for difference research questions. 2. Explain the distinctions of within subjects design versus between groups design

classifications. 3. Utilize a decision tree (Figure 6.1) to guide the selection of appropriate inferential

statistics (Tables 6.1-6.4). a. Identify the research problem. b. Identify the variables and their level of measurement. c. Select appropriate inferential statistic.

4. Describe the relationship between difference and associational inferential statistics as a function of the general linear model.

5. Interpret the results of a statistical test. a. Determine whether to reject the null hypothesis. b. Determine the direction of the effect. c. Evaluate the size of the effect.

6. Discuss the relationship between statistical significance and practical significance. TERMINOLOGY: • variables • levels of measurement • descriptive statistics • inferential statistics

o difference inferential statistics o associational inferential statistics

• difference question designs • between group designs • within subjects design (repeated measures design) • single factor designs • between groups factorial designs • mixed factorial designs • basic (bivariate) statistics

o phi or Cramer’s V o eta o Pearson product moment correlation o Kendall’s tau or Spearman rho

• complex statistics o factorial ANOVA o multiple regression o discriminant analysis o logistic regression

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

o MANOVA o ANCOVA

• loglinear • general linear model • statistical significance

o critical value o calculated value o statistically significant o Sig.

• practical significance • effect size

o r family of effect size measures o d family of effect size measures

• confidence intervals ASSIGNMENTS: See additional activities and extra SPSS problems for assignment examples.

Chapter6/Chapter Outlines.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Chapter 6 – Selecting and Interpreting Statistics Chapter Outline

I. General Design Classifications for Difference Questions

A. Labeling difference question designs. 1. State overall type of design (e.g. between groups, within

subjects). 2. State the number of independent variables. 3. State the number of levels within each independent variable.

B. Between groups designs: each participant in the research is in only one condition or group.

C. Within subjects or repeated measures designs 1. Within subjects designs.

a. Each participant in the research receives or experiences all of the conditions or levels of the independent variable.

b. Also includes designs where participants are matched (e.g. parent & child; husband & wife).

2. Repeated measures designs: each participant is assessed more than once (e.g. pretest & posttest).

D. Single factor (one-way) design 1. Has only one independent variable. 2. Factor and way are other terms for group difference independent

variables. E. Between groups factorial design

1. When there is more than one group difference independent variable.

2. Each level of one factor (independent variable) is possible in combination with each level of the other factor(s).

a. The number of levels of each factor is used in the description of the design.

b. For example: a design that includes gender (2 levels) and ethnicity (4 levels) would be labeled as a 2 x 3 between groups factorial design.

F. Mixed factorial design: Has both a between groups independent variable and a within subjects independent variable.

G. Describing designs 1. Each independent variable is described using one number that

represents the number of levels for that variable. 2. Example: 3 x 4 between groups factorial design would have 2

independent variables, one with 3 levels and one with 4 levels. II. Selection of Inferential Statistics

A. Types of research questions. 1. Difference questions: compare groups and utilize difference

inferential statistics. (Tables 6.1 & 6.3) a. Basic (bivariate) statistics: one independent and one

dependent variable.

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

b. Complex statistics: three or more variables. 2. Associational questions: examine the association or relationship

between two or more variables and utilize associational inferential statistics (Tables 6.2 & 6.4).

B. Using Tables 6.1 and 6.4 to Select Inferential Statistics 1. Decide the number of variables.

a. 2 variables = Tables 6.1 or 6.2 b. 3 or more variables = Tables 6.3, 6.4 or 6.5

(Basic 2 variable Questions and Statistics) 2. If there are two variables and the independent variable is nominal

or has 2-4 levels = Table 6.1. a. Identify number of levels of IV. b. Identify type of research design (between or within). c. Determine the type of measurement for the DV.

3. If there are 2 variables and both are nominal use the bottom rows of Table 6.1 (difference question) or Table 6.2 (associational question).

4. If there are 2 variables and both variables have 5 or more ordered levels use Table 6.2 (associational question).

(Complex Questions and Statistics-3 or more variables) 5. If there is one normal/scale DV and the IV’s (2 or more) are

nominal or have a few ordered levels use Table 6.3. 6. If there is one normal/scale DV and the IV’s/predictors (2 or

more) are normal/scale or dichotomous use the top row of Table 6.4 (complex associational question).

7. If there is one DV that is nominal or dichotomous and there are 2 or more IV’s use the bottom row of Table 6.4 (or 6.3).

8. If there are 2 are more normal (scale) DV’s use the general linear model to do MANOVA.

III. The General Linear Model (GLM) A. Difference between associational and difference questions.

1. Mathematically, the distinction between associational and difference questions is artificial.

2. Both associational and difference inferential statistics serve the purpose of exploring and describing relationships (Fig. 6.2).

a. The GLM subsumes both associational and difference inferential statistics.

b. The relationship between the IV and DV can be expressed by an equation with weights for each of the independent/predictor variables plus an error term.

IV. Interpreting the Results of a Statistical Test A. Statistical Significance

1. The SPSS calculated value is compared to a critical value found in a statistics table.

2. Statistically significant: probability (p) is less than the preset alpha (usually .05).

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

a. Sig.: SPSS label for the p value. b. Usually, if the calculated value (t, F, etc.) is large, the

probability (p) is small. c. This Sig. is also the probability of committing a Type I

error (rejecting the null hypothesis when it is actually true). 3. The p and the null hypothesis

a. p > .05: don’t reject the null hypothesis; results are not statistically significant and could be due to chance.

b. p < .05: reject the null hypothesis; results are statistically significant and are not likely due to chance.

B. Practical Significance versus Statistical Significance 1. Statistical significance does not necessarily insure that the results

have practical significance or are important. 2. Effect size and/or confidence intervals must be examined to

determine the strength of association. a. It is possible, with a large sample, to have a statistically

significant result that is weak (small effect size). b. Small effect size may indicate that the difference or

association is of little practical importance. C. Confidence Intervals

1. An alternative to null hypothesis significance testing (NHST). 2. May provide more practical information than NHST. 3. Confidence intervals allow us to determine the interval that

contains population mean difference 95% of the time. D. Effect Size

1. The strength of the relationship between the independent variable and the dependent variable.

2. r family of effect size measures a. Pearson correlation coefficient (r): values range from –1.0

to +1.0 (0 = no effect and +1/-1 =maximum effect). b. Also includes other associational statistics such as rho, phi,

eta and the multiple correlation (R). c. Can be reported as a squared or unsquared value.

i. Squared values (r2) indicate the percent of variance of the DV that can be predicted from the IV, but give small numbers that give an underestimated impression of the strength or importance of the effect.

ii. Unsquared values (r) give a larger value and are recommended for r family indices.

3. d family of effect size measures a. Focuses on the magnitude of the difference rather than the

strength of the association. b. Computed by subtracting the mean of the second group

from the mean of the first group and dividing by the pooled standard deviation of both groups.

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

c. All d family effect sizes express effect sizes in standard deviation units.

d. Values usually vary from 0 to +/- 1.0, but can be > 1.0. 4. Issues about effect size measures.

a. d is not available on SPSS outputs but can be calculated from information provided on SPSS outputs.

b. r and R are available on SPSS outputs. c. Most journals now expect authors to discuss the effect size

as well as statistical significance. E. Interpreting Effect Sizes

1. Table 6.5 provides guidelines for the interpretation of effect sizes based upon the effect sizes usually found in the behavioral sciences and education.

2. The absolute meaning of large, medium, and small are relative to findings in these disciplines. Suggest using the following terms instead:

a. Minimal in place of small. b. Typical in place of medium. c. Substantial in place of large.

3. Cohen’s (1998) examples of effect size: a. Small = “difficult to detect”. b. Medium = “visible to the naked eye”. c. Large = “grossly perceptible”.

4. Effect size is not the same as practical significance. a. Effect size indicates the strength of the relationship and is

more relevant to practical significance than statistical significance.

b. However, effect size measures are not direct indexes of the importance of a finding.

V. An Example of How to Select and Interpret Inferential Statistics A. Steps in the process:

1. Identify the research problem. 2. Identify the variables and their level of measurement. 3. State the research question(s). 4. Identify the type of each research question. 5. Select an appropriate statistic. 6. Interpret the results of the statistic.

a. Determine if the results were statistically significant. b. If the results are statistically significant:

i. Determine the direction of the effect. ii. Calculate and interpret the effect size.

iii. If necessary, calculate and interpret confidence intervals to evaluate practical significance.

VI. Writing About Your Outputs A. Methods Chapter

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

1. Update methods to include descriptive statistics about the demographics of the participants.

2. Add literature based evidence about the reliability and validity of measures/instruments.

3. Discuss if statistical assumptions were violated or not. B. Results Chapter

1. Includes a description of the findings. 2. Include figures and tables to illustrate the findings. 3. Do not include a discussion of the findings in this section. 4. Results of statistics should include:

a. The value of the statistic (e.g. t = 2.05) b. The degrees of freedom (and N for chi-square) c. The p or Sig. Value (e.g. p = .048)

C. Discussion Chapter 1. Puts the findings in context to research literature, theory and the

purposes of the study. 2. Explain why the results turned out the way they did.

Chapter1/Chapter Guides.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Chapter 1 - Variables, Research Problems and Questions Study Guide

OBJECTIVES: The student will be able to:

1. Explain the difference between research problems, research hypotheses, and research questions.

2. Provide definitions for different types of variables. 3. Identify the research question, research hypothesis, and types of variables used in a study. 4. Determine if a research question is a difference research question, an associational

research question, or a descriptive research question. 5. Explain the relationship between the type of independent variable used in a study and the

type of research question that can be answered (difference, associational, descriptive). 6. Discuss how the type of research questions drives the selection of the type of statistic. 7. Utilize the SPSS data editor and variable view features to examine the variables of an

existing dataset. TERMINOLOGY:

• research problem • variable

o independent variable (active vs. attribute) o dependent variable o extraneous variable

• operational definition • randomized experimental study • quasi-experimental study • non-experimental study • factor • grouping variable • values (categories, levels, groups, samples) • variable label • value label • research hypotheses • research question

o difference research question o associational research question o descriptive research question o complex research question (multivariate)

ASSIGNMENTS: See additional activities for assignment examples.

Chapter1/Chapter Outlines.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Chapter 1 – Variables, Research Problems and Questions Chapter Outline

I. Research Problems: Statement about the relationships between two or more

variables. II. Variables

A. Definition: Characteristic of the participants or situation for a study 1. Must be able to vary or have different values. 2. Concepts that do not vary are called constants. 3. Operational definition: defines a variable in terms of the

operations or techniques used to measure it or make it happen. B. Independent Variables

1. Active (manipulated) independent variable: can be given to participants within a specified period of time during the study.

a. Are not necessarily manipulated by the experimenter. b. Treatment is always given after the study is planned. c. Randomized experimental & quasi-experimental studies

must have active independent variables. 2. Attribute (measured) independent variable: preexisting attributes

of the persons or their ongoing environment. a. Cannot be manipulated by the experimenter. b. Non-experimental studies have attribute independent

variables. 3. Other terms for independent variables:

a. factor b. grouping variable

4. Inferences about cause and effect: a. Designs with active independent variables (experimental,

quasi-experimental) can provide data to infer that the independent variable caused the change or difference in the dependent variable.

b. Designs with attribute independent variables (non- experimental) should not be used to conclude a cause and effect relationship between the independent variable and the dependent variable.

5. Values of the independent variable: a. Several options or values of a variable. b. Also called: categories, levels, groups, samples

C. Dependent Variables 1. Presumed outcome or criterion that is supposed to measure or

assess the effect of the independent variable. 2. Must have at least two values, but usually have many values that

vary from high to low.

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

D. Extraneous Variables 1. Not of interest in a particular study but could influence the

dependent variable. 2. May also be called nuisance variables or covariates.

III. Research Hypothesis and Questions A. Research hypothesis: predictive statements about the relationship between

variables. B. Research questions: similar to hypotheses, but do not make specific

predictions. 1. Difference research questions: compare two or more different

groups on the dependent variable a. Utilize difference inferential statistics (e.g. ANOVA or t-

test) 2. Associational research questions: find the strength of association

between variables or to make predictions about a variable from one or more variables.

a. Utilize associational inferential statistics (e.g. correlation, multiple regression)

3. Descriptive research questions: summarize or describe data without trying to generalize to a larger population of individuals.

4. Complex research questions: involve more than two variables at a time.

a. Utilize complex inferential statistics. b. May be called multivariate in some books.

IV. Sample Research Problem: The Modified High School and Beyond (HSB) Study A. Research Problem: What factors influence mathematics achievement?

1. Identify primary dependent variable 2. Identify independent and extraneous variables 3. Identify types of independent variables (active vs. attribute) 4. Identify the research approach (experimental, quasi-

experimental, non-experimental) B. SPSS Variable View

1. Columns give information on database variables a. Name shows the variable name b. Label gives a longer description of the variable c. Values shows assigned value labels d. Missing identifies if certain values are designated by user

for missing values C. SPSS Data Editor

1. Shows raw data a. Variables are across the top (identified by short variable

names) b. Participants are listed down the left side.

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

D. Research Questions for the Modified HSB Study 1. Descriptive questions (Chapter 4) 2. To examine continuous variables for normality (Chapter 4). 3. Determine relationships between two categorical variables with

crosstabulations (Chapter 8). 4. Associational questions (Chapter 9) 5. Complex associational questions (Chapter 9) 6. Basic difference questions (Chapter 10) 7. Complex difference questions (Chapter 11)

III. Research Hypothesis and Questions
IV. Sample Research Problem: The Modified High School and Beyond (HSB) Study
Chapter2/Chapter Guides.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Chapter 2 – Data Coding, Entry, and Checking Study Guide

OBJECTIVES: The student will be able to:

1. Describe the steps necessary to plan, pilot test and collect data. 2. Prepare data for entry into SPSS or a spreadsheet 3. Define and label variables. 4. Display your SPSS codebook (dictionary). 5. Enter data into SPSS or a spreadsheet. 6. Check accuracy of data entry using SPSS Descriptive Statistics.

TERMINOLOGY: • pilot study • content validity • coding • dummy coding • codebook • define variables • label variables • missing values • data entry form • descriptive statistics ASSIGNMENTS: See additional activities and extra SPSS problems for assignment examples.

Chapter2/Chapter Outlines.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Chapter 2 – Data Coding, Entry, and Checking Chapter Outline

I. Plan the Study, Pilot Test, and Collect Data

A. Plan the study 1. Identify the research problem, question and hypothesis. 2. Plan the research design.

B. Select or develop the instrument(s) 1. Select from available instruments 2. Modify available instruments 3. Develop your own instruments

C. Pilot test and refine the instruments 1. Try out instrument on friends or colleagues 2. Conduct pilot study with a similar sample population 3. Utilize experts to check content validity of instrument items

D. Collect the data 1. Use methods appropriate for selected instruments 2. Check raw data before entering 3. Set “rules” for dealing with problematic responses.

II. Code Data for Data Entry A. Rules for data coding (assigning numbers to values or levels of a variable)

1. All data should be numeric. 2. Each variable for each case or participant must occupy the same

column in the SPSS Data Editor. 3. All values (codes) for a variable must be mutually exclusive. 4. Each variable should be coded to obtain maximum information. 5. For each participant, there must be a code or value for each

variable. 6. Apply any coding rules consistently for all participants. 7. Use high numbers (value or code) for the “agree”, “good”, or

“positive” end of a variable that is ordered. B. Make a coding form: to streamline data entry processes

III. Problem 2.1: Check the Completed Questionnaires (follow instructions in book)

IV. Problem 2.2: Define and Label the Variables (follow instructions in book)

V. Problem 2.3: Display Your Dictionary or Codebook (follow instructions in book)

VI. Problem 2.4: Enter Data (follow instructions in book)

VII. Problem 2.5: Run Descriptives and Check the Data (follow instructions in book)

I. Plan the Study, Pilot Test, and Collect Data
II. Code Data for Data Entry
A. Rules for data coding (assigning numbers to values or levels of a variable)
B. Make a coding form: to streamline data entry processes
III. Problem 2.1: Check the Completed Questionnaires (follow instructions in book)
IV. Problem 2.2: Define and Label the Variables (follow instructions in book)
V. Problem 2.3: Display Your Dictionary or Codebook (follow instructions in book)
VI. Problem 2.4: Enter Data (follow instructions in book)
VII. Problem 2.5: Run Descriptives and Check the Data (follow instructions in book)
Chapter2/Extra SPSS Problems.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Chapter 2 – Data Coding, Entry, and Checking Using the college student data.sav file, from http://www.psypress.com/ibm-spss-intro- stats/ (“Data Sets (ZIPS)” button) or the Moodle Web site for this book, do the following problems. Print your outputs and circle the key parts for discussion. 1. Compute the N, minimum, maximum, and mean, for all the variables in the college

student data file. How many students have complete data? Identify any statistics on the output that are not meaningful. Explain.

There are 47 students who have complete data. This value is found by looking at the value given for the Valid N (listwise). The mean is not meaningful for nominal (unordered) variables. In this example, nominal variables include: gender of student, marital status, and age group. The mean for dichotomous variables coded as 0 and 1 can be meaningful because the means actually tell the percent of students that answered with a “1” on their survey. In this example, the following variables are dichotomous: does subject have children, television shows-sitcoms, television shows-movies, television shows- sports, television shows-news.

2. What is the mean height of the students? What about the average height of the same

sex parent? What percentage of students are males? What percentage have children?

Mean height of the students = 67.30 inches Average height of same sex parent = 66.78 inches Percentage of students that are male = 52.0% Percentage of students with children = 52.0%

Chapter3/Chapter Guides.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Chapter 3 – Measurement and Descriptive Statistics Study Guide

OBJECTIVES: The student will be able to:

1. Utilize frequency distributions to determine if data is normally distributed. 2. Define the various levels of measurement (nominal, ordinal, interval, ratio, etc.) and

recognize terms that are used interchangeably. 3. Distinguish between the types of measurement (e.g. nominal vs. ordered, ordinal vs.

normal). 4. Utilize SPSS to generate descriptive statistics (frequency distributions, measures of

central tendency, measures of variability) for a data set. 5. Select the appropriate descriptive statistics based upon the level of measurement of the

data. 6. Describe the difference between parametric and non-parametric statistics. 7. Describe the properties of the normal curve. 8. Determine whether data is normally distributed and describe types of non-normality

exhibited (skewness, kurtosis, etc.). 9. Explain the relationship between the area under the normal curve and probability

distributions. 10. Explain the purpose of converting data to a standard normal curve and generating z-

scores. TERMINOLOGY: • frequency distribution

o approximately normally distributed o not normally distributed o negatively skewed o positively skewed

• levels of measurement o nominal (categorical, qualitative, discrete) o dichotomous o ordinal (ranks) o interval o ratio o scale o approximately normal (continuous, dimensional, quantitative)

• descriptive statistics o frequency tables o bar charts o histograms o frequency polygons

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

o box and whiskers plot • measures of central tendency

o mean o median o mode

• measures of variability o range o minimum o maximum o standard deviation o skewness o kurtosis o interquartile range

• parametric vs. nonparametric statistics • power • normal curve

o area under the normal curve o standard normal curve o z scores

• kurtosis ASSIGNMENTS: See additional activities and extra SPSS problems for assignment examples.

Chapter3/Chapter Outlines.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Chapter 3 – Measurement and Descriptive Statistics Chapter Outline

I. Frequency Distributions

A. Definition: tally of the number of times each score on a single variable occurs.

B. Approximately normally distributed: there is a small number of scores for the low and high values and most of the scores occur in the middle values (distribution exhibits a “normal curve”).

C. Not normally distributed: distribution does not exhibit a normal curve. 1. Negatively skewed: tail of the curve (extreme scores) is

elongated on the low end (left side). 2. Positively skewed: tail of the curve (extreme scores) is elongated

on the high end (right side). II. Levels of Measurement

A. Measurement: the assignment of numbers or symbols to different characteristics (values) of the variables.

B. Nominal Variables: numerals assigned to each category stand for a name of category.

1. Categories have no implied order or value. 2. Categories are distinct and non-overlapping. 3. Other terms for nominal variables:

a. Categorical b. Qualitative c. Discrete

C. Dichotomous Variables: have only two levels or categories. 1. May or may not have an implied order 2. Other terms for dichotomous variables:

a. dummy variables b. discrete variables c. categorical variables

D. Ordinal Variables: mutually exclusive categories that are ordered from low to high, but the intervals between categories may not be equal.

1. Also includes ordered variables with only a few categories (2-4) 2. Distribution of the scores is not normally distributed. 3. Other terms for ordinal variables:

a. Ranks b. Categorical

E. Approximately Normal (or Scale) Variables: levels or scores are ordered from low to high and the frequencies of the scores are approximately normally distributed.

1. May be continuous (have an infinite number of possible values within a range).

2. If not continuous, should have at least five ordered values or levels.

3. Other terms for approximately normal variables:

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

a. interval – have ordered categories that are equally spaced b. ratio – have ordered categories that are equally spaced and

have a true zero c. continuous d. dimensional e. quantitative

F. How to Distinguish Between the Types of Measurement 1. Nominal versus ordinal variables:

a. Only two levels = treat as nominal in SPSS b. Three or more categories and not ordered = nominal c. Three or more categories and ordered = ordinal

2. Ordinal versus normal (scale) variables: a. Five or more ordered levels with equal intervals and

approximately normal distribution = normal b. Three or more ordered levels with unequal intervals and not

normally distributed = ordinal III. Descriptive Statistics

A. Frequency Tables: tabulates the number of occurrences of each level of a variable as well as the number of missing values; also calculates the valid percent and cumulative percent for each level.

1. Nominal data: order of categories in table is arbitrary; cumulative percent column is not useful

2. Ordinal or approximately normal data: order of categories in tables is shown from low to high; cumulative percent column is useful.

B. Bar Charts: creates discrete (not connected) columns to illustrate the frequency distribution; appropriate for nominal data.

C. Histograms: similar to a bar chart, but there are no spaces between the bars which indicates a continuous variable underlying the scores.

D. Frequency Polygons: connects points between the categories; best used with approximately normal data (but can be used with ordinal data).

E. Box and Whiskers Plot: useful for ordinal and normal data; gives a graphical representation of the distribution of scores.

1. Box: middle 50% of cases (those between the 25th and 75th percentiles)

2. Whiskers: represent the expected range of scores. 3. Outliers: scores that fall outside the box and whiskers.

F. Measures of Central Tendency 1. Mean: the arithmetic average; statistic of choice for normally

distributed data. 2. Median: the middle score; appropriate measure for ordinal data

or data that is skewed. 3. Mode: the most common category; can be used with any type of

data, but is the least precise information about central tendency. G. Measures of Variability: tells about the spread or dispersion of scores.

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

1. Range: highest score minus the lowest score; does not give an indication of spread of scores for ordered data.

2. Standard Deviation: most common measure of variability; based upon the deviation of each score from the mean of all scores; most appropriate for normally distributed data.

3. Interquartile Range: the distance between the 25th and 75th percentiles (as shown in the box plot); appropriate for ordinal data.

4. Nominal Data: variability measures are not appropriate; rather look at the number of categories and the frequency counts.

H. Conclusions About Measurement and the Use of Statistics 1. Normal data: utilize means and standard deviations for

parametric statistics. 2. Ordinal data: utilize median and nonparametric tests. 3. Nominal data: utilize mode or count.

IV. The Normal Curve A. Properties of the Normal Curve: the normal curve is theoretically formed

by counting an “infinite” number of occurrences of a variable. 1. Unimodal – the distribution has one hump which is in the middle

of the distribution. 2. The mean, median and mode are equal. 3. The curve is symmetric (not skewed). 4. The range is infinite (the extremes never touch the X axis). 5. The curve is not too peaked or too flat and is neither too short

nor too long (does not exhibit kurtosis). B. Non-Normally Shaped Distributions

1. Skewness: one tail of the frequency distribution is longer than the other.

2. Mean and median are different. C. Kurtosis

1. Refers to the shape of the curve. 2. Leptokurtic (positive kurtosis): frequency distribution is more

peaked than normal. 3. Platykurtic (negative kurtosis): frequency distribution is flatter

than normal. D. Area Under the Normal Curve (Figure 3.10)

1. The normal curve is a probability distribution whose area is equal to 1.0 and portions of the curve are fractions of 1.0.

2. Areas of the curves can be divided in terms of standard deviations.

a. 34% of area under the normal curve is between the mean and 1 standard deviation above or below the mean (thus, 68% of the area under the normal curve is within 1 standard deviation to the left and right of the mean).

b. 13.5% of the area under the normal curve is accounted for by adding a second standard deviation to the first (thus,

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

95% of the area under the normal is within 2 standard deviations to the left and right of the mean).

c. 5% of the area under the normal curve falls beyond 2 standard deviations to the left and right of the mean (thus, this is why values not falling within 2 standard deviations of the mean are seen as relatively rare events).

E. The Standard Normal Curve 1. A normal curve converted so the mean is equal to 0 and the

standard deviation is equal to 1. 2. This conversion allows comparison of normal curves with

different means and standard deviations. 3. z scores = units of the standard normal distribution

a. standard scores = term for raw scores that are converted to the standard normal curve.

Chapter3/Extra SPSS Problems.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Chapter 3 – Measurement and Description Statistics Use the hsbdata.sav file from http://www.psypress.com/ibm-spss-intro-stats/ (“Data Sets (ZIPS)” button) to do these problems with one or more of these variables: math achievement, mother’s education, ethnicity, and gender. Use Tables 3.2, 3.3, and the instructions in the text to produce the appropriate plots or descriptive statistics. Be sure that the plots and/or descriptive statistics make sense (i.e. that they are a “good choice” or “OK”) for the variable. 3.1 Create bar charts. Discuss why you did or didn’t create each.

• Select Analyze => Descriptive Statistics => Frequencies. • Move math achievement, mother’s education, ethnicity, and gender into the

Variables box. • Select Charts => Bar Charts => Continue => OK.

Bar charts can be used with any of the four levels of measurements, but it is better to use frequency polygons or histograms if you have normally distributed data. Each of these types of plots displays the frequency or number of subjects on the Y or vertical axis and shows the levels or values of the variables on the X axis of the plot. In histograms and frequency polygons the bars or points are connected implying that the levels of the variable are ordered from low to high. In a bar chart the bars are separated implying that there might not be an order to the levels or categories of the variable. 3.3 Create Frequency polygons. Discuss why you did or didn’t create each. Compare

the plots in 3.1, 3.2, and 3.3.

• Select Graphs => Line. Click Simple and Summaries for groups of cases • Click Define. • Move math achievement into the Category Axis box. => OK. • Repeat the steps above, except this time instead of moving math achievement, move

mother’s education in the Category Axis box. => OK. Frequency polygons and histograms are similar. They are designed for normally distributed data but are okay to use with ordinal variables. A frequency polygon connects the midpoints of the top of each bar in a histogram. In other words, you can make a frequency polygon from a histogram by taking a straight edge and connecting the middle of each of the bars. 3.5 Compute the mean, median, and mode. Discuss which measures of central

tendency are meaningful for each of the four variables.

• Select Analyze => Descriptive Statistics => Frequencies. • Move the four variables into the Variables box. • Statistics => Mean, Median, Mode => Continue => OK.

Although the mean, median, and mode are okay to use with ordinal or normal data, the mean is the most appropriate with normal data and the median is best with ordinal data.

http://www.psypress.com/ibm-spss-intro-stats/
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Neither the mean nor the median are not meaningful with nominal data. If you ask SPSS to compute a mean or median for ethnicity, it will do so, but because the ethnic categories are not in any order, the result would not be interpretable. The mode would tell you which ethnic group was the largest. Similarly, the mode (and median) tell you which level of a dichotomous variable is most frequent. The mean of a dichotomous variable (e.g., gender) is the percent of participants who have the higher value (i.e., female, in this case).

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Fig. E.3

Fig. E.4

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Ch.3 Output 2.0 Frequencies

Statistics

math achievement test mother's education ethnicity gender

N Valid 75 75 73 75

Missing 0 0 2 0

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Frequency Table

math achievement test

Frequency Percent Valid Percent

Cumulative

Percent

Valid -1.67 1 1.3 1.3 1.3

1.00 2 2.7 2.7 4.0

2.33 1 1.3 1.3 5.3

3.67 3 4.0 4.0 9.3

4.00 2 2.7 2.7 12.0

5.00 5 6.7 6.7 18.7

5.33 1 1.3 1.3 20.0

6.33 2 2.7 2.7 22.7

6.67 1 1.3 1.3 24.0

7.67 4 5.3 5.3 29.3

8.00 1 1.3 1.3 30.7

9.00 4 5.3 5.3 36.0

9.33 1 1.3 1.3 37.3

10.33 4 5.3 5.3 42.7

10.67 1 1.3 1.3 44.0

11.67 2 2.7 2.7 46.7

12.00 2 2.7 2.7 49.3

13.00 3 4.0 4.0 53.3

14.33 9 12.0 12.0 65.3

14.67 1 1.3 1.3 66.7

15.67 2 2.7 2.7 69.3

17.00 5 6.7 6.7 76.0

18.33 1 1.3 1.3 77.3

18.67 1 1.3 1.3 78.7

19.67 3 4.0 4.0 82.7

20.33 1 1.3 1.3 84.0

21.00 3 4.0 4.0 88.0

22.33 2 2.7 2.7 90.7

22.67 1 1.3 1.3 92.0

23.67 6 8.0 8.0 100.0

Total 75 100.0 100.0

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

mother's education

Frequency Percent Valid Percent

Cumulative

Percent

Valid < h.s. 17 22.7 22.7 22.7

h.s. grad 31 41.3 41.3 64.0

< 2 yrs voc 2 2.7 2.7 66.7

2 yrs voc 5 6.7 6.7 73.3

< 2 yrs coll 7 9.3 9.3 82.7

> 2 yrs coll 5 6.7 6.7 89.3

coll grad 3 4.0 4.0 93.3

master's 3 4.0 4.0 97.3

MD/PhD 2 2.7 2.7 100.0

Total 75 100.0 100.0

ethnicity

Frequency Percent Valid Percent

Cumulative

Percent

Valid Euro-Amer 41 54.7 56.2 56.2

African-Amer 15 20.0 20.5 76.7

Latino-Amer 10 13.3 13.7 90.4

Asian-Amer 7 9.3 9.6 100.0

Total 73 97.3 100.0 Missing multiethnic 1 1.3

blank 1 1.3 Total 2 2.7

Total 75 100.0

gender

Frequency Percent Valid Percent

Cumulative

Percent

Valid male 34 45.3 45.3 45.3

female 41 54.7 54.7 100.0

Total 75 100.0 100.0

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Bar Charts

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Fig. E.5

Fig. E.6

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Ch. 3 Output 3.3 GRAPH /LINE(SIMPLE)= COUNT BY mathach.

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

GRAPH /LINE(SIMPLE)= COUNT BY maed.

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Fig. E.7 Ch. 3 Output 1.1 FREQUENCIES VARIABLES=mathach maed ethnic gender /STATISTICS= MEAN MEDIAN MODE /ORDER= ANALYSIS Frequencies

Statistics

math

achievement

test

mother's

education ethnicity gender

N Valid 75 75 73 75

Missing 0 0 2 0

Mean 12.5645 4.11 1.77 .55

Median 13.0000 3.00 1.00 1.00

Mode 14.33 3 1 1

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Frequency Table

math achievement test

Frequency Percent Valid Percent

Cumulative

Percent

Valid -1.67 1 1.3 1.3 1.3

1.00 2 2.7 2.7 4.0

2.33 1 1.3 1.3 5.3

3.67 3 4.0 4.0 9.3

4.00 2 2.7 2.7 12.0

5.00 5 6.7 6.7 18.7

5.33 1 1.3 1.3 20.0

6.33 2 2.7 2.7 22.7

6.67 1 1.3 1.3 24.0

7.67 4 5.3 5.3 29.3

8.00 1 1.3 1.3 30.7

9.00 4 5.3 5.3 36.0

9.33 1 1.3 1.3 37.3

10.33 4 5.3 5.3 42.7

10.67 1 1.3 1.3 44.0

11.67 2 2.7 2.7 46.7

12.00 2 2.7 2.7 49.3

13.00 3 4.0 4.0 53.3

14.33 9 12.0 12.0 65.3

14.67 1 1.3 1.3 66.7

15.67 2 2.7 2.7 69.3

17.00 5 6.7 6.7 76.0

18.33 1 1.3 1.3 77.3

18.67 1 1.3 1.3 78.7

19.67 3 4.0 4.0 82.7

20.33 1 1.3 1.3 84.0

21.00 3 4.0 4.0 88.0

22.33 2 2.7 2.7 90.7

22.67 1 1.3 1.3 92.0

23.67 6 8.0 8.0 100.0

Total 75 100.0 100.0

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

mother's education

Frequency Percent Valid Percent

Cumulative

Percent

Valid < h.s. 17 22.7 22.7 22.7

h.s. grad 31 41.3 41.3 64.0

< 2 yrs voc 2 2.7 2.7 66.7

2 yrs voc 5 6.7 6.7 73.3

< 2 yrs coll 7 9.3 9.3 82.7

> 2 yrs coll 5 6.7 6.7 89.3

coll grad 3 4.0 4.0 93.3

master's 3 4.0 4.0 97.3

MD/PhD 2 2.7 2.7 100.0

Total 75 100.0 100.0

ethnicity

Frequency Percent Valid Percent

Cumulative

Percent

Valid Euro-Amer 41 54.7 56.2 56.2

African-Amer 15 20.0 20.5 76.7

Latino-Amer 10 13.3 13.7 90.4

Asian-Amer 7 9.3 9.6 100.0

Total 73 97.3 100.0 Missing multiethnic 1 1.3

blank 1 1.3 Total 2 2.7

Total 75 100.0

gender

Frequency Percent Valid Percent

Cumulative

Percent

Valid male 34 45.3 45.3 45.3

female 41 54.7 54.7 100.0

Total 75 100.0 100.0

Chapter4/Chapter Guides.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Chapter 4 – Understanding Your Data and Checking Assumptions Study Guide

OBJECTIVES: The student will be able to:

1. Describe the purpose of Exploratory Data Analysis (EDA). 2. Explain the purpose of statistical assumptions. 3. Select appropriate types of analyses and plots to conduct EDA based upon the level of

measurement of the variable. 4. Utilize SPSS to conduct EDA. 5. Interpret SPSS output from EDA.

TERMINOLOGY: • exploratory data analysis (EDA) • statistical assumptions

o homogeneity of variances o normality

• parametric tests • robust • non-parametric tests • skewness

o positive skew o negative skew

ASSIGNMENTS: See additional activities and extra SPSS problems for assignment examples.

Chapter4/Chapter Outlines.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Chapter 4 – Understanding Your Data and Checking Assumptions Chapter Outline

I. Exploratory Data Analysis (EDA)

A. What is EDA? 1. The first step to complete after entering data and before running

any inferential statistics. 2. Computing various descriptive statistics and graphs in order to

examine your data. a. Look for data errors, outliers, non-normal distributions, etc. b. Determine if the data meets the assumptions of the statistics

you plan to use. c. Gather basic demographic information about the subjects. d. Examine relationships between the variables to determine

how to conduct the hypothesis testing. B. How to do EDA

1. Generate plots of the data 2. Generate numbers from the data.

C. Check for Errors 1. Examine raw data before entering. 2. Compare some raw data against entered data. 3. Compare maximum and minimum values against the allowable

ranges. 4. Examine the means and standard deviations to see if they seem

reasonable. 5. Look to see if there is an unreasonable amount of missing data. 6. Look for outliers.

D. Statistical Assumptions: explain when it is and isn’t reasonable to perform a specific statistical test.

1. Parametric tests a. Usually have more assumptions than nonparametric tests. b. Generally designed for use with data that exhibits

approximately normal distribution c. S.some parametric tests are more robust in dealing with

violations of assumptions than others. 2. Nonparametric tests

a. Have fewer assumptions b. Can often be used when assumptions for parametric tests

are violated. E. Parametric Tests

a.

Chapter4/Extra SPSS Problems.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Chapter 4 – Understanding Your Data and Checking Assumptions Using the College Student data file, do the following problems. Print your outputs and circle the key parts of the output that you discuss. 4.1 For the variables with five or more ordered levels, compute the skewness.

Describe the results. Which variables in the data set are approximately normally distributed/scale? Which ones are ordered but not normal?

• Select Analyze => Descriptive Statistics => Descriptives. • Move student height, same sex parent’s height, amount of tv watched per week,

hours of study per week, student’s current gpa, positive evaluation-institution, positive evaluation-major, positive evaluation-facilites, positive evaluation-social life, hours per week spent working in the Variables box.

• Options => Check Skewness (in addition to Mean, Std. Deviation, Minimum, and Maximum) => Continue => OK.

The Valid N (listwise) for the variables selected is 48. The Means all seem reasonable and within the expected range. The Minimum and Maximum values are all with the expected range, based on the codebook. The N for each variable makes sense and only two variables are missing values (positive evaluation-major and hours per week spent working).

The Skewness Statistic is utilized to determine which of these variables are approximately normally distributed. The guideline is that if the Skewness Statistic is between -1 and 1, the variable is at least approximately normal. In this case, all the variables with five or more ordered levels fall into that range and would be considered approximately normally distributed. For this dataset, the ordinal variables with five or more ordered levels (positive evaluation-institution, positive evaluation-major, positive evaluation-facilities, positive evaluation-social life) are all approximately normally distributed and we can assume they are more like scale variables and we can use inferential statistics that have the assumption of normality with them. None of the variables examined for this problem were not normal. 4.3 Which variables are nominal? Run frequencies for the nominal variables and

other variables with fewer than five levels. Comment on the results.

• Select Analyze => Descriptive Statistics => Frequencies. • Move gender of student, marital status, age group, does subject have children,

television shows-sitcoms, television shows-movies, television shows-sports, television shows-news shows

The table titled Statistics provides the number of participants for whom we have Valid data and the number of Missing data. No other statistics were requested because almost all of them are not appropriate to use with nominal and dichotomous data. Age group has three ordered levels so it is ordinal and the median would be appropriate.

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

The other tables are labeled Frequency Table and there is one for each of the variables selected. The left-hand column shows the Valid categories (or levels or values), Missing values, and Total number of participants. The Frequency column gives the number of participants who had each value. The Percent column is the percent who had each value, including missing values. For example, in the marital status table, 40.0% of ALL participants were single, 36.0% were married, 22.0% were divorced, and 2.0% were missing. The Valid Percent shows the percent of those with nonmissing data at each value; e.g. 40.8% of the 49 students with valid data were single. Finally, Cumulative Percent is the percent of the subjects in a category plus the categories listed above it.

Fig. E.8

Fig. E.9

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Ch. 4 Output 4.1 DESCRIPTIVES VARIABLES=height pheight hrstv hrsstudy currgpa evalinst evalprog evalphys evalsocl hrswork /STATISTICS=MEAN STDDEV MIN MAX SKEWNESS.

Descriptives

Fig. E.10

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation Skewness Statistic Statistic Statistic Statistic Statistic Statistic Std. Error

student height in inches 50 60.00 75.00 67.3000 3.93959 .163 .337

same sex parent's height 50 58.00 76.00 66.7800 5.10418 .333 .337

amount of tv watched per

week

50 4 25 11.98 6.096 .645 .337

hours of study per week 50 2 38 15.62 8.310 .950 .337

student's current gpa 50 2.4 4.0 3.172 .3907 .147 .337

positive evaluation,

institution

50 2 5 3.38 .945 .059 .337

positive evaluation, major 49 1 5 3.27 .953 -.115 .340

positive evaluation, facilities 50 1 5 2.76 1.061 -.136 .337

positive eval, social life 50 1 5 3.10 1.182 .031 .337

hours per week spent

working

49 0 50 26.12 14.857 -.516 .340

Valid N (listwise) 48

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Ch. 4 Output 4.3 FREQUENCIES VARIABLES=gender marital age children tvsitcom tvmovies tvsports tvnews /ORDER=ANALYSIS.

Frequencies

Frequency Table

gender of student

Frequency Percent Valid Percent

Cumulative

Percent

Valid males 26 52.0 52.0 52.0

females 24 48.0 48.0 100.0

Total 50 100.0 100.0

marital status

Frequency Percent Valid Percent

Cumulative

Percent

Valid single 20 40.0 40.8 40.8

married 18 36.0 36.7 77.6

divorced 11 22.0 22.4 100.0

Total 49 98.0 100.0 Missing System 1 2.0 Total 50 100.0

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

age group

Frequency Percent Valid Percent

Cumulative

Percent

Valid less than 22 17 34.0 34.0 34.0

22-29 18 36.0 36.0 70.0

30 or more 15 30.0 30.0 100.0

Total 50 100.0 100.0

does subject have children

Frequency Percent Valid Percent

Cumulative

Percent

Valid no 24 48.0 48.0 48.0

yes 26 52.0 52.0 100.0

Total 50 100.0 100.0

television shows-sitcoms

Frequency Percent Valid Percent

Cumulative

Percent

Valid no 18 36.0 36.0 36.0

yes 32 64.0 64.0 100.0

Total 50 100.0 100.0

television shows-movies

Frequency Percent Valid Percent

Cumulative

Percent

Valid no 32 64.0 64.0 64.0

yes 18 36.0 36.0 100.0

Total 50 100.0 100.0

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

television shows-sports

Frequency Percent Valid Percent

Cumulative

Percent

Valid no 24 48.0 48.0 48.0

yes 26 52.0 52.0 100.0

Total 50 100.0 100.0

television shows-news shows

Frequency Percent Valid Percent

Cumulative

Percent

Valid no 27 54.0 54.0 54.0

yes 23 46.0 46.0 100.0

Total 50 100.0 100.0

Chapter5/Chapter Guides.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Chapter 5 – Data File Management Study Guide

OBJECTIVES: The student will be able to:

1. Explain why data transformations might be necessary. 2. Count data. 3. Recode and relabel data. 4. Compute scale scores using either the numeric expression or function features of the

SPSS Compute Variable command. 5. Check transformed data for errors and normality.

TERMINOLOGY: • data transformation • file management • summated variable (composite variable, scale score) • count • recode • reverse code • relabel • compute ASSIGNMENTS: See additional activities and extra SPSS problems for assignment examples.

Chapter5/Chapter Outlines.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Chapter 5 – Data File Management Chapter Outline

I. Problem 5.1: Count Math Courses Taken

A. Follow the directions in the book to use the Count command to determine how many math courses the participants took.

II. Problem 5.2: Recode and Relabel Mother’s and Father’s Education A. Recode is useful to either reduce the number of levels of a variable or to

combine two or small groups or categories of a variable. B. Follow the directions in the book to use the Recode command to change

the levels of a variable. III. Problem 5.3: Recode and Compute Pleasure Scale Score

A. A scale score can be computed by taking the average of several variables. B. Follow the directions in the book to compute a scale score from several

items. IV. Problem 5.4: Compute Parents Revised Education with the Means Command

A. Follow the directions in the book to compute a new variable. V. Problem 5.5: Check for Errors and Normality for the New Variables

A. Follow the directions in the book to utilize the Descriptives command to check the new variables.

VI. Saving the Updated HSB Data File A. Follow the directions in the book to save the recodes.

Chapter5/Extra SPSS Problems.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Chapter 5 – Data File Management Using the college student data, solve the following problems: 5.1. Compute a new variable labeled average overall evaluation (aveEval) by

computing the average score (evalinst + evalprog + evalphys + evalsocl)/4.

• Select Transform =>Compute type aveEval • Type or click the formula shown above into the Numeric Expressions box. =>

OK.

You should check the new variable to make sure you typed the formula correctly. You can visually compute a few by examining these four variables in the Data View, and/or running Descriptives on the new variable, aveEval, to check if the results seem reasonable. Valid N (listwise) = 49; Minimum = 1.75; Maximum = 4.25; Mean = 3.1224. 5.3 Count the number of types of TV shows that each student watches.

• Select Transform => Count • Move tv sitcom, tvmovies, tvsports, and tvnews into the Numeric Variables box. • Name the Target Variable TVShows and label it Number of types of TV shows

watched. • Click Define Values => type “1” => Add => Continue => OK.

Each of the four types of TV shows are coded 1 for yes , watch them, or 0 for nom don’t watch, so the above commands count the number of different types of shows watched, from 0 (none of them) to 4 (all four). The COUNT can be evaluated visually by inspecting the Data View and/or by funning rfrequencies on the new variable. The mean number of types of TV shows watched is 1.98 and the mode is 2.00.14 students watch 1 type of TV show; 23 students watch 2 types of TV shows; 13 students watch 3 types of TV shows.

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Fig. E. 11 Ch. 5 Output 5.1 COMPUTE aveEval= (evalinst+evalprog+evalphys+evalsocl)/4 . EXECUTE .\

IBM SPSS for Introductory Statistics: Use and Interpretation, 5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's Manual by Gene W. Gloeckner and Don Quick

Fig. E.12

Fig. E. 13 Ch. 5 Output 5.3 COUNT TVShows = tvsitcom tvmovies tvsports tvnews (1). VARIABLE LABELS TVShows ‘Number of types of TV shows watched ‘. EXECUTE.

Appendix A.doc
Appendix A
Data Collection
This Appendix and Appendix B are designed to provide introductory background information on developing a questionnaire, collecting data with it, and getting the data ready to enter and analyze. This chapter also explains the importance of understanding your data, how to develop a questionnaire, and how to set up data coding sheets.