Question 1

Dummy variables are used to respecify categorical variables.

Accepted Answer

Dummy variables are used to represent different categories of a categorical variable in a quantitative way, often in regression or other statistical analyses. They allow us to include categorical variables in a model as predictor variables, by creating one or more binary variables (0 or 1) that indicate the presence or absence of a particular category.

Question 2

SPSS Data Entry allows you to verify that respondents have answered completely by setting rules. These rules can be used on existing datasets to validate and check the data only of questionnaires that were constructed in Data Entry.

Accepted Answer

The answer of SPSS Data Entry allows you to verify...

Question 3

When using univariate techniques, the number of samples is is determinded based on how the data are treated for the purpose of analysis, not on how the data were collected.

Accepted Answer

Univariate techniques focus on analyzing a single variable, and the sample size can be determined based on the statistical requirements of the analysis rather than the method of data collection.

Question 4

Data cleaning includes consistency checks and treatment of missing responses. The checks at this stage are less extensive than the checks made during editing.

Accepted Answer

The answer of Data cleaning includes consistency checks and treatment...

Question 5

The major technique for examining variable interdependence is factor analysis.

Accepted Answer

Factor analysis is a statistical technique used for examining variable interdependence and determining underlying factors or dimensions that explain the common variance among variables.

Question 6

The effect of weighting is to increase or to decrease the number of cases in the sample that possess certain characteristics.

Accepted Answer

Weighting adjusts the sample to make it more representative of the population by either increasing or decreasing the number of cases in the sample that possess certain characteristics.

Question 7

Treatment of missing responses poses problems, particularly if the proportion of missing responses is more than 10 percent.

Accepted Answer

When a significant proportion of responses are missing, it can affect the validity and reliability of the results, and various methods of imputation or statistical analysis must be used to handle missing data.

Question 8

Missing value codes should be distinct from the codes assigned to the legitimate responses.

Accepted Answer

Missing value codes should not be confused with legitimate response codes, so they should be distinct from them. This allows for easier identification and handling of missing values in data analysis.

Question 9

The data- preparation process begins after the fieldwork is done.

Accepted Answer

Data preparation is a preliminary step in the research process, involving the cleaning and organizing of data before analysis, and can begin as soon as initial data is collected, not necessarily after all fieldwork is done.

Question 10

Unsatisfactory respondents may differ from satisfactory respondents in systematic ways, and the decision to designate a respondent as unsatisfactory may be subjective. Both of these factors bias the results.

Accepted Answer

This is because both factors mentioned (systematic differences and subjective decision-making) can lead to a biased sample, which in turn can bias the results obtained from that sample.

Question 11

When respecifying a categorical variable with K categories, only K - 1 dummy variables are needed because only K - 1 categories are independent.

Accepted Answer

This is known as the "dummy variable trap" - if we include a dummy variable for every category, the model will be over-parameterized and the variables will not be linearly independent, leading to issues with interpretability and hypothesis testing. Therefore, we drop one category and use K-1 dummy variables to represent the remaining K-1 categories.

Question 12

The purpose of respecification is to create variables that are consistent with the objectives of the study.

Accepted Answer

Respecification involves creating variables that align with the study objectives, therefore the statement is true.

Question 13

Computer packages like SPSS, SAS, EXCEL, and MINITAB can be programmed to identify
out- of- range values for each variable and print related code information to make it easy to check each variable systematically for out- of- range values.

Accepted Answer

Computer packages like SPSS, SAS, EXCEL, and MINITAB can be programmed to identify out-of-range values and print related code information for easy checking.

Question 14

The use of dummy variables refers to a respecification procedure using variables that take on only two values, usually 0 or 1.

Accepted Answer

The statement is true, as dummy variables are constructed in such a way that they represent the presence or absence of a certain characteristic or attribute. They are often used in regression analysis to model categorical variables such as gender, race, or educational attainment.

Question 15

All extreme values result from errors and point to problems with the data.

Accepted Answer

Extreme values can result from legitimate data, such as outliers or rare occurrences. While extreme values can also occur due to errors, they do not always necessarily indicate problems with the data.

Question 16

When setting up the code book for a questionnaire, you would allow 2 columns to code the response to the question
"Do you have a valid passport? 1. yes 2. no"

Accepted Answer

This question has a binary response (yes or no), so only one column is needed to code the response, with 1 representing "yes" and 2 representing "no".

Question 17

If possible, standard codes should be used for missing data.

Accepted Answer

Standard codes for missing data help in maintaining consistency and accuracy while analyzing data.

Question 18

At the editing stage, the researcher makes a preliminary check for consistency.

Accepted Answer

At the editing stage, one of the tasks is to ensure consistency in the data and information presented.

Question 19

The data- preparation process is guided by the preliminary plan of data that was formulated in the research design phase.

Accepted Answer

The preliminary plan of data guides the data-preparation process in order to ensure that the data collected is appropriate for the research questions being addressed. This is necessary to avoid problems in the analysis phase.

Question 20

Standardized z_iscores can be obtained as: Z_i= (X - X)/s.

Accepted Answer

The formula for standardized z-scores is Z = (X - μ) / σ, where X is the raw score, μ is the mean, and σ is the standard deviation. The given formula, Z = (X - X) / s, is incorrect because it uses the sample mean (X) instead of the population mean (μ) and the sample standard deviation (s) instead of the population standard deviation (σ).

Quiz 14: Data Preparation