SPSS Transform Recode into Different Variable in MS Excel to Reverse Response Scales

While SPSS allows one to perform some interesting transforms on variables from within the software package, it is often much cleaner to perform data transformations and variable computations from with MS Excel before data Import into SPSS.

While using MS Excel, suppose your response data for a 1 to 10 “Likert scale” is in column B and you want to recode it into Column C:



Use the Choose Function in MS Excel to reverse scale as follows:
1. Enter “=CHOOSE(B1, 10,9,8,7,6,5,4,3,2,1)” without the quote marks into Column C
2. Copy the function in Step 1 down the column for the remainder of Column C.
3. Use Paste Special Values to copy the values in Column C over the function.
Note: The Choose Function uses an “index” scheme to decide how to replace the values. For example, the first index location #1 appears in the function argument list immediately after the cell B1, so a response value of 1 becomes 10, and so forth.

Mastering Basic Statistics

Trust me. You can do statistical analysis. The basics of statistics can be mastered... Forget about those mind-numbing textbooks for a second. Descriptive Statistics are all about what the data looks like. Inferential Statistics are all about whether two sets of data are different or if two sets of data have a relationship.

Descriptive Statistics are a summary of what the sample data looks like, such as the measure of central tendency (e.g., mean for interval data) and measures of dispersion (e.b., standard deviation (SD) for interval data). Data that is dispersed about a mean like the bell-shape is normally distributed (i.e., 68.26%in 1 SD, 95.44% in 2 SD, 99.7% in 3 SD). The randomly drawn sample is best but rarely possible, so a non-random or convenience sample can be used with justification.

When compiling descriptive statistics, you need to know whether the sample data (i.e., level of measurement) is nominal (yes, no, or a label), ordinal (in some kind of order such as doneness of meat: rare, medium rare, medium, or well done) or a number that has order and the value means something (such as "that movie is an 8 on a scale of 10"). You also need to know the unit of analysis, such as the individual, group, organization, or society. Descriptive Statistics tell us what Inferential Statistics we can safely use to draw conclusions.

Inferential Statistics are how we make a decision about the POPULATION guided by what the Descriptive Statistics have told us about the SAMPLE data using probability theory. There are two types of decisions: Measures of Difference and Measures of Association. Measures of Difference (z, t, F, etc.) test differences between a number and sample, two samples or more than two samples. Measures of Association (r, correlation, regression) test whether variables move together and possibly whether there is some causal relationship. (Causal relationships are tricky to prove so be careful about saying X causes Y.)

When applying Inferential Statistics, the types of measures of difference or measures of association that can be used are governed by the level of measurement, the number of samples you are comparing, whether the sample is random/independent, and if the data is tightly dispersed about the mean like the normal distribution. When you are comparing samples, you have to make sure that the unit of analysis in each sample aligns with the other samples and your research question. (e.g., students in a classroom vs. a classroom of students, such as can a single student be judged by being in a particular class or should the particular class be judged by a single student.) Test statistics are calculated from sample data and critical values are looked up on a distribution (probability) table, and you compare these two in hypothesis testing. If you see a low p value, that is good.

All good quantitative research uses variations of the above instances to boil the research question down to a testable hypothesis for a large sample for descriptive, exploratory, or causal/experimental research. All good research articles explain how construct validity (i.e., theory or practical problem), external validity (i.e., how and why the sample was chosen), internal validity (i.e., why they think they saw is what they saw) and conclusion validity (i.e., how the descriptive and inferential statistics support our discussion) are achieved. A sample size of one in qualitative research might use ethnography, action research or other methods to build a case study or foundation for quantitative research.

That's it. That's about all a business manager or MBA must know about statistics. Of course, there is a lot more that you could know, but the basics can be mastered.

Rumsey's Ten Common Statistical Mistakes

Rumsey's Ten Common Statistical Mistakes

• Misleading Graphs
• Biased Data
• No Margin of Error [reported]
• Non-random Samples
• Missing Sample Sizes (i.e., not reported)
• Misinterpreted Correlations
• Confounding Variables (i.e., outside influences not discussed)
• Botched Numbers
• Selectively Reporting Results
• The Almighty Anecdote
  

Reference

Rumsey, D. (2003). Statistics for dummies. New York: Wiley.

Rumsey's Ten Criteria for a Good Survey

Rumsey's Ten Criteria for a Good Survey

• Target Population Well Defined
• Sample Matches the Target Population
• Sample is Randomly Selected
• Sample Size is Large Enough
• Good Follow-Up Minimizes Non-Response
• Type of Survey Used is Appropriate
• Questions are Well Worded
• Survey is Properly Timed
• Survey Personnel are Well Trained
• Survey Answers the Original Question
  

Reference

Rumsey, D. (2003). Statistics for dummies. New York: Wiley.

Understanding Null and Alternative Hypotheses

Is Grandma's Freezer Cold? (Understanding Null and Alternative Hypotheses)

When approaching business research, managers are sometimes confused by the concepts of the null and alternative hypotheses. The concepts are incredibly useful though, when the decision can be framed as a binary choice.

The null hypothesis embodies the condition that nothing has changed. For example, if we wished to learn if deep freezers were cold inside, we could think of the research in terms of null and alternative hypotheses.

The null hypothesis would be that the freezer in our sample is cold inside, which would be the normal condition. The alternative hypothesis would be that the freezer in our sample is not cold.

Therefore, to draw our sample, we walk up to Grandma's deep freezer, open the door, and stick our hand inside. Yes, Grandma's plugged in freezer is cold inside.

Internal validity, which means that what we saw what we thought we saw, is supported because we sensed that the freezer was cold with our own hands.

External validity is good in this case, which means that we can project our sample on the population of freezers that are plugged in (i.e., we did not check air conditioners, tap water, or ovens, but a freezer).

Construct validity, which is the theoretical background of measuring the temperature of freezers by sticking your hand in them, is supported because we have stuck our hand in all sorts of places to ascertain temperature before.

Conclusion validity, or support derived from statistically drawing a conclusion about all freezers from our sample of one, is not very good because our sample is very small.