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The research on capital punishment: Recent scholarship and unresolved questions

2014 review of research on capital punishment, including studies that attempt to quantify rates of innocence and the potential deterrence effect on crime.

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by Alexandra Raphel and John Wihbey, The Journalist's Resource January 5, 2015

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Over the past year the death penalty has again come into focus as a major public policy and political issue, catalyzed by several high-profile events.

The botched execution of convicted murderer and rapist Clayton Lockett in Oklahoma in 2014 was seen as a potential turning point in the debate, bringing increased attention to the mechanisms by which persons are executed. That was followed by a number of other closely scrutinized cases, and the year ended with few executions relative to years past. On December 31, 2014, Maryland Gov. Martin O’Malley commuted the sentences of the remaining four prisoners on death row in that state. In 2013, Maryland became the 18th state to abolish the death penalty after Connecticut in 2012 and New Mexico in 2009.

Meanwhile, polling data suggests some softening of public attitudes, though the majority Americans continue to support capital punishment. Gallop noted in October 2014 that the level of public support (60%) is at its lowest in 40 years. A Washington Post -ABC News poll in mid-2014 found that more Americans support life sentences, rather than the death penalty, for convicted murderers. Further, recent polls from the Pew Research Center indicate that only a bare majority of Americans now support capital punishment, 55%, down from 78% in 1996.

Scholarly research sheds light on a number of important aspects of this issue:

False convictions

One key reason for the contentious debate is the concern that states are executing innocent people. How many people are unjustly facing the death penalty? By definition, it is difficult to obtain a reliable answer to this question. Presumably if judges, juries, and law enforcement were always able to conclusively determine who was innocent, those defendants would simply not be convicted in the first place. When capital punishment is the sentence, however, this issue takes on new importance.

Some believe that when it comes to death-penalty cases, this is not a huge cause for concern. In his concurrent opinion in the 2006 Supreme Court case Kansas v. Marsh , Justice Antonin Scalia suggested that the execution error rate was minimal, around 0.027%. However, a 2014 study in the Proceedings of the National Academy of Sciences suggests that the figure could be higher. Authors Samuel Gross (University of Michigan Law School), Barbara O’Brien (Michigan State University College of Law), Chen Hu (American College of Radiology) and Edward H. Kennedy (University of Pennsylvania School of Medicine) examine data from the Bureau of Justice Statistics and the Department of Justice relating to exonerations from 1973 to 2004 in an attempt to estimate the rate of false convictions among death row defendants. (Determining innocence with full certainty is an obvious challenge, so as a proxy they use exoneration — “an official determination that a convicted defendant is no longer legally culpable for the crime.”) In short, the researchers ask: If all death row prisoners were to remain under this sentence indefinitely, how many of them would have eventually been found innocent (exonerated)?

Interestingly, the authors also note that advances in DNA identification technology are unlikely to have a large impact on false conviction rates because DNA evidence is most often used in cases of rape rather than homicide. To date, only about 13% of death row exonerations were the result of DNA testing. The Innocence Project , a litigation and public policy organization founded in 1992, has been deeply involved in many such cases.

Death penalty deterrence effects: What do we know?

A chief way proponents of capital punishment defend the practice is the idea that the death penalty deters other people from committing future crimes. For example, research conducted by John J. Donohue III (Yale Law School) and Justin Wolfers (University of Pennsylvania) applies economic theory to the issue: If people act as rational maximizers of their profits or well-being, perhaps there is reason to believe that the most severe of punishments would serve as a deterrent. (The findings of their 2009 study on this issue, “Estimating the Impact of the Death Penalty on Murder,” are inconclusive.) In contrast, one could also imagine a scenario in which capital punishment leads to an increased homicide rate because of a broader perception that the state devalues human life. It could also be possible that the death penalty has no effect at all because information about executions is not diffused in a way that influences future behavior.

In 1978 — two years after the Supreme Court issued its decision reversing a previous ban on the death penalty ( Gregg v. Georgia ) — the National Research Council (NRC) published a comprehensive review of the current research on capital punishment to determine whether one of these hypotheses was more empirically supported than the others. The NRC concluded that “available studies provide no useful evidence on the deterrent effect of capital punishment.”

Researchers have subsequently used a number of methods in an effort to get closer to an accurate estimate of the deterrence effect of the death penalty. Many of the studies have reached conflicting conclusions, however. To conduct an updated review, the NRC formed the Committee on Deterrence and the Death Penalty, comprised of academics from economics departments and public policy schools from institutions around the country, including the Carnegie Mellon University, University of Chicago and Duke University.

In 2012, the Committee published an updated report that concluded that not much had changed in recent decades: “Research conducted in the 30 years since the earlier NRC report has not sufficiently advanced knowledge to allow a conclusion, however qualified, about the effect of the death penalty on homicide rates.” The report goes on to recommend that none of the reviewed reports be used to influence public policy decisions on the death penalty.

Why has the research not been able to provide any definitive answers about the impact of the death penalty? One general challenge is that when it comes to capital punishment, a counter-factual policy is simply not observable. You cannot simultaneously execute and not execute defendants, making it difficult to isolate the impact of the death penalty. The Committee also highlights a number of key flaws in the research designs:

• There are both capital and non-capital punishment options for people charged with serious crimes. So, the relevant question on the deterrent effect of capital punishment specifically “is the differential deterrent effect of execution in comparison with the deterrent effect of other available or commonly used penalties.” None of the studies reviewed by the Committee took into account these severe, but noncapital punishments, which could also have an effect on future behaviors and could confound the estimated deterrence effect of capital punishment.
• “They use incomplete or implausible models of potential murderers’ perceptions of and response to the capital punishment component of a sanction regime”
• “The existing studies use strong and unverifiable assumptions to identify the effects of capital punishment on homicides.”

In a 2012 study, “Deterrence and the Dealth Penalty: Partial Identificaiton Analysis Using Repeated Cross Sections,” authors Charles F. Manski (Northwestern University) and John V. Pepper (University of Virginia) focus on the third challenge. They note: “Data alone cannot reveal what the homicide rate in a state without (with) a death penalty would have been had the state (not) adopted a death penalty statute. Here, as always when analyzing treatment response, data must be combined with assumptions to enable inference on counterfactual outcomes.”

However, even though the authors do not arrive at a definitive conclusion, the National Research Council Committee notes that this type of research holds some value: “Rather than imposing the strong but unsupported assumptions required to identify the effect of capital punishment on homicides in a single model or an ad hoc set of similar models, approaches that explicitly account for model uncertainty may provide a constructive way for research to provide credible albeit incomplete answers.”

Another strategy researchers have taken is to limit the focus of studies on potential short-term effects of the death penalty. In a 2009 paper, “The Short-Term Effects of Executions on Homicides: Deterrence, Displacement, or Both?” authors Kenneth C. Land and Hui Zheng of Duke University, along with Raymond Teske Jr. of Sam Houston State University, examine monthly execution data (1980-2005) from Texas, “a state that has used the death penalty with sufficient frequency to make possible relatively stable estimates of the homicide response to executions.” They conclude that “evidence exists of modest, short-term reductions in the numbers of homicides in Texas in the months of or after executions.” Depending on which model they use, these deterrent effects range from 1.6 to 2.5 homicides.

The NRC’s Committee on Deterrence and the Death Penalty commented on the findings, explaining: “Land, Teske and Zheng (2009) should be commended for distinguishing between periods in Texas when the use of capital punishment appears to have been erratic and when it appears to have been systematic. But they fail to integrate this distinction into a coherently delineated behavioral model that incorporates sanctions regimes, salience, and deterrence. And, as explained above, their claims of evidence of deterrence in the systematic regime are flawed.”

A more recent paper (2012) from the three authors, “The Differential Short-Term Impacts of Executions on Felony and Non-Felony Homicides,” addresses some of these concerns. Published in Criminology and Public Policy , the paper reviews and updates some of their earlier findings by exploring “what information can be gained by disaggregating the homicide data into those homicides committed in the course of another felony crime, which are subject to capital punishment, and those committed otherwise.” The results produce a number of different findings and models, including that “the short-lived deterrence effect of executions is concentrated among non-felony-type homicides.”

Other factors to consider

The question of what kinds of “mitigating” factors should prevent the criminal justice system from moving forward with an execution remains hotly disputed. A 2014 paper published in the Hastings Law Journal , “The Failure of Mitigation?” by scholars at the University of North Carolina and DePaul University, investigates recent executions of persons with possible mental or intellectual disabilities. The authors reviewed 100 cases and conclude that the “overwhelming majority of executed offenders suffered from intellectual impairments, were barely into adulthood, wrestled with severe mental illness, or endured profound childhood trauma.”

Two significant recommendations for reforming the existing process also are supported by some academic research. A 2010 study by Pepperdine University School of Law published in Temple Law Review , “Unpredictable Doom and Lethal Injustice: An Argument for Greater Transparency in Death Penalty Decisions,” surveyed the decision-making process among various state prosecutors. At the request of a state commission, the authors first surveyed California district attorneys; they also examined data from the other 36 states that have the death penalty. The authors found that prosecutors’ capital punishment filing decisions remain marked by local “idiosyncrasies,” meaning that “the very types of unfairness that the Supreme Court sought to eliminate” beginning in 1972 may still “infect capital cases.” They encourage “requiring prosecutors to adhere to an established set of guidelines.” Finally, there has been growing support for taping interrogations of suspects in capital cases, so as to guard against the phenomenon of false confessions .

Related reading: For an international perspective on capital punishment, see Amnesty International’s 2013 report ; for more information on the evolution of U.S. public opinion on the death penalty, see historical trends from Gallup .

Keywords: crime, prisons, death penalty, capital punishment

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10 facts about the death penalty in the U.S.

Most U.S. adults support the death penalty for people convicted of murder, according to an April 2021 Pew Research Center survey . At the same time, majorities believe the death penalty is not applied in a racially neutral way, does not deter people from committing serious crimes and does not have enough safeguards to prevent an innocent person from being executed.

Use of the death penalty has gradually declined in the United States in recent decades. A growing number of states have abolished it, and death sentences and executions have become less common. But the story is not one of continuous decline across all levels of government. While state-level executions have decreased, the federal government put more prisoners to death under President Donald Trump than at any point since the U.S. Supreme Court reinstated capital punishment in 1976.

As debates over the death penalty continue in the U.S. , here’s a closer look at public opinion on the issue, as well as key facts about the nation’s use of capital punishment.

This Pew Research Center analysis examines public opinion about the death penalty in the United States and explores how the nation has used capital punishment in recent decades. 

The public opinion findings cited here are based primarily on a Pew Research Center survey of 5,109 U.S. adults, conducted from April 5 to 11, 2021. Everyone who took part in the survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology . Here are the  questions used  from this survey, along with responses, and its  methodology .

Findings about the administration of the death penalty – including the number of states with and without capital punishment, the annual number of death sentences and executions, the demographics of those on death row and the average amount of time spent on death row – come from the Death Penalty Information Center and the Bureau of Justice Statistics.

Six-in-ten U.S. adults strongly or somewhat favor the death penalty for convicted murderers, according to the April 2021 survey. A similar share (64%) say the death penalty is morally justified when someone commits a crime like murder.

Support for capital punishment is strongly associated with the view that it is morally justified in certain cases. Nine-in-ten of those who favor the death penalty say it is morally justified when someone commits a crime like murder; only a quarter of those who oppose capital punishment see it as morally justified.

A majority of Americans have concerns about the fairness of the death penalty and whether it serves as a deterrent against serious crime. More than half of U.S. adults (56%) say Black people are more likely than White people to be sentenced to death for committing similar crimes. About six-in-ten (63%) say the death penalty does not deter people from committing serious crimes, and nearly eight-in-ten (78%) say there is some risk that an innocent person will be executed.

Opinions about the death penalty vary by party, education and race and ethnicity. Republicans and Republican-leaning independents are much more likely than Democrats and Democratic leaners to favor the death penalty for convicted murderers (77% vs. 46%). Those with less formal education are also more likely to support it: Around two-thirds of those with a high school diploma or less (68%) favor the death penalty, compared with 63% of those with some college education, 49% of those with a bachelor’s degree and 44% of those with a postgraduate degree. Majorities of White (63%), Asian (63%) and Hispanic adults (56%) support the death penalty, but Black adults are evenly divided, with 49% in favor and 49% opposed.

Views of the death penalty differ by religious affiliation . Around two-thirds of Protestants in the U.S. (66%) favor capital punishment, though support is much higher among White evangelical Protestants (75%) and White non-evangelical Protestants (73%) than it is among Black Protestants (50%). Around six-in-ten Catholics (58%) also support capital punishment, a figure that includes 61% of Hispanic Catholics and 56% of White Catholics.

Opposition to the death penalty also varies among the religiously unaffiliated. Around two-thirds of atheists (65%) oppose it, as do more than half of agnostics (57%). Among those who say their religion is “nothing in particular,” 63% support capital punishment.

Support for the death penalty is consistently higher in online polls than in phone polls. Survey respondents sometimes give different answers depending on how a poll is conducted. In a series of contemporaneous Pew Research Center surveys fielded online and on the phone between September 2019 and August 2020, Americans consistently expressed more support for the death penalty in a self-administered online format than in a survey administered on the phone by a live interviewer. This pattern was more pronounced among Democrats and Democratic-leaning independents than among Republicans and GOP leaners, according to an analysis of the survey results .

Phone polls have shown a long-term decline in public support for the death penalty. In phone surveys conducted by Pew Research Center between 1996 and 2020, the share of U.S. adults who favor the death penalty fell from 78% to 52%, while the share of Americans expressing opposition rose from 18% to 44%. Phone surveys conducted by Gallup found a similar decrease in support for capital punishment during this time span.

A majority of states have the death penalty, but far fewer use it regularly. As of July 2021, the death penalty is authorized by 27 states and the federal government – including the U.S. Department of Justice and the U.S. military – and prohibited in 23 states and the District of Columbia, according to the Death Penalty Information Center . But even in many of the jurisdictions that authorize the death penalty, executions are rare: 13 of these states, along with the U.S. military, haven’t carried out an execution in a decade or more. That includes three states – California , Oregon and Pennsylvania – where governors have imposed formal moratoriums on executions.

A growing number of states have done away with the death penalty in recent years, either through legislation or a court ruling. Virginia, which has carried out more executions than any state except Texas since 1976, abolished capital punishment in 2021. It followed Colorado (2020), New Hampshire (2019), Washington (2018), Delaware (2016), Maryland (2013), Connecticut (2012), Illinois (2011), New Mexico (2009), New Jersey (2007) and New York (2004).

Death sentences have steadily decreased in recent decades. There were 2,570 people on death row in the U.S. at the end of 2019, down 29% from a peak of 3,601 at the end of 2000, according to the Bureau of Justice Statistics (BJS). New death sentences have also declined sharply: 31 people were sentenced to death in 2019, far below the more than 320 who received death sentences each year between 1994 and 1996. In recent years, prosecutors in some U.S. cities – including Orlando and Philadelphia – have vowed not to seek the death penalty, citing concerns over its application.

Nearly all (98%) of the people who were on death row at the end of 2019 were men. Both the mean and median age of the nation’s death row population was 51. Black prisoners accounted for 41% of death row inmates, far higher than their 13% share of the nation’s adult population that year. White prisoners accounted for 56%, compared with their 77% share of the adult population. (For both Black and White Americans, these figures include those who identify as Hispanic. Overall, about 15% of death row prisoners in 2019 identified as Hispanic, according to BJS.)

Annual executions are far below their peak level. Nationally, 17 people were put to death in 2020, the fewest since 1991 and far below the modern peak of 98 in 1999, according to BJS and the Death Penalty Information Center. The COVID-19 outbreak disrupted legal proceedings in much of the country in 2020, causing some executions to be postponed .

Even as the overall number of executions in the U.S. fell to a 29-year low in 2020, the federal government ramped up its use of the death penalty. The Trump administration executed 10 prisoners in 2020 and another three in January 2021; prior to 2020, the federal government had carried out a total of three executions since 1976.

The Biden administration has taken a different approach from its predecessor. In July 2021, Attorney General Merrick Garland ordered a halt in federal executions while the Justice Department reviews its policies and procedures.

The average time between sentencing and execution in the U.S. has increased sharply since the 1980s. In 1984, the average time between sentencing and execution was 74 months, or a little over six years, according to BJS . By 2019, that figure had more than tripled to 264 months, or 22 years. The average prisoner awaiting execution at the end of 2019, meanwhile, had spent nearly 19 years on death row.

A variety of factors explain the increase in time spent on death row, including lengthy legal appeals by those sentenced to death and challenges to the way states and the federal government carry out executions, including the drugs used in lethal injections. In California, more death row inmates have died from natural causes or suicide than from executions since 1978, according to the state’s Department of Corrections and Rehabilitation .

Note: This is an update to a post originally published May 28, 2015.

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Messing Up Texas?: A Re-Analysis of the Effects of Executions on Homicides

Patrick t. brandt.

1 Political Science, Economic, Political and Policy Sciences, University of Texas, Dallas, Richardson, Texas, United States of America

Tomislav V. Kovandzic

2 Criminology, Economic, Political and Policy Sciences, University of Texas, Dallas, Richardson, Texas, United States of America

Analyzed the data: PTB. Contributed reagents/materials/analysis tools: PTB TVK. Wrote the paper: PTB TVK.

Associated Data

All relevant data are within the paper and its Supporting Information files.

Executions in Texas from 1994–2005 do not deter homicides, contrary to the results of Land et al. (2009). We find that using different models—based on pre-tests for unit roots that correct for earlier model misspecifications—one cannot reject the null hypothesis that executions do not lead to a change in homicides in Texas over this period. Using additional control variables, we show that variables such as the number of prisoners in Texas may drive the main drop in homicides over this period. Such conclusions however are highly sensitive to model specification decisions, calling into question the assumptions about fixed parameters and constant structural relationships. This means that using dynamic regressions to account for policy changes that may affect homicides need to be done with significant care and attention.

Introduction

In the last decade there is a resurgence of academic studies estimating the possible deterrent effect of capital punishment on homicide rates [ 2 – 6 ] With few exceptions [ 7 , 8 ] these recent deterrence studies employ non-experimental fixed-effects panel designs that span the period since the reinstatement of the death penalty in the U.S. after the 1976 Supreme Court decision in Gregg v. Georgia , and use ordinary least squares (OLS) or instrumental variables (IV) estimators. Joana Shepherd, author of several of these studies, summarizes the latest econometric findings in her congressional testimony as follows: “The modern studies have consistently shown that capital punishment has a strong deterrent effect, with each execution deterring between 3 and 18 murders” [ 9 ]. Another leading researcher in this area, Nai Mocan, the co-author of two of the recent panel studies is quoted in an Associated Press report about the robustness of the deterrent effects of executions, saying “Science does really draw a conclusion. It did. There is no question about it. The conclusion is there is a deterrent effect” [ 10 ].

Like their national, aggregate time-series predecessors [ 11 ], these pro-deterrence death penalty papers have been subjected to considerable academic scrutiny, with critics highlighting numerous conceptual and methodological problems. These include the failure of the research properly mitigating omitted variable bias (e.g., prison population growth), using possibly dubious deterrence ratio variables as proxies for potential homicide perpetrators’ perceptions of the expected costs of committing capital murder, assuming that executions are simultaneously determined (i.e., executions are endogenous events) through the application of the instrumental variables (IV) estimator (and even if that were the case using invalid and unreliable instrumental variables to instrument for execution risk), failing to correct standard errors for the presence of serial correlation, and sampling fragility of the results to alternative periods and outliers [ 12 – 16 ]. Despite several studies addressing these methodological shortcomings, the literature confirms critics’ suspicions that the recent findings were largely a byproduct of the aforementioned criticisms. When these issues are addressed, there is little or no robust empirical evidence of a significant relationship between the presence of the death penalty or increases in execution risk and the homicides rate. The pro-deterrence authors counter that the problems highlighted were either incorrect or inconsequential to their original conclusions [ 2 , 17 , 18 ].

Given this conflicting body of evidence, and the fact that many of the pro-deterrence death penalty research findings have found their way into the policy-making arena, it is not surprising then that the National Research Council (NRC) convened a panel of scholars to reconcile the latest scientific evidence on the deterrent effects of the death penalty. The NRC committee concluded that “research to date on the effect of capital punishment is not informative about whether capital punishment decreases, increases, or has no effect on homicide rates. Therefore, the committee recommends that these studies not be used to inform deliberations requiring judgments about the effect of the death penalty on homicide” [ 19 ].

[ 1 ] recently tackle another limitation of the modern panel studies of the death penalty: How to reliably identify the causal effect of executions on homicide given the sporadic, infrequent, or nonexistent application of executions in the majority of states with active capital punishment statutes. Fig 1 shows that of the 36 states with active death penalty statutes in 2009, only 13 have carried out more than twenty executions since the Gregg decision and 19 states have recorded 10 or fewer executions during this period [ 20 ]. What can also be inferred from Fig 1 is that most death penalty states in most years do not execute a single death row inmate. Berk’s [ 12 ] reanalysis of [ 4 ] reveals the highly skewed nature of the execution distribution in state panel data. Specifically, 86 percent of the state-year observations on the execution variable were equal to 0 with another 8 percent of the observations equal to 1. Importantly, only 5 percent of the observations contained execution values larger than 5. Given the relative infrequency of executions in the post- Gregg era, it is no surprise then that one of the main concerns raised by those assessing the sensitivity of the modern statistical evidence on the death penalty has been the suitability of panel studies when only a handful of states account for the lion’s share of the nation’s executions [ 1 , 12 , 14 ]. This is the main conclusions drawn by [ 13 ] after sifting through the recent death penalty literature: “Our key insight is that the death penalty at least as it has been implemented in the United States since Gregg ended the moratorium on executions is applied so rarely that the number of homicides it can plausibly have caused or deterred cannot be reliably disentangled from the large year-to-year changes in the homicide rate caused by other factors.” Berk’s [ 12 ] reanalysis of the state panel dataset of [ 4 ] shows that a few leverage points have unusually large effect on the conclusions about deterrence. Berk shows that their pro-deterrence findings were largely driven by 11 of the most influential observations (just 1 percent of the 1,000 state-year observations), with most of those observations occurring in Texas.

Not surprisingly, Land et al.’s [ 1 ] solution to this low dosage or insufficient treatment problem is to zero in on Texas, a state accounting for 464 of the 1,231 (38 percent) of all persons executed since the Gregg decision [ 20 , 21 ]. They apply autoregressive integrated moving average (ARIMA) intervention time-series methods [ 22 ] to first-differences of monthly data on executions and homicides in Texas from 1994–2005. They build a series of seasonal linear transfer function models, using first and seasonal differences of both executions and homicides based on the assumption that “short-run” effects of executions needed to be separated from any “long-run” trends (or non-stationarity). Their decision to focus on the post-1993 period is based on U.S. Supreme Court decisions in McCleskey v. Zant and Herrera v. Collins which greatly limited habeas corpus petitions brought on by death row inmates in federal courts. They claim that these court decisions marked the beginning of a regime change in Texas since the number of death row inmates executed from 1994 through 2005, compared to the 1980 to 1993, grew by 300% (from 71 to 284) or from an average of 0.42 executions a month to nearly 2 executions per month. Therefore, the increased frequency and consistency of executions in the post-1993 period makes Texas a fertile test site for examining the deterrent effects of executions. [ 1 ] apply Granger causality tests to assess whether one or two-way causality exists between the two series and find no evidence of Granger feedback from homicide to executions. They build a series of seasonal linear transfer function models. Results for their best fitting model (see, their Table 2 , Model 7) reveal a statistically significant reduction of 1.3 homicides in the first month following an execution with additional reduction of 1.2 homicides occurring 4 months later—for a total deterrent effect of 2.5 homicides per execution.

Standard errors in parentheses.

In our view, Land et al.’s [ 1 ] decision to first difference the execution and homicide series without formally testing for the presence of trends (be they deterministic, stochastic, or seasonal) leads to incorrect inferences about their key results. We argue and demonstrate that differencing possibly stationary or deterministically trending series such as executions and homicides induces false dynamics, making it wrongly appear that a causal relationship exists between the two series because of the false correlations. Once we test for and properly model the data, the subsequent specifications are more parsimonious than those reported by [ 1 ] and strongly resist the interpretation that executions in Texas significantly deter homicides . Perhaps more importantly, we show that the deterrent effects of executions are inflated and largely spurious because of the authors’ failure to control for other important factors coinciding with Texas’ execution binge and decline in homicides—the incapacitative effects of prison growth generally, and an improving economy (proxied with unemployment rates). The results of our analysis do not support the conclusions drawn by [ 1 ]. When controlling for changes in the number of persons incarcerated and unemployed, the magnitude of the effects of executions on homicide are less than half the published estimates and are statistically indistinguishable from zero. Consistent with previous research, we find that changes in incarceration were primarily responsible for reducing homicide after the mid-1990s. We employ the latest econometric time-series methods for interpreting time-series results, a set of impulse response functions with error bands that quantify the relative uncertainty of the effects of an execution on the number of subsequent homicides. Finally, we show that these results are robust to different, possibly endogenous changes in parameters using a series of changepoint models as a robustness test.

Monthly Texas Homicides Time Series, 1994–2005

The Land et al. [ 1 ] claims about the deterrent effects of the death penalty on homicides in Texas relies upon a monthly analysis of the relationships of the executions on homicides. Based on data from 1994–2005, they conclude that the use of the death penalty in Texas reduces the short term number of monthly homicides by 2.5 based on ARIMA models with multiplicative seasonal processes. (We thank Ken Land and Hui Zheng for providing us their data.)

Like in most of the modern time series literature, we start with an initial characterization of the time series properties of monthly homicides in Texas. We do this so that one can then know the empirical regularities that need to be captured in building any model that purports to explain or predict aggregate homicides in Texas over recent decades. Despite the necessity to first analyze the dynamic properties of the time series of interest and then incorporate the effects of covariates, [ 1 ] rely on what we consider to be multiple ad hoc model specifications to assess the deterrent effects of executions on homicides. The end result is a set of atheoretical models that fail to account for the dynamic properties of the Texas homicide series and more importantly, induce spurious correlations with executions.

We start with an analysis of the presence of the deterministic trends, stochastic trends and seasonal trends or cycles in the homicides. This is part of the standard Box-Jenkins or dynamic regression framework. The initial goal of this analysis is to capture those parts of the time series of interest so we can separate out any potential correlates of homicide.

Critical to the specification analysis of these models are the determination of the presence of either trend or difference stationarity, with and without drift in the data. A deterministic trend stationary variable, X t is a function of time, where the regression X t = βt leads to a description of the trend. A difference stationary series is one where the first differences of the series of interest, say X t is rendered non-trending by first differencing or using the X t − X t − 1 = Y t values of changes as the dependent variable of interest. One then proceeds to model these first differences as a function of their lags and a constant, such as Y t = c + Σ ℓ = 1 p ϕ ℓ Y t - ℓ , where c would be the drift or the rate of change in the X t series and the ϕ ℓ coefficients capturing the remaining short run dynamics. If the trend model includes a constant, c , this is interpreted as a drift term which indicates the secular shift in the trend over time. Additionally, a time series may include both deterministic and stochastic trends or drift.

Determining the presence of trends is a critical part of ARIMA modeling. The analysis of [ 1 ] focuses on the short term effects, under the presumption that there was a stochastic trend in Texas homicides that needed to be removed. Here we show this is not the case. Specifying a trend or differencing the data when no stochastic trend is present leads to an incorrect ARIMA model and an incorrect estimate of the effects of executions on homicide reduction. We provide details on why this is the case below. We show how this can be corrected and provide proper inferences about this relationship. The results show that executions are not meaningful predictors of the reduction in the number of homicides in Texas. Further, we show that the [ 1 ] findings are a methodological artifact that is not robust to alternative dynamic model specifications and the inclusion of relevant correlates of homicide to mitigate omitted variable biases.

Testing for trends, 1994–2005

To test for trends in the Texas monthly homicide series one can consider any of several tests in the literature. We start with the two of the more common: the augmented Dickey-Fuller (ADF) and the KPSS tests [ 23 ]. The ADF test assumes that the time series model is a random walk, or that the model of a time series y t is

where the coefficient ρ = 1 and the error process is Gaussian white noise. If we reject that ρ = 1, then we conclude that the series is stationary. The test is augmented by additional lagged values of the series to account for residual serial correlation. These additional lags are selected by a model fit statistic such as the Akaike Information Criteria (AIC). Further versions of this test can be constructed that test whether the deterministic trend coefficient τ trend or c differ from zero. These versions allow one to rule out stochastic and deterministic drift in the monthly homicides time series.

The KPSS tests are parallels to the ADF tests [ 24 ]. Instead of evaluating the null hypothesis that ρ = 1, the null hypothesis is that the series is stationary or ∣ ρ ∣ ≠ 1. This test also has variants for whether a time trend is included in the model. As with the ADF test, there are short (4 lags) and long (13 lags) corrections to account for serial correlation.

Table 1 presents the results of a battery of ADF and KPSS tests applied to the 1994–2005 Texas monthly homicides series that are the main evidence in [ 1 ]. These tests have non-standard test statistic distributions that depend on the sample size and model specification; these are reproduced here for easy exposition. In total nine tests were computed to determine whether there is a trend in the Texas homicide data.

Critical values given the right-most columns.

Test 1 assesses the null of a unit root with no drift or deterministic trend using the ADF specification with no drift—or that c = 0 and τ trend = 0. Based on the critical values in the right-most columns of the table, the test statistic of -0.94 does not reject the null hypothesis that the series is a unit root or trending variable. Note however that this model includes no drift or deterministic component. These are added in the ADF tests 2 and 3. In both tests the null hypothesis of a unit root is strongly rejected. The drift terms are significant as is the trend term. So we can reject the unit root hypothesis if we include a deterministic trend and/or a constant when modeling the series.

The KPSS results, Tests 4–9, use a different null hypothesis (stationarity and not a unit root, or ρ = 0 under the null), but come to the same conclusions. Tests 4 and 5 reject the null of stationarity, but with only a short lag correction for serial correlation. When longer lagged corrections for serial correlation are added, as in Tests 6 and 7, there is less evidence for rejecting stationarity. Finally, in Tests 8 and 9 where stationarity is assumed under the null with no corrections for serial correlation, the tests strongly reject the null of stationarity (in part because of the unmodeled serial correlation).

There is strong reason to suspect that differencing the data (both first and seasonal differencing) is incorrect. There is only weak to no evidence for non-stationarity or stochastic trends. In fact, the ADF and KPSS tests (Tests 2, 3, and 6) offer scant evidence for non-stationarity once a trend and/or constant are included in the model. So across the most general of the tests (2, 3, and 6) one sees equivalent results: there is no need to difference the data and there may be evidence of a deterministic trend. The reason to prefer these test results is that they are for more general null specifications that include different possible trends and serial correlation corrections. Failing to account for these alternative trends leads to potentially conflicting and erroneous inferences in the ADF and KPSS tests.

We can graphically examine the data and some autocorrelations to see if there are trends in Texas homicides between 1994 and 2005. Fig 2 plots the raw data, the first and seasonal differenced data used in the [ 1 ] analysis and their associated autocorrelation functions (ACF) and partial autocorrelation functions (PACF). Column 1 (2) shows the data and correlations for the raw monthly (differenced) homicides time series. The ACFs show the raw correlations over different lags (measured in terms of the monthly period of the data) and the partial autocorrelations. The ACFs capture the pattern of raw correlations over lags t − 1, t − 2,…, and are used to assess the patterns of decaying autoregressive lags and moving averages. The PACFs capture the lags at higher lags controlling for those at lower lags, and are used to identify patterns of decaying and seasonal serial correlation. Data that are overdifferenced will exhibit serial correlation and moving averages at the period of the differencing [ 25 ].

The left (right) column of Fig 2 is the raw (differenced) data and correlation functions. The undifferenced series appears near stationary and has an ACF that decays slowly toward zero across 1.5 periods (18 months), indicative of a seasonally stationary process. The PACF for this series confirms this, with no significant lags after the first 12 months (period 1.0). The differenced series in the second column shows weaker to no autocorrelation but has statistically significant moving average spikes at lags 1 and 12 (at periods 0 and 1.0). These ACF and PACF results for the differenced series are consistent with those for a stationary series that has been overdifferenced. To see this, consider a stationary white noise time series process, or y t = ϵ t where ϵ t ∼ N ( μ , σ 2 ) (where the distributional requirement can be relaxed). If we first difference this series (regardless of the stationary dynamic process for y t ) we see:

where we have assumed that θ = 1 and ∇ is a first differencing operator. So we have taking a stationary time series that may or may not have had a moving average component term and included one where θ = 1. This implies an autocorrelation function where the first order autocorrelation is - θ 1 + θ = - 0 . 5 . So even if there is truly first order autocorrelation, it is not eliminated by taking this difference—it is just replaced by a spurious one.

The easiest way to see this time series identification point is to generate a series of independent and identically distributed or uncorrelated random variates and then apply a first and seasonal differencing to them. The resulting ACFs and PACFs will look like those in the second column of Fig 2 with PACF spikes of -0.5 at period 1. Since the generated data are serially uncorrelated, the resulting patterns are the result of overdifferencing the data. This is the same approach used to determine the order of differencing in non-stationary data, since when too many additional differences are taken, the result are excess moving average processes looking like those described here.

Based on these unit root tests, and the ACF and PACF results, it is very unlikely that Texas homicides over the 1994–2005 sample period should be modeled using first and seasonal differences, since there is no evidence of unit roots. In what follows we examine models for the undifferenced homicides’ time series since there is little to no evidence to support the approach taken by Land et al. Our assumption is that the series is stationary with possible changes in deterministic trends or regime.

Identification, specification, and estimation, 1994–2005

We begin, with possible transfer functions for the the dynamics of the explanatory or input variables [ 22 ], analyzing the homicide series. This analysis begins with an investigation of the dynamics of the 1994–2005 Texas monthly homicide series. Based on Fig 2 and the earlier unit root tests, the initial model is a multiplicative seasonal ARIMA model with p autoregressive lags, d differences, q moving average terms, P seasonal, D seasonal differences, and Q seasonal moving averages. This is written as an ARIMA ( p , d , q )( P , D , Q ) s model where s is the period of the seasonality, in this case s = 12. Since the last section offered no evidence to support differencing, we set d = D = 0.

The initial model for the Texas homicide series h t sample is an ARIMA (3,0,0)(1,0,1) 12 with a deterministic linear trend t . This model was selected based on the results of the earlier unit root tests which indicated that there was no stochastic trend (so no need for first differencing), but the possibility of a deterministic trend as a function of time. The ACF and PACF functions provided an initial idea of the serial correlation patterns and lags to be used. These were then refined until white noise residuals were achieved based on Box-Ljung statistics for correlations at various lags up to 16 months. The results of this estimation strategy are (with standard errors in parentheses):

The Box-Ljung statistics for residual serial correlation in indicate no unmodelled serial correlation for lags one through 16. This is an ARIMA model with several interesting features. First, the sum of the first three autoregressive terms is stationary since ∣0.79∣ < 1 and all of the coefficients are positive. The fact that this sum is less than 1 is indicative of a stationary process. The ARIMA results are consistent with the earlier unit root tests and provide evidence that the homicide series should not be first or seasonally differenced prior to analysis. Second, the deterministic trend term t has a p-value of 0.03 or a significant negative trend. This captures the downward trend in the number of Texas homicides over the sample: the mean number of homicides per month is 1994 is 169 while it is 117 in 2005. Third, the seasonal component is stationary with a seasonal AR term of 0.93. A seasonally differenced version of this model is plagued by serial correlation and fits worse than this model. This alternative model’s Box-Ljung statistics indicate serial correlation after lag three. The error variance of the estimate for the seasonally differenced model is 231; it is 224 for the non-differenced data model in Eq (4) .

Since this model has no residual serial correlation it is a good basis for inference about the Texas homicides. Any efforts to model this series as a function of other variables has to have them mimic the properties seen in this basic ARIMA model: there is positive serial correlation, strong seasonal effects that decay slowly, and a shift over time in the trend.

Transfer function analysis, 1994–2005

One of the most difficult challenges facing death penalty researchers using either a single time-series or panel designs concerns specifying the timing for when changes in execution risk are supposed to have an effect on the number of homicides. Should changes in execution risk have an immediate or lagged effect on homicides? While it may be difficult in many cases to specify when a change in law or policy is supposed to have an effect on the target variable, in those instances where theory (or an understanding of the intervention) does provide guidance on the matter, it is imperative those candidates be selected and tested [ 26 ]. Failure to do so can result in making ex post facto interpretations (which may be true) for unexpected results, thereby making the policy efficacy hypothesis nearly impossible to falsify [ 27 ]. As noted over a half a century ago by [ 28 ]: “If such time series are to be interpreted as experiments, it seems essential that the experimenter must specify in advance the expected time relationship between the introduction of the experimental variable and the manifestation of an effect” (emphasis added).

The most common intervention point in the single time-series or panel design death penalty literature is a one time period lag of the execution risk measure under the assumption that it takes time for actual changes in execution risk to alter prospective killers perceptions of execution risk. However, we are not aware of any compelling explanation in the deterrence literature as to why it would take several months or up to a year for prospective killers, whether assessing risk consciously or unconsciously, to be reminded of the possibility of being executed for murder. Land et al. [ 1 ] take another approach in the time series policy literature, relying on ARIMA model building methods to identify the best-fitting model (i.e., let the execution and homicide data speak for themselves). They include all lags of executions 1 to 12 in the homicide models and, as noted above, find that the best fitting ARIMA model is one which produces statistically significant coefficients for executions at lags 1 and 4. They interpret the significant coefficients for executions as a deterrent effect of the death penalty and provide a statistical explanation for why the deterrent effect of executions ceases to exist after one month and then picks up again three months later. As far as we know, there is nothing in the deterrence doctrine or any other criminological theory that can even remotely explain the impact patterns they have identified. Put another way, the belief that prospective killers consider execution risk in the subsequent month following an execution, cease to consider risk of execution two and three months later, but then consider risk of execution some four months later is implausible at best, impossible at worst.

We suggest the more theoretically defensible model, within the deterrence framework, is to estimate a dynamic input model that adds contemporaneous and once lagged counts of the numbers of executions as predictors in the homicide model of Eq (4) . To the extent that deterrence depends on conscious consideration of risk, more frequent and recent executions should serve as the greatest reminder to prospective killers of the risk of execution. Conscious assessment of risk does not have to be very sophisticated, and thus does not even necessarily entail comparing execution frequency from one month to the next. Conversely, preconscious considerations of execution risk, especially those rooted in fear, might also be influenced by places and times with more frequent executions. While it is impossible to know for sure how execution frequency is treated by prospective killers in risk assessment calculations, assuming they are considered at all, we believe this model offers the most plausible scenario by which deterrent effects via frequent executions is likely to manifest itself.

Table 2 presents the results for the ARIMA model with the contemporaneous and lagged monthly number of executions included in the model. The basic ARIMA model specification is as in Eq (4) and is presented in the first column of Table 2 . The only change here is the addition of the regressor(s) for the number of executions. In the table, column 1 reproduces the baseline ARIMA model for the homicide series with ϕ j as the autoregressive coefficient for the j ′ th lag, t is the deterministic linear time trend and θ k is the k ′ th lag moving average coefficient.

The contemporaneous effect of executions on homicides is very small. The effect of contemporaneous executions on homicides is −0.93 with a standard error of 0.68. The two-sided p-value for whether this contemporaneous effect in column 2 of Table 2 differs from zero is 0.17. The p-value of the joint effect of the contemporaneous and lagged effects modeled in column three of Table 2 is 0.20. The total effect or cumulative drop in the number of homicides is given by the impact multiplier

So there are four fewer homicides after an execution. The estimate for the model with two lagged values of executions is -7.3 fewer homicides. Repeating the earlier impact multiplier computation for the model with the contemporaneous and lagged executions as regressors yields - 0 . 90 - 0 . 82 1 - 0 . 32 - 0 . 15 - 0 . 30 = - 7 . 3 . These estimated effects are larger than those reported in misspecified ARIMA models in Land et al.

It bears emphasizing, however, that substantively these estimates are very small and uncertain. The standard deviation of the residuals (reported in Table 2 ) is on the order of 15 homicides. So even the largest effect is within a standard deviation of the residual error of the model. Our argument, however, is that the effects of executions on homicides are highly uncertain and substantively meaningless. There are three reasons for this claim.

First , the p-values for the hypothesis tests for whether the coefficients for the executions variables are different from zero are barely different from the widely and erroneously accepted 5% level of significance. Given the possible model uncertainty and the restriction of the sample to 1994–2005, we should require more significant evidence, even for a one-sided hypothesis test.

Second . these marginal and total effect estimates do not fully account for the uncertainty of the parameters in the ARIMA specification. Accounting for this full uncertainty requires the computation of an impulse response and its error bands. The impulse response traces out the impacts of a one unit change in the number of executions on the dynamic path of homicides. This can be accomplished via a dynamic simulation of the ARIMA model that accounts for the model uncertainty for a single increase in the executions given that the parameter estimates equal those in Table 2 with error variances σ ^ 2 as reported in the table. This is consistent with the time series econometrics literature described in [ 29 ] and [ 30 ] who document that the best practices for tracing out the dynamic effects of a change to a covariate or a unit shock in a time series model is via a Monte Carlo simulation. Such a simulation (conducted below) accounts for the parameter uncertainty and the sample size of the model when estimating the distribution around the change in executions on the number of homicides over time.

Third , estimating the impulse response function for the effect of a marginal change in executions on the number of homicides per month will allow us to measure and report the possible asymmetry in the effects. The reason this matters is that a decision-maker’s cost function about this research is clearly not symmetric. In the decisions and inferences about deterrence, the cost of a Type I error is not likely to be the same as a Type II error. More bluntly, the cost of an execution relative to a potentially saved life is not the same. The way we can help quantify this tradeoff is to provide information about the full density around the impulse response function which traces out the dynamic impact of a single execution on the homicides series.

To address these concerns, we simulate the impulse responses. This allows us to see both the uncertainty around the effects and over what horizon the responses are observed. An impulse response (function) traces out a path of the homicides assuming one additional execution. With impulse responses one is interested in the size and uncertainty or variance around the response as well as how long it takes for the effects to be realized, based on the estimated time series model. The impulse response effectively shows the time path of the cumulative response or impact multipliers computed above. More importantly, we want to look at the shape and variance of the response. These allow us to assess the likelihood that an execution affects the number of subsequent homicides—an important first step in establishing the deterrent effect of executions.

Impulse responses are best summarized using Monte Carlo simulation since this allows one to construct an error band or standard error estimate around the dynamic path of the effect of an execution [ 29 ]. The impulse responses for the effects of executions from the last two models in Table 2 were simulated 5000 times and are summarized in Fig 3 . These responses account for both the ARIMA regression parameter uncertainty and the residual variance of the responses over 12 months . Failing to account for the residual variance uncertainty in the simulation yields identical modal impulse response estimates. But it will lead to understating the uncertainty by a by nearly a factor of 20 because it fails to model the uncertainty from the estimate of σ 2 for each model. Once this residual parameter uncertainty is included (correctly) in the Monte Carlo simulation, the resulting estimates are those in the figure. The modal estimates are presenting using the solid line while the 68% confidence region, or approximately one standard deviation confidence region of the response is plotted using dashed lines.

Responses are in solid lines with 68% or approximate one standard deviation confidence regions in dashed lines.

These impulse responses trace out the change in the number of homicides for an additional execution based on the models in the final two columns of Table 2 . As the figures show, the total estimates over twelve months match those predicted: within 12 months the first transfer function model predicts 3.34 fewer homicides (the cumulative sum over the 12 months, out of a total of 4.28 fewer) and the second transfer function model predicts 7.02 fewer homicides (the cumulative sum over 12 months, out of a total of 7.3 fewer). But these effects are not statistically or substantively meaningful because the error bands on the responses cover zero over the full time horizon. So while there is an effect, it is swamped by the residual variation in the monthly Texas homicide series. We also computed Bayesian-shape error bands that account for the serial correlation in the uncertainty of the impulse response estimates [ 29 ]. While these error bands are smaller than those in Fig 3 the same conclusions still hold.

The conclusion here is straightforward: there is no evidence in this analysis or that of [ 1 ] that can be used to offer evidence for executions having a policy-relevant, deterrent effect on homicides in Texas over the 1994–2005 period. As a preliminary conclusion, the results of [ 1 ] are highly sensitive to model specification assumptions.

Another model

Based on the estimates in Table 2 and the impulse responses in Fig 3 the estimated effects of executions do not have a deterrent effect on the number of monthly homicides in Texas. Our claim is based on the fact that once we account for the residual variation of the fitted ARIMA models, it is hard to say that the effects of executions on homicides are substantively meaningful. This is because the ARIMA model only accounts for about 58% of the observed variation in the homicides series, per Table 2 .

So one might ask, how could a better fitting time series model be constructed? Could the the effects of execution be more clearly revealed? This is mainly a call to advance inquiry beyond the bivariate analysis to the inclusion of relevant controls to reduce the risk of omitted variable bias and spurious inference for the execution variable. Such a criticism has been made recently by [ 25 ].

To evaluate this possibility, we specified additional models that included two additional covariates. The first is the (monthly) number of tens of thousand of prisoners in Texas prisons. These data on the on-hand offender population by month were obtained from the Texas Department of Criminal Justice (TDCJ) through a public information/open records request on September 3, 2010. Several studies such as [ 31 ] and [ 32 , 33 ] suggest that controlling for prison expansion in the U.S. and Texas is a necessary covariate for analyzing the drop in crimes seen across the U.S. in this period. During the study period covered by [ 1 ] (i.e., 1994 to 2005), the number of people incarcerated in Texas prisons increased dramatically from 62,322 to 151,925 or by 143 percent. A sophisticated multivariate panel analysis of Texas counties finds that most of the drop in violent and property crime in Texas during the 1990s can be attributed to increases in the number of offenders in jail and prison [ 34 ]. It is worth noting that [ 34 ] explicitly controlled for executions in both the violent and property crime specifications and found no evidence of a deterrent effect of executions on either crime type at occurring at the county-level. Of course, the results for the execution variable may have differed if homicide had been used as the dependent variable. [ 32 , 33 ] also cites prison growth as one of the leading causes of the crime decline, especially for homicide, experienced in the U.S. during the 1990s [ 31 , 35 , 36 ].

The second covariate is the first differences of the Texas unemployment rate. This monthly-level unemployment data for Texas were obtained from the Bureau of Labor Statistics website on April 18, 2012. We use differences for the unemployment data because they have a unit root. Failing to do so would lead to an unbalanced specification with a trend, or a spurious regression. Most macro-structural theories of crime contend that improving economic conditions should result in less crime, although the relationship between unemployment and crime is admittedly tenuous, at best [ 37 – 39 ]. Many of these same theories, however, predict a criminal justice system that is less punitive when the economy is improving as unemployed/marginal workers are perceived as less threatening and needed potential laborers [ 40 – 43 ]. This was clearly not the case in Texas or anywhere else in the U.S. throughout most of the 1990s and early 2000s. Indeed, in most years between 1994–2005 unemployment rates were lower than the previous year’s levels while incarceration rates and the number of persons executed were generally higher than the previous year. This means the deterrent effect identified by Land et al. may be a spurious correlation since Texas’ decision to increase the use of the death penalty coincided with an improving economy.

Again, we restrict the analysis to the critical 1994–2005 time period for comparison to the results in [ 1 ]. We use the earlier ARIMA specification and look at the effects of adding these additional covariates on the results. Table 3 presents the results with these new covariates. The effects of an additional execution are smaller here: the estimated coefficient is now −1.31, but the dynamic multiplier is - 1 . 31 1 - 0 . 14 = - 1 . 52 . So the effects of additional executions are mitigated when we control for the number of prisoners (in 10000s) and the change in unemployment. Each additional 10000 prisoners lowers the number of homicides by nearly 9, or a total effect of 10.4 fewer homicides. So changes in incarceration alone predicts a larger drop in homicides than executions. Also, the effect of changes in unemployment is not a predictor of homicides in Texas. Note also that this last model is superior to second column of results in Table 2 , since a likelihood ratio test comparing them has a value of 18.2 (p-value < 0.01). We also considered specifications using changes in the number of prisoners month-over-month and year-over-year. Neither of these rival specifications fit as well as that reported here.

The results of this section confirm a rather obvious result: there are omitted variable biases in the ARIMA specification. That is, we can easily improve the fit of the homicide time series model in Eq (4) by adding strongly suggested covariates such as the change in the number of prisoners. As in most omitted variable problems, the marginal effects of substantively significant variables changes radically when different specifications are examined. So adding a variable that a) is predicted to negatively be correlated with the homicides and b) positively correlated with executions (since more prisons mean there can be more people sentenced) will attenuate the effect of executions. The number of monthly homicides is correlated with the number of 10000 prisoners at −0.7 and the number of executions is correlated with the number of 10000s prisoners at 0.19.

Simply put, bivariate time series studies of the death penalty, no matter how statistically sophisticated, are virtually worthless for evaluating the deterrent effect of executions as they cannot rule out any alternative explanations for changes in homicide, a point recently and cogently made by [ 25 ].

Changepoint analyses

Possible causes of the sensitivity of these results are that there are time-varying or omitted structural changes in the homicides series and the other variables. These could include the sanctions regime discussed in [ 25 ]. The issue is that given the discussion in [ 1 ], we are unsure of when the sanction regime changes in Texas. One way to evaluate the possible sanctions regime effect is to employ a model that allows for structural changes in the regression parameters. The Bai and Perron (1998) [ 44 ] model is commonly used to detect such structural changes. The model assumes a regression with m structural breaks (or m + 1 regimes) with the following specification

for j = 1,…, m + 1. Here, the dependent variable time series y t is explained by a set of fixed regressors and a set whose effects change over the m + 1 regimes. The effects of regressors x t are not time-varying, while the effects of z t do depend on the changepoints or regimes. The indices for the breakpoints are T 1 ,…, T m , and define when the changepoints occur (assuming that T 0 = 0 and T m + 1 = T ). The timing of the breaks or the values of the T j are assumed unknown and need to be estimated.

For each partition of the sample into ( T 1 ,…, T m )—meaning a split of the sample into the m + 1 regimes—we minimize the sum of squared errors,

The estimator then selects the optimal split of the sample into the estimated breakpoints ( T ^ 1 , … , T ^ m ) that minimizes the sum of squared errors. The optimal number of breakpoints is selected based the minimum the Bayesian Information Criteria (BIC) that accounts for fit, but penalizes using too many parameter or regimes. We examine dynamic regression model specifications with zero to five breaks and report those that minimize the BIC for a given specification of regressors.

We begin with an admittedly underspecified breakpoint analysis of the full monthly homicides series, covering from 1980(1)–2009(8). For this series, regressed only on a constant (so this is a simple changing means model), the optimal number of breakpoints is three, at 1984(12), 1990(4), and 1994(11) (results not reported). One problem with this initial specification is that it does not capture the autoregressive or seasonal components of the homicides series. Note the Land et al. sample comes after each of these breakpoints. But this sets a simple upper bound on the number. Including covariates for the dynamics of the homicides, the number of prisoners, the number of executions, etc. should explain some of these breakpoints or change the dates of their estimated locations.

Table 4 presents the dynamic regression model with one break. This two regime model optimally splits the sample at 1994(10) and accounts for the autoregressive dynamics of the homicides series (the residuals for this model are white noise). Note that the intercept drops in the latter period (after 1994) when the number of homicides falls. Also, the AR(1) process weakens (the coefficient drops from 0.42 in the first regime to 0.20 in the second regime).

First entry is the coefficient, second in parantheses are the standard errors, followed by the two-sided p-values.

Next, we include the covariates in the breakpoint model. We include here the year-over-year percentage change in prisoners (since the levels of prisoners are possibly non-stationary) and the number of executions at time t and t − 1, as before. We also looked at models with only the change in prisoners or only the executions, which produce similar results. We only have the prisoners’ data from 1985(3), so the sample for the change specification (year-over-year) begins in 1986(3). The optimal breakpoint model with this specification is reported in Table 5

First entry is the coefficient, second in parentheses are the standard errors, followed by the two-sided p-values.

The optimal number of breakpoints for this specification is one and it splits the sample at 1992(1)—prior to the date of Land et al.’s sample. The dynamics of the homicides series (seen in the AR coefficients) are remarkably similar before and after the breakpoint. Note that the effects of the main covariates are contrary to expectations: executions increase the number of homicides prior to 1992 and are not statistically significant predictors after 1992. This further supports that argument that the specification decisions and lack of structural identification make it difficult, if not impossible to make clear claims about the relationships between executions and homicides in Texas over this sample period.

Our conclusions about the effect of executions on homicides in Texas from 1994–2005, as reported in [ 1 ] are very sensitive to model specification decisions and samples.

First, changing from a model of first and seasonal differences to one with a pure autoregressive specification leads to one that is more parsimonious and better explains the dynamics of the Texas monthly homicides over this stretch. This is critical, since without a correct dynamic specification of the basic ARIMA process, the subsequent analysis of the effects of executions on homicides will be incorrect.

Second, using the improved time series specifications, we see that the results about the effects of executions are very sensitive to model specification decisions vis-a-vis those made in [ 1 ]. When we change the dynamic specification we get an estimated effect that is larger than that reported previously, but also more uncertain. Using a standard interpretative method to assess the dynamic effects, an impulse response function with error bands to ascertain the full uncertainty of the model and its estimates, indicates that the effects of executions on homicides are no different than random chance. So while the impact multiplier is negative and weak evidence for the executions deterring homicides in Texas, the results are not unambiguous.

Third, the results are called into question when we add two highly plausible covariates to the analysis: changes in unemployment rates and the number of prisoners in Texas prisons. Adding these covariates to the analysis shows that the incarceration of more criminals predicts a larger drop in the number of Texas homicides than the effect predicted by an execution. But even with these additional controls, the large residual variance swamps the estimated dynamic effects. So even for this updated model, there is little to no evidence that strongly suggests that either incarceration or execution changes substantively the number of homicides in Texas between 1994 and 2005.

Fourth, the changepoint model results in the previous section show that the critical changes come prior to 1994 and in fact do not work as Land et al. suggested. Rather we see that the effects for executions are weak and not significant by any conventional expectation. Further, the lack of identification makes determining the effects of these deterrence and incapacitation variables quite hard.

What substantive conclusions should be drawn from these results? The initial conclusion should be that research purporting to find effects for executions on homicides are likely suspect because of issues involving model underspecification and interpretation. One typically likes to interpret the marginal impacts of single covariates, but this alone does not account for all of the full dynamic effects or the potential model misspecification. Using the impulse analysis methodology employed here does a much better job of this.

Further there is no good identification strategy for disentangling the possible deterrent effects of executions on homicides [ 25 ]. At best, what is presented here suggests that there might be some weak correlations. At worst, it shows that the inclusion of different covariates or model specifications lead to dramatically different and highly sensitive results. This is not a good basis for making policy projections or decisions since there is no evidence that the effects of the covariates like executions on the homicides variable are robust to different specification or identification assumptions. While one can continue to suggest new or additional covariates, the limited degrees of freedom for the homicides series means that there need to be more theoretical work ahead that can define better data analysis strategies.

Finally, we caution that these results are based on what can be described as good, but not exhaustive time series data analytic techniques. Employing ARIMA and changepoint models we are making explicit assumptions of the endogeneity of the homicides series and the exogeneity of the other covariates (the input series). This is typically hard to defend in many social science applications [ 45 ]. So future empirical examinations should employ methods that look at the possible dynamic, endogenous nature of the aggregate homicides time series with other predictors, rather than making indefensible strict exogeneity assumptions.

Supporting Information

S1 replication archive, acknowledgments.

Brandt’s research has been funded in part by NSF grant SES-0921051. The authors are responsible for any errors. Replication data and code in R are available as part of the Supplemental Replication zip archive S1 Replication Archive files for this paper.

Funding Statement

The authors have no support or funding to report.

Data Availability

Finding Sources for Death Penalty Research

Barry Winiker/photolibrary/Getty Images

• Writing Research Papers
• Writing Essays
• English Grammar
• M.Ed., Education Administration, University of Georgia
• B.A., History, Armstrong State University

One of the most popular topics for an argument essay is the death penalty . When researching a topic for an argumentative essay , accuracy is important, which means the quality of your sources is important.

If you're writing a paper about the death penalty, you can start with this list of sources, which provide arguments for all sides of the topic.

Amnesty International Site

Amnesty International views the death penalty as "the ultimate, irreversible denial of human rights." This website provides a gold mine of statistics and the latest breaking news on the subject.

Mental Illness on Death Row

Death Penalty Focus is an organization that aims to bring about the abolition of capital punishment and is a great resource for information. You will find evidence that many of the people executed over the past decades are affected by a form of mental illness or disability.

Pros and Cons of the Death Penalty

This extensive article provides an overview of arguments for and against the death penalty and offers a history of notable events that have shaped the discourse for activists and proponents.

Pro-Death Penalty Links

This page comes from ProDeathPenalty and contains a state-by-state guide to capital punishment resources. You'll also find a list of papers written by students on topics related to capital punishment. 

• Capital Punishment: Pros and Cons of the Death Penalty
• Pros & Cons of the Death Penalty
• 5 Arguments in Favor of the Death Penalty
• Ethos, Logos, Pathos for Persuasion
• History of Capital Punishment in Canada
• Preparing an Argument Essay: Exploring Both Sides of an Issue
• Recent Legal History of the Death Penalty in America
• Tips on How to Write an Argumentative Essay
• The Death Penalty in the United States
• New Challenges to the Death Penalty
• 50 Argumentative Essay Topics
• Furman v. Georgia: Supreme Court Case, Arguments, Impact
• "The Penalty of Death" by H.L. Mencken
• The Best Interactive Debate Websites for Students and Teachers
• Coker v. Georgia: Supreme Court Case, Arguments, Impact
• Controversial Speech Topics

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Investigations

States botched more executions of black prisoners. experts think they know why.

Chiara Eisner

A lethal injection gurney is seen at the at Nevada State Prison, a former penitentiary in Carson City, Nev., in 2022. Emily Najera for NPR hide caption

A lethal injection gurney is seen at the at Nevada State Prison, a former penitentiary in Carson City, Nev., in 2022.

Studies of the death penalty have long shown racial inequality in its application, but a new report has found the disparity extends inside the death chamber itself. In an analysis of the more than 1,400 lethal injection executions conducted in the U.S. since 1982, researchers for the nonprofit Reprieve reported that states made significantly more mistakes during the executions of Black people than they did with prisoners of other races.

Reprieve, which advocates against the death penalty, found that nationwide, half of the botched lethal injection executions were of Black people, though only a third of the prisoners executed were Black. The pattern was starkest in some Southern states. In Arkansas, Oklahoma and Georgia, three quarters or more of the botched lethal injection executions were of Black people, though they accounted only for a third or less of executions in those states.

Lethal injection requires execution workers to administer drugs intravenously to the prisoner to stop their heart. It has become the most commonly used execution method across the country, though it is also the method with the most recorded "botches," or mistakes.

There is no standard definition of what constitutes a botched execution. For its analysis, Reprieve designated an execution as botched if it met certain criteria. Researchers checked documents and witness reports to confirm details like whether there was evidence that a prisoner made visible or audible expressions of pain, was still conscious after a drug was administered, or whether execution workers had struggled at length to find a prisoner's veins.

Joe Nathan James Jr. was executed on July 28, 2022, by lethal injection at an Alabama prison for the 1994 shooting death of his former girlfriend. His execution lasted for at least three hours, and was widely considered botched. Alabama Department of Corrections via AP hide caption

Joe Nathan James Jr. was executed on July 28, 2022, by lethal injection at an Alabama prison for the 1994 shooting death of his former girlfriend. His execution lasted for at least three hours, and was widely considered botched.

That happened in 2022, when execution workers in Alabama spent three hours attempting to insert an IV line into the veins of Joe Nathan James, Jr., a Black man. His autopsy showed puncture marks and cuts in his feet, hands, wrists and arms.. A few months later, Alabama left white prisoner Kenneth Smith alive on the gurney for hours after they struggled to find a vein to use for his lethal injection execution, prompting his lawyers to ask the state to use nitrogen gas to execute him in January.

Lengthy procedures like those were not uncommon, the Reprieve analysis found. Over one third of lethal injections lasted more than 45 minutes and over a quarter took an hour or more.

The executions typically involved a three-drug regimen, though the protocol can vary. Some states have injected just one drug and others up to four. With the three-drug method, the first drug used is an anesthetic, to numb the prisoner. The second drug paralyzes the muscles, and the third stops the heart. Most executions were conducted with sodium thiopental as the anesthetic. Other states used drugs like pentobarbital, midazolam, etomidate or fentanyl in their regimens. None of the drugs have been FDA-approved for this application, and pharmaceutical companies have widely opposed their use in executions .

Still, the Reprieve analysis found that no specific drug led states to make more mistakes.

"There are botched executions, many of them, regardless of the drug, regardless of the cocktail," said Maya Foa, the executive director of Reprieve. "Continuing to tinker with the machinery of death is not making this better."

Gasping For Air: Autopsies Reveal Troubling Effects Of Lethal Injection

Reprieve determined that 73 lethal injection executions were botched; just over 5% of those conducted since 1982. The total may be conservative. Previous research has identified that the percentage of botched lethal injection executions using the same criteria could be higher than 7%, though that study did not examine the race of the prisoners, as Reprieve's did.

"The analysis shows not only are we botching these executions and causing people torture more often than with many other methods," said Foa, "But we are doing that to Black prisoners far, far more frequently than we are to white prisoners."

A gurney lies in Alabama's lethal injection chamber at Holman Correctional Facility in Atmore, Ala. Dave Martin/AP hide caption

A gurney lies in Alabama's lethal injection chamber at Holman Correctional Facility in Atmore, Ala.

Studies of the death penalty have previously shown racial discrimination is prevalent throughout many steps of administering capital punishment – from jury selection to the sentencing and appeals process. A 2020 report from the nonprofit Death Penalty Information Center showed that people of color have been overrepresented on death rows in the U.S., and that killers of Black people were less likely to face the death penalty than those who kill white people.

But the Reprieve analysis is one of the first times that empirical evidence has indicated that racism extends even to the final step of the death penalty: the execution itself. While the study does not explain how or why states make more mistakes when executing Black prisoners, Foa said she thinks that the fact that Black people suffer from higher mortality rates and receive poorer medical treatment in the U.S should provide clues.

Ruqaiijah Yearby, a professor of health law at The Ohio State University who studies racism in healthcare, agreed. She said that racist tropes that can limit Black people from accessing equitable medical care, like the false notion that Black people have a higher tolerance for pain, could also be involved in the administration of drugs in the death chamber. Yearby cited research that showed that nationwide, Black cancer patients received lower doses of pain medication than cancer patients who were white.

"Black people don't have thicker skin, we don't have bigger bones," Yearby said. "But if you believe that, then you're going to treat somebody differently than if you're going to do it to a white person."

Dr. Scott Bowman, a professor of criminal justice at Texas State University whose academic work has focused on race and law enforcement, said he would expect that sort of discrimination to show up in lethal injection executions in subtle ways.

"You can't find a vein and you think, well, it really is hard to find veins in Black people, so I'm just going to keep sticking," he said.

Researchers would find it difficult to identify those kinds of interactions in the death chamber, partly because they could be subtle, and because the criminal justice system lacks transparency when those in power make mistakes, he said. But insiders could know more.

Carrying out executions took a secret toll on workers — then changed their politics

NPR interviewed four workers, none of whom were Black, who collectively witnessed or helped carry out 26 executions across the country.

Craig Baxley, a former executioner from South Carolina who pushed lethal injection drugs into prisoners' veins, said he "never noticed anything as far as treatment, or how anybody reacted to whether they were white or Black."

Jeanne Woodford, a former warden of the state prison in San Quentin, Calif., who oversaw four executions during her tenure, said something similar.

"I didn't see any difference at all," she remembered.

Woodford was aware, though, of some execution workers who may have believed people of color might have been more difficult to inject with the lethal injection drugs, a common misconception.

"I heard some guys say, 'Oh these guys are really muscular, it's going to be harder," she said. An execution worker in Nevada suggested that "maybe the nervous system of the Black inmate works different."

Spiritual advisor Jeff Hood is seen after Kenneth Smith was executed in 2024 in Atmore, Ala. Smith, a white man, was executed by gas after Ala. botched his lethal injection in 2022. Gabrielle Caplan for NPR hide caption

Spiritual advisor Jeff Hood is seen after Kenneth Smith was executed in 2024 in Atmore, Ala. Smith, a white man, was executed by gas after Ala. botched his lethal injection in 2022.

But Jeff Hood, a spiritual advisor who has been inside the death chamber during three executions of Black people and three white prisoners in Oklahoma, Texas and Alabama , said he did witness differences in the treatment of Black prisoners while they were strapped to the gurney.

"I can definitely tell you that the restraints that I have seen on Black folk have been unquestionably tighter than the restraints that I have seen on white folk," Hood said.

He believes that was related to the correctional officers' fear and prejudice of Black people, something Hood says is common where he lives in Arkansas. The only times Hood said he heard execution workers discussing whether a prisoner would resist was when the person scheduled to be executed was a person of color. This may have had an impact on how Black prisoners were treated as they were put to death, he thinks.

Kenneth Smith could be the first person executed with nitrogen gas. He spoke with NPR

"If your assumption is that the person who is condemned is going to resist, then you are going to take much more liberties with the body than if you believe that the person was going to be perfectly peaceful," the spiritual advisor said. "And when you begin to take liberties with someone's body, you leave protocol and you leave best practices. When you leave protocol and you leave best practices, of course you are going to have a botched execution."

The authors of the Reprieve report recommended imposing a moratorium on lethal injection executions conducted at the state and federal levels, noting that there were fundamental legal, constitutional and ethical issues with the method. They called on governors of states where executions are allowed to commission investigations to better understand the issues, as well as repeal secrecy laws that may have prevented previous oversight.

"The death penalty in its application in the United States is racist," said Foa, Reprieve's executive director. "And we cannot continue to do this."

Nicholas McMillan and Jonathan Franklin contributed reporting.

Death Penalty: APA7 Citation Help

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Citing Different Sources in APA7

• AI (Artificial Intelligence like ChatGPT)
• Book, One Author
• Book, Two Authors
• Book, ebook online
• Brochure, print or online
• Chapter in an Edited Book
• Children’s book with illustrator same as author
• Children’s book with illustrator different than author
• Children’s book, part of a series
• Class Readings from Questia/Cengage
• Course Syllabus/Assignment from Instructor
• Court Cases
• Diagnostic Manual (DSM)
• Dictionary, Encyclopedia, Reference Book
• Dissertations from Library
• Facebook Post
• Facebook Page
• Ferguson's Career Guidance Center article
• Ferguson's Career Guidance Center Video
• Film or Video
• Image from Web
• Government webpage
• Instagram Photo or Video
• Instagram Highlight
• Interview or Personal Communication
• Music Album
• Music - Single song or track
• Newspaper, magazine, or journal article without a DOI
• Online Forum Post (ie Reddit)
• Online Magazine, with an Author
• Podcast episode
• Powerpoint Slides or lecture notes
• Religious Work
• Radio interview recording in a digital archive
• Scholarly Journal Article - One author
• Scholarly article with article number
• Scholarly Journal Article (two authors) with DOI
• Scholarly Journal Article (Multiple authors)
• Speech from a website
• Speech from YouTube
• Textbook with an edition
• Textbook (eTextbook)
• Textbook, Online with Editors
• TV Series Episode
• Twitter Profile
• Webpage, no date
• Webpage on a News Website
• Webpage with an Author and Date
• Webpage on a website with a group author
• Website, Page or Article, No Author
• Website, press release
• YouTube or other Streaming Video

OpenAI. (2023). Gun violence in North Carolina  (June 5 version) [Large language model].  https://chat.openai.com/chat

The in-text citation would be

 (OpenAI, 2023).

*OpenAI is the author of the ChatGPT model.

Klymkowsky, M. (2018, September 15). Can we talk scientifically about free will? Sci-Ed. https://blogs.plos.org/scied/2018/09/15/can-we-talk-scientifically-about-free-will/

(Klymbkowsky, 2018) is the in-text citation

For direct quotes, see the information below for quoting items without page numbers.

Gleick, J. (1987). Chaos: Making a new science . Penguin. 

(Gleick, 1987) is the in-text citation

An ebook would have the URL at the end of the citation.

Klaas, B. (2024).  Fluke : Chance, chaos, and why everything we do matters . Scribner. https://ebookcentral.proquest.com/lib/rowancabarrus/detail.action?docID=30676412

Gale, P., & Lerner, N. (2000). The Bacon guide to peer tutoring . Allyn & Bacon. 

(Gale & Lerner, 2000) is the in-text citation

Clarke, A. (2019).  Harper's practical genetic counselling, eighth edition . Taylor & Francis Group.  https://ebookcentral.proquest.com/lib/rowancabarrus/detail.action?docID=5906192#

UNCG. (2021).  Information for potential UNCG students  [Brochure] . 

In-text is (UNCG, 2021)

If an online brochure

UNCG. (n.d.) Visitor brochure [Brochure].  https://admissions.uncg.edu/visitor-brochure/

Include title and [Brochure] in the title. This brochure has no date. Do not use the webpage's copyright date.

In-text is (UNCG, n.d.)

Harris, M. (2000). Talk to me: Engaging reluctant writers. In B. Raforth (Ed.), A tutor's guide: Helping writers (pp. 24-34). Heinemann. 

Here's how it looks for an ebook 

Melson, G. & Fine, A. (2019). Animals in the lives of children. In A. Fine (Ed.), Handbook on animal-assisted therapy : Theoretical foundations and guidelines for practice  (pp. 223-245). Elsevier Science and Technology. https://ebookcentral.proquest.com/lib/rowancabarrus/reader.action?docID=629941&ppg=259

(Harris, 2000)  is the in-text citation

Beaton, K. (2016).  King baby  (K. Beaton, Illus.). Arthur A. Levine Books.

(Beaton, 2016)

Crimi, C. (2019).  Weird little robots  (C. Luyken, Illus.). Candlewick Press.

(Crimi, 2019)

O’Connor, J. (2017).  Nancy Clancy, late-breaking news!  (R. Preiss Glasser, Illus.). HarperCollins Publishers.

• Parenthetical citation : (O’Connor, 2017)

Dickinson, E. (1950). Because I could not stop for death. In F.O. Matthiessen (Ed.),  The Oxford Book of American Verse  (p. 439). Oxford.

In-text citation would be (Dickinson, 1950)

Johnson, L. (2020). Course syllabus English 112 . Rowan-Cabarrus Community College. URL.

Bittle, J. (2023).  English 112  [Syllabus]. Rowan-Cabarrus Community College. URL

In-text Citation (Johnson, 2020) or (Bittle, 2023)

*The URL is the www.rccc.edu/blackboard....... webpage address.

Brown v. Board of Education, 347 U.S. 483 (1954).  https://supreme.justia.com/cases/federal/us/347/483/

Parenthetical: ( Brown v. Board of Education,  1954)

Narrative:  Brown v. Board of Education  (1954)

From page 359 of APA Manual 7th Edition.

US Supreme Court case, with a page number (through 2012 Supreme Court term)

U.S. is short for  United States Reports.

Published in Volume 347 on page 483 in the year 1954.

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). https://doi.org/10.1176/appi/books.9780890424496

(American Psychiatric Association, 2013)- In-text Citation

For a source with an editor.

Venes, D. (Ed.). (2021).  Taber's cyclopedic medical dictionary  (24 th  ed.). F. A. Davis Company. 

If there's a URL, then add it after the publisher. 

Intext is (Venes, 2021) 

______________________________

If you are referring only to an article in a reference book that has a different author, then do this.

Graham, G. (2019). Behaviorism. In E. N. Zalta (Ed.),  The Stanford encyclopedia of philosophy  (Summer 2019 ed.). Stanford University. https://plato.stanford.edu/archives/sum2019/entries/behaviorism/

Intext is (Graham, 2019) or (Graham, 2019, p. 81) for a direct quote.

___________________________

If you are looking at an entry in a dictionary or encyclopedia that has a group author, then do this.

American Psychology Association. (n.d.) Positive transference. In APA dictionary of psychology. Retrieved August 31, 2019, from https://dictionary.apa.org/positive-transference 

*Note: There is an (n.d.)  in the publishing date because this source is online and consistently updated. Therefore a retrieval date is preferred so your instructor can tell when you read the information.

Intext is (American Psychology Association, n.d.)

____________________________

Paulson, J. A. (2016).  Neuropsychological functioning and inflammation in past and current PTSD  (Order No. 3706233) [Doctoral dissertation, Alliant International University]. ProQuest Central. http://proxy154.nclive.org/login?url=https://www.proquest.com/dissertations-theses/neuropsychological-functioning-inflammation-past/docview/1694579970/se-2?accountid=13601

Intext citation will be 

(Paulson, 2016)

Cuellar, N. G. (2016). Study abroad programs [Editorial]. Journal of Transcultural Nursing ,  27 (3), 209. https://doi.org/10.1177/1043659616638722

(Cuellar, 2016) In-text Citation

National Institute of Mental Health. (2018, November 28). Suicide affects all ages, genders, races, and ethnicities. Check out these 5 action steps for helping someone in emotional pain [Infographic]. Facebook. https://bit.ly/321 Qstq

(National Institute of Mental Health, 2018) In-text Citation

Smithsonian's National Zoo and Conservation Biology Institute. (n.d.). Home [Facebook page]. Facebook. Retrieved July 22, 2019, from https://www.facebook.com/nationalzoo

(Smithsonian's National Zoo and Conservation Biology Institute, n.d.) In-text Citation

Oncological nurses. (n.d.). Ferguson's Career Guidance Center. Retrieved November 16, 2023 from https://fcg.infobase.com/recordurl/1302021?aid=99147

In-text (Oncological nurses, n.d.)

Cambridge Educational. (2018).  Animal trainer-career Q&A: Professional advice and insight. Ferguson's Career Guidance.  https://fcg-infobase-com.proxy154.nclive.org/video/151048

Forman, M. (Director). (1975). One flew over the cuckoo's nest [Film]. United Artists.

(Forman, 1975) In-text Citation

Goya, F. (1800). The family of Charles IV. Museo National del Prado . https://www.netmuseum.org/ /hd_goya

Office of Women's Health. (2019, March 14).  Breastfeeding.  U.S. Department of Health and Human Services.  https://www.womenshealth.gov/breastfeeding

 In-text Citation is (Office of Women's Health, 2019). 

*If author and publisher are the same, you do not need to repeat name after the title of webpage. This reference/citation shows Office of Women's Health as the group author and the U.S. Department of Health and Human Services as the publisher. 

Centers for Disease Control and Prevention. (2018, January 23). People at high risk of developing flu-related complications . https://www.cdc.gov/flu/about/disease/high_risk.htm

Intext is (Centers for Disease Control and Prevention, 2018)

According to the Centers for Disease Control and Prevention (2018)

If you want to abbreviate you must connect the entire name of organization with the abbreviation on the first intext and then abbreviate after that.

First instance is: (Centers for Disease Control and Prevention [CDD], 2018). Next intext would be (CDC, 2018)

First instance is: According to the Centers for Disease Control and Prevention (CDC, 2018). Next instance, (CDC, 2018).

Zeitz MOCAA [@zeitzmocaa]. (2018, November 26). Grade 6 learners from Parkfields Primary School in Hanover Park visited the museum for a a tour and workshop hosted by [Photographs]. Instagram. https://www.instagram.com/p/BqpHpjFBs3b/

(Zeitz MOCAA, 2018) In-text Citation

The New York Public Library [@nypl]. 9n.d.) The raven [Highlight]. Instagram. Retrieved April 16, 2019, from https://bitly.com/2FV8bu3

(The New York Public Library, n.d.) In-text Citation

Interviews or other personal communication (emails, letters, phone calls or messages that aren't able to be recovered or found) are NOT included in the reference list.

These personal communications are in-text cited as 

(E. Robbins, personal communication, September 29, 2020)

Bowie, D. (2016). Blackstar  [Album]. Columbia. 

(Bowie, 2016) In-text Citation

Childish Gambino. (2018). This is America [Song]. mcDJ; RCA.

(Childish Gambino, 2018) In-text Citation

Magryta, C. (2015, August 30). Top ten reasons to breastfeed your infant. The Salisbury Post . https://www.salisburypost.com/2015/08/30/8-30-15-lifestyle-dr-magryta-column/

National Aeronautics and Space Administration [nasa]. (2018, September 12). I'm NASA astronaut Scott Tingle . [Online forum post]. Reddit. https://www.reddit.com/r/IAmA/comments/9fagqy/im_nasa_astronaut_scott_tingle_ask_me_anything/

(National Aeronautics and Space Administration, 2018) In-text Citation

Nemko, M. (2020, February 6). The three principles of good parenting . Psychology Today .  https://www.psychologytoday.com/us/blog/how-do-life/202002/the-three-principles-good-parenting

(Nemko, 2020) In-text Citation

McCurry, S. (1985). Afghan girl [Photograph]. National Geographic. https://www.nationalgeographic.com/magazine/national-geographic-magazine-50-years-of-covers/#/ngm-1985-jun-714.jpg

(McCurry, 1985) In-text Citation

Vedantam, S. (Host). (2015-present). Hidden brain [Audio podcast]. NPR. https://www.npr.org/series/423302056/hidden-brain

(Vendantam, 2015-present) In-text Citation

Glass, I. (Host). (2011, August 12). Amusement park (No. 443) [Audio podcast episode]. In This American life . WBEZ Chicago. https://www.thisamericanlife.org/radio-archives/episode/443/amusement-park

(Glass, 2011)  In-text Citation

Here is for a Powerpoint or slideshow with an instructor's name .

Dasher, R. (2024). Module 6 - adulthood  [Powerpoint slides]. Psychology Department, Rowan-Cabarrus Community College. https://rccc.blackboard.com

(Dasher, 2024). In-text Citation

King James Bible. (2017). King James Bible Online. https://www.kingjamesbibleonline.org/ (Original work published 1769)

(King James Bible, 1769/2017)  In-text Citation

de Beauvoir, S., (1960, May 4). Simone de Beauvoir discusses the art of writing  [Interview]. Studs Terkel Radio Archive; The Chicago History Museum. https://studsterkel.wfmt.com/programs/simone-de-beauvoir-discusses-art-writing

(de Beauvoir, 1960) In-text Citation

Brewer, T. (2002). Test-taking anxiety among nursing & general college students. Journal of Psychosocial Nursing,   40 (11), 22-29. https://proxy154.nclive.org/login?url=https://search.proquest.com/docview/220140572?accountid=13601

(Brewer, 2002)  In-text Citation

According to Brewer (2002), 

Burin, D., Kilteni, K., Rabuffetti, M., Slater, M., & Pia, L. (2019). Body ownership increases the interference between observed and executed movements. PLOS ONE , 14 (1), Article e0209899. http://doi.org/10.1371/journal.pone.0209899

Intext citation (Burin et al., 2019)

Narrative citation Burin et al. (2019) 

Direct quote (Burin et al., 2019, Introduction) 

McCauley, S. M., & Christiansen, M. H. (2019). Language learning as language use: A cross-linguistic model of child language development. Psychological Review , 126 (1), 1-51. https://doi.org/10.1037/rev0000126

(McCauley & Christiansen, 2019) In-text Citation

For more information about DOIs, see the section in the box on this page about APA Citation Guides and Templates.

Pauwels, S., Symons, L., Eva-Lynn Vanautgaerden, Ghosh, M., Duca, R. C., Bekaert, B., Freson, K., Huybrechts, I., Langie, S., Koppen, G., Devlieger, R., & Godderis, L. (2019). The influence of the duration of breastfeeding on the infant’s metabolic epigenome.  Nutrients,  11 (6), 1408. https://dx.doi.org/10.3390/nu11061408

(Pauwels et al., 2019) for in-text citation when authors' names are not mentioned in sentence (Parenthetical citation)

Pauwels et al. (2019) when author is mentioned in the text (Narrative citation)

APA7 requires that you list up to 20 authors in the reference list 

King, M. L., Jr. (1963, August 28).  I have a dream  [Speech audio recording]. American Rhetoric. https://americarhetoric.com/speeches/mlkihaveadream.htm

npatou. (2016, June 6).  Malcolm X's legendary speech: "The bullet or the ballot"  [Video]. YouTube.  https://www.youtube.com/watch?v=8zLQLUpNGsc

Giertz, S. (2018, April). Why you should make useless things [Video]. TED Conferences. https://www.ted.com/why_you_should_make_useless_things

In-text Citation Format:

• Parenthetical citation : (Giertz, 2018)
• Narrative citation : Giertz (2018)

Berger, K. S. (2022).  Invitation to the lifespan  (5th ed). Worth Publishers. 

Intext is (Berger, 2022). 

Losco, J. & Baker, R. (2018). Am gov 2017-2018 . McGraw-Hill Education.

In-text citation (Losco & Baker, 2018)

Betts, G., Young, K. A., Wise, J. A., Johnson, E., Poe, B., Kruse, D. H., Korol, O., Johnson, J. E., Womble, M., & DeSaix, P. (Eds.). (2022, September 19).  Anatomy and physiology 2e.  OpenStax.  https://openstax.org/details/books/anatomy-and-physiology-2e

Intext 

(Betts et al, 2022)

*You can list up to 20 authors or editors in an APA citation.

Barris, K. (Writer & Director). (2017, January 11). Lemons (Season 3, Episode 12) [TV series episode]. In K. Barris, J. Groff, A. Anderson, E. B. Dobbins, L. Fishburne, & H. Sugland (Executive producers), Black-ish. Wilmore Films; Artists First; Cinema Gypsy Productions; ABC Studios. 

(Barris, 2017) In-text Citation

Simon, D., Colesberry, R. F., & Kostroff Noble, N. (Executive Producers). (2002-2008). The wire [TV series]. Blown Deadline Productions; HBO. 

(Simon et al., 2002-2008) In-text Citation

Badlands National Park [@BadlandsNPS]. (2018, February 26). Biologists have identified more than 400 different plant species growing in @BadlandsNPS #DYK #biodiversity [Tweet]. Twitter. https://twitter.com/BadlandsNPS/status/968196500412133379

(Badlands National Park, 2018) In-text Citation

APA Style [@APA_Style]. (n.d.). Tweets [Twitter profile]. Twitter. Retrieved November 1, 2019, from https://twitter.com/APA_Style

(APA Style, n.d.) In-text Citation

Rowan-Cabarrus Community College. (n.d.). Student wellness center.  https://www.rccc.edu/wellness/ .

In-text is (Rowan-Cabarrus Community College, n.d.)

If the information changes frequently or regularly, a retrieval date is recommended.

U.S. Census Bureau. (n.d.). U.S. and world population clock . U.S. Department of Commerce. Retrieved June 4, 2020, from https://www.census.gov/popclock/

In-text Citation is (U.S. Census Bureau, n.d.)

Miller, C. (n.d.). Does social media cause depression?  Child Mind Institute.  https://childmind.org/article/is-social-media-use-causing-depression/

In-text  Citation is (Miller, n.d.)

or   According to Miller (n.d.), 

Avramova, N. (2019, January 3). The secret to a long, happy life? Think age-positive . CNN. https://www.cnn.com/2019/01/03/22/health/memory-forgetting-psychology.html

In-text citation is

(Avramova, 2019)

Livio, M. (2020, May 19). When Galileo stood trial for defending science. A&E Television Networks.  https://www.history.com/news/galileo-copernicus-earth-sun-heresy-church

In-text citation would be (Livio, 2020)

World Health Organization. (2018, March). Questions and answers on immunizations and vaccine safety . https://who.int.features.qu/84/en

(World Health Organization, 2018) is the in-text.

(World Health Organization [WHO], 2018) if you want to abbreviate in subsequent in-text citations. The whole name must be written out and the abbreviation indicated in the first in-text reference.

Boston Tea Party . (2019, December 12). A&E Television Networks. https://www.history.com/topics/american-revolution/boston-tea-party

( Boston Tea Party , 2019) In-text Citation

*Italicize title if italicized in reference list. (APA, 2020, p. 265).

American Psychological Association. (2020, November 17).  Psychologists report large increase in demand for anxiety, depression treatment  [Press release]. https://www.apa.org/news/press/releases/2020/11/anxiety-depression-treatment

Intext citation (American Psychological Association, 2020)

University of Oxford. (2018, December 6). How do geckos walk on water? [Video]. YouTube. https://www.youtube.com/watch?v=qm1xGfOZJc8

In-text Citation-  (University of Oxford, 2018)

APA Tutorial

Apa tutorial  , apa7 sample papers.

• APA7 Career Paper ENG111 Student Use Dec2020
• APA7/MLA Annotated Bib Example for ENG112 (updated January 2020) This annotated bib has an MLA header and assignment information and APA7 citations. This meets the criteria for ENG112 and Issues in the Occupation or Discourse Community. Only Level 1 Headings are used in this paper.
• APA7/MLA Annotated Bib ENG112, title and headings (February 2020) This is another option for the ENG112 annotated bib with a title and headings. It has an MLA header and assignment information and APA7 citations. Level 1 and Level 2 headings are used in this paper. This meets the criteria for ENG112 and Issues in the Occupation or Discourse Community.

When Parts of a Citation are Missing

Table 9.1 on page 284 in  Publication Manual of the American Psychological Association Seventh Edition.

Direct Quotation of Material Without Page Numbers

If you use direct quotes, you must include some way for the reader to find the information. In a source with page numbers, you include the page number in the in-text citation (Robb, 2021, p. 88). 

Many sources from the web don't have page numbers. You should choose the approach that will best help your reader.

• Provide a heading or section name: 

For people who know that they have a serious allergic reaction to some substance, it is recommended they wear a medical alert bracelet to let  "others know that you have a serious allergy in case you have a reaction and you're unable to communicate" (Mayo Clinic, 2021, Prevention section). 

This is the reference for the end of your paper

Mayo Clinic. (2021). Allergies.  https://www.mayoclinic.org/diseases-conditions/allergies/symptoms-causes/syc-20351497

• Provide a paragraph number:

According to an article on NPR, after the Covid-19 pandemic, many people are "rethinking what work means to them, how they are valued, and how they spend their time" (Hsu, 2021, para 4).

Hsu, A. (2021, June 24). As the pandemic recedes, millions of workers are saying 'I quit.' NPR.  https://www.npr.org/2021/06/24/1007914455/as-the-pandemic-recedes-millions-of-workers-are-saying-i-quit 

• Provide a time stamp.

The destruction is even more important when one realizes that "around a third of all Australian fish spend at least some time on the Great Barrier Reef" (Films Media Group, 2018, 7:11). 

Here is the reference to the film

Films Media Group. (2018).  Can we save the reef?  https://fod.infobase.com/PortalPlaylists.aspx?wID=99147&xtid=186717

Many of the resources in the Library have a PDF option. This is really useful when your document has charts, pictures or formatting that doesn't translate well into html. To get to the PDF, look for 

When you double-click the pdf to open it, check the header or footer for the page number. Sometimes the page number is in the sidebar as well.

This is the page number you use for a direct quote in the in-text citation. 

Intext Citing Specific Parts of a Source

APA uses the (Author, Date) system UNLESS you need to cite a specific part of a source (for example, direct quotes or statistics). Here are some examples:

(Robb, 2019, p. 35)

(Shores, 2021, para. 3)

(Anderson, 2020, paras. 2-3)

(Ervin & Ervin, 2019, Table 5)

(Folck Institute for Archival Studies, 2017, 45:14) - This is a video or sound recording

(Deese, 2020, Slide 8)  - Powerpoint or Slideshare type source

( King James Bible , 1769/2017, 1 Cor. 13:1)

(Aristotle, ca 350 B.C.E./1994, Part IV)

(Shakespeare, 1623/1995, 1.3.36-37)

For more information, see page 264 of the   Publication Manual of the American Psychological Association , 7 th  ed.

Personal Interviews in APA

Reminder: Personal Interviews do NOT go into a reference list in APA style. 

Interviews should be cited like this for intext citations:

(E. Robbins, personal communication, January 4, 2021)

• Students must be able to complete four classes of calculus to take 300-level engineering classes at NCSU (J. Smith, personal communication, August 15, 2018).
• According to J. Smith, an instructor at NCSU, students must complete four classes of calculus to take 300-level engineering classes (personal communication, August 15, 2018).
• Purdue Owl APA Page

Ask-A-Librarian

Rowan-cabarrus student or faculty need help  click the chat now icon.

This service is only for Rowan-Cabarrus Community College faculty, staff, and students.

APA Citations and YouTube

• Want to Watch a Video for the Presentation? click here This is an 8-minute video, created in Canva, to explain how to cite YouTube videos in APA format.
• APA Citation Help This is the page from this research guide about all things APA.
• Plagiarism and Integrating Sources This is the page from this guide for plagiarism and integrating sources.
• APA Citations and YouTube *****Open this file to view a slideshow about citing YouTube videos in APA format.*****

APA7 Citation Guides and Templates

• APA 7 Career Paper for Student Use - Sept2020
• APA7 Style Guide (updated May 2022)

A few things about APA formatting

             Fonts

• APA7 allows for fonts other than Times New Roman 12 BUT our English and ACA departments have chosen to only allow Times New Roman 12. Other allowed fonts (for classes outside of English/ACA) are 11-point Calibri, 11-point Arial, or 10-point Lucida Sans Unicode. 11-point Georgia or 10-point Computer Modern are also allowed (APA Publication Manual, 2019, p. 44)

            Spacing

• Double-space always

            Headings

• Rowan-Cabarrus generally doesn't use headings other than Level 1 (centered and bold) and Level 2 (left aligned and bold)
• The heading is page number, right-aligned.
• No running headers are required for student papers

        Abstracts

• Abstracts are only necessary when the instructor asks for them.

          APA Versions  

• There are two versions of APA on the web. Be careful
• Within APA7, there are two versions (professional and student). Be careful.

        Authors/ Publishers

• Three or more authors can be abbreviated to Fist author, et al. on the first in-text citation
• Up to 20 authors are spelled out in the reference list
• Publisher location is not required for books 
• No Date - If no date is provided, use the initials n.d. where you would normally put the date.

        DOIs vs URLs

 If  a book, journal, report or other publication has a DOI, it must be included in the reference .

• If no DOI is available, use the source’s URL in the citation.

According to Purdue Owl's page titled DOIs vs. URLs , APA allows for the use of either the modern alphanumeric string format ("doi:0000000/000000000000") or the older doi.org format ("https://doi.org/10.0000/0000 ").  Use whichever is provided by the source. 

All hyperlinks retain the https://

APA, Long Paraphrase and Narrative and Parenthetical Intext Citations

There are two ways to cite your information in your paper.

If you include all the information about your source in the parenthesis at the end of the sentence, it is called a parenthetical intext citation. Page numbers are added for direct quotes.

• In conclusion, the research shows that dogs have innate predatory behavior traits which are enhanced by the dogs' desires to protect their human owners (Tucker & Maddey, 2020).     
• The research found that "dogs are more willing to attack or defend territory that is considered to be their own" (Tucker & Maddey, 2020, p. 81). 

If you include the author's name in the sentence, it is called a narrative intext citation. The date would go in the parenthesis. 

• Tucker and Maddey (2020) found that predatory behavior in dogs is due to many different factors.   

Often, long paraphrases continue for multiple sentences. Usually you'll intext cite the source in the first sentence. It is not necessary to cite every single sentence  IF  you've made it clear in the narrative that the information discussed is from the before-mentioned source. 

*Note that the bold words show where the information is coming from . Students should NOT bold any words.

Here's an example:

Tucker and Maddey (2020)  found that predatory behavior in dogs is due to many different factors. One of the factors is the physical territory of the alleged threat. The research found that "dogs are more willing to attack or defend territory that is considered to be their own"  (Tucker & Maddey, 2020, p. 81) .  Another factor  they discovered is that dogs are more willing to prey on a threat if their human owners are nearby. In an experiment conducted over multiple days using cameras,  Tucker and Maddey  discovered that dogs were shown to be much more protective with predatory behavior when their owners were in the vicinity than when the owners were away. In conclusion, the research shows that dogs have innate predatory behavior traits which are enhanced by the dogs' desires to protect their human owners  (Tucker & Maddey, 2020) .   

Citing a Source Within a Source (Secondhand or Secondary Source)

Academic articles, books, and other sources often refer to previously published articles, books and other sources. You'll usually see the author of the previous source in the sentence or in the intext citation.

You will NOT include this source as if you read the study yourself.

For example, there is a paper written by Anderson that is referred to in an article written by Robb. You read the article by Robb; NOT the paper by Anderson. This is what you write:

According to Anderson's 2013 study (as cited in Robb, 2019), learning APA "can be difficult, especially when students are focusing on content area and not writing styles" (p. 33). In addition, some elements of APA seem subjective to students (Anderson, 2013 as cited in Robb, 2019).

In the reference list, you include the article you read; not the article you read about.

Robb, L. (2019). Librarianship in community colleges. Journal of Libraries , 110(2), 31-35. https://doil.something/something/000000. 

Direct Quotes

• Examples - APA Block, Long Direct Quotes

Guidelines for Direct Quotes

A direct quote uses the exact words of a source. .

Think of the quote as a rare and precious jewel. 

Quotes can be super-effective in getting your point across to the reader. Just be sure you’re not stringing a bunch of quotes together – you want your voice to be stronger than the voice of your sources. You always need to interpret, analyze, add to and explain more about the quote to your reader.  

Here are some guidelines to help you decide when to use quotes:

• Wording that is so memorable, unforgettable or powerful, or expresses a point so perfectly, that you cannot change it without weakening the meaning.
• An important passage is so dense or rich that it requires you to analyze it closely. This requires that the passage be quoted so the reader can follow your analysis.
• A claim you are making is such that the doubting reader will want to hear exactly what the source said. This is mostly when you criticize or disagree with a source. You want your reader to know you aren't misrepresenting the source.
• Your attempts to paraphrase or summarize are awkward or much longer than the source material.

You may choose to quote an entire passage from a source or just words or phrases. Make sure to use signal words (see below) to move between your ideas and the words of your source and avoid wordy or awkward introductions to a quote. Also, always cite your work. See examples below for ideas on how to use quotes.

Direct Quotes (APA format)

As Ali Akbar Hamemi remarked in 2005, "There is no doubt that America is a super-power in the world and we cannot ignore them" (Vick, 2017, p. 13). 

Direct Quotes (MLA format)

For Charles Dickens, the eighteenth century was both "the best of times" and "the worst of times" (35).

Sometimes it may be necessary to include long direct quotes ( of over 40 words) if you are unable to paraphrase or summarize. A long quote is treated differently as a block quotation with a .5 inch margin from the left but still double-spaced.  Notice that there are no quotation marks around the block quotations even though these are direct quotes.  

Block quotation with parenthetical citation:

Researchers found when studying gray wolves that coloring around eyes may change over the lifespan:

Facial color patterns change with growth in many American  canid  species, although no studies have directly examined such developmental changes. For example, all newborn gray wolves observed in the present study had dark-colored bodies and C-type faces with dark-colored irises. (Ueda et al., 2014, p. 4)

Ueda, S., Kumagai, G., Otaki, Y., Yamaguchi, S., & Kohshima, S. (2014). A comparison of facial color pattern and gazing behavior in canid species suggests gaze communication in gray wolves (canis lupus).  PLoS One,  9 (6) doi:https://dx.doi.org/10.1371/journal.pone.0098217

Block quotation with narrative citation:

Manning and Kaler (2011) describe the difficulties of using survey methods when observing owls:

Survey  methods with observers outside the vehicle were 3 times more likely to displace an owl than a single vehicle stop where observers remained inside the vehicle. Owls were displaced farther distances by all survey methods compared to control trials, but distances and time displaced did not differ among survey methods. (p. 526)

Manning, J. A., & Kaler, R. S. A. (2011). Effects of survey methods on burrowing owl behaviors.  Journal of Wildlife Management,  75 (3), 525-530. Retrieved from https://proxy154.nclive.org/login?url=https://search.proquest.com/docview/925615280?accountid=13601

For more information, see page 272 of the   Publication Manual of the American Psychological Association , 7 th  ed.

So, when using quotes:

• Always have a good reason  for using a direct quote. Otherwise, paraphrase or summarize.
• Do not allow quotes to speak for themselves . Your research paper is about communicating YOUR IDEAS.  Your research simply helps prove or support those ideas.
• Always make sure you  provide an analysis of the quote .  Show your readers that you understand how the quote relates to your ideas by analyzing its significance.
• Do not use quotes as padding . If quotes do not have adequate analysis, readers will feel that you don’t have a grasp on what that quote means, and they also might feel that you are using quotes as “filler” to take up space.
• Use no more than 2 direct quotes per paragraph .
• Carefully integrate quotations into your text so that they flow smoothly and clearly into the surrounding sentences. Use a signal phrase or signal verb, such as those in the following example:

As Thompson (2020) makes clear in his article, Youtube's  algorithms "can’t distinguish between true and false data,  except in the most crude way" (para. 5).  

Paraphrasing/Summarizing

• APA Long Paraphrase
• MLA Long Paraphrase

Guidelines for Paraphrasing and Summarizing

Think of Paraphrases and Summaries as your foundations

Paraphrase and summarize long passages where the main point is important to the point you are making, but the details are not . You should use paraphrasing and summarizing much more often than direct quotes. A good balance would be 75% paraphrasing and summarizing and 25% direct quotes.

Paraphrase:  You are paraphrasing when you take someone else’s words and rewrite them in your own words without altering the meaning or providing interpretation. Paraphrases are about the same length as the original. Always cite your paraphrase. Summarize: You are summarizing  when you condense the author's words or ideas without altering the meaning or providing interpretation using your own words -- basically, you’re presenting the original information in a nutshell. Always cite it.

Examples of Paraphrases

Introduce paraphrases clearly in your text, usually with a signal phrase that includes the author of the source. Here is original text and paraphrased text.

Volunteers feel more socially connected, they're less lonely, and suffer from depression less, studies show. Volunteering creates physical benefits too: Regular volunteers are less likely to develop  high blood pressure  and live longer, some studies show. (text is from "Dalai Lama: 5 Things to Keep in Mind for the Next Four Years" from CNN.com, written by Jen Christensen)

Paraphrased text in APA style:

Volunteering has psychological and physical benefits, according to studies. Along with being less depressed and lonely, volunteers also live longer and are less likely to have high blood pressure (Christensen, 2017).

Paraphrased text in MLA style:

Volunteering has psychological and physical benefits, according to studies. Along with being less depressed and lonely, volunteers also live longer and are less likely to have high blood pressure (Christensen).

Examples of Summaries

Summaries, too, need to be carefully integrated into your text.   Make sure to signal the reader that you are summarizing and include the correct citation.

Here is an example of a summary in APA format:

In Christensen's article, she explores Dalai Lama's advice to people who want to find happiness in an uncertain world. His Holiness believes that people should focus on developing compassion, letting go of anger, self-reflecting, helping others, and being playful like children (Christensen, 2017). 

Here's the summary in MLA format:

In Christensen's article, she explores Dalai Lama's advice to people who want to find happiness in an uncertain world. His Holiness believes that people should focus on developing compassion, letting go of anger, self-reflecting, helping others, and being playful like children (Christensen). 

Whenever you include summaries, paraphrases, or quotations in your own writing, it is important that you identify the sources of the material; even unintentional failure to cite material is plagiarism. Be especially careful with paraphrases and summaries, where there are no quotation marks to remind you that the material is not your own.

Often, long paraphrases continue for multiple sentences. Usually you'll intext cite the source in the first sentence. It is not necessary to cite every single sentence IF you've made it clear in the narrative that the information discussed is from the before-mentioned source.

*Note that the bold words show where the information is coming from . Students should NOT bold the words.

Tucker and Maddey (2020) found that predatory behavior in dogs is due to many different factors. One of the factors is the physical territory of the alleged threat. The research found that "dogs are more willing to attack or defend territory that is considered to be their own" (Tucker & Maddey, 2020, p. 81) . Another factor they discovered is that dogs are more willing to prey on a threat if their human owners are nearby. In an experiment conducted over multiple days using cameras, Tucker and Maddey  discovered that dogs were shown to be much more protective with predatory behavior when their owners were in the vicinity they when the owners were away. In conclusion, the research shows that dogs have innate predatory behavior traits which are enhanced by the dogs' desires to protect their human owners (Tucker & Maddey, 2020) .   

If you're using information from a source more than once in a row (with no other sources referred to in between), you can use a simplified in-text citation. The first time you use information from the source, use a full in-text citation. The second time, you only need to give the page number.

Cell biology is an area of science that focuses on the structure and function of cells (Smith 15). It revolves around the idea that the cell is a "fundamental unit of life" (17). Many important scientists have contributed to the evolution of cell biology. Mattias Jakob Schleiden and Theodor Schwann, for example, were scientists who formulated cell theory in 1838 (20). 

*Thank you to the Library at Columbia College for this example.

Reasons why you would want to paraphrase from a source:

• To change the organization of ideas for emphasis.  You may have to change the organization of ideas in the passages you pull from your sources so that you can emphasize the points  most related to your paper.  Be sure to restate in your own words, but don’t change the meaning.
• To simplify the material.  You may have to simplify complex arguments, sentences, or vocabulary.
• To clarify the material.  You may have rewrite to clarify technical passages or put specialized information into language your audience will be better able to understand.

Paraphrasing is a valuable skill because:

• It is better than quoting information from a passage that doesn't have memorable or important words or phrases
• It helps you control the temptation to quote too much
• It allows the writer to put the idea of a source into their own voice (but always cite it to show it is someone else's idea).

Tips on Summarizing:

A summary is a  condensed  version of someone else’s writing. Like paraphrasing, summarizing involves using your own words and writing style to express another author’s ideas. Unlike the paraphrase, which presents important details, the summary presents only the most important ideas of the passage. For example, you could summarize a book in a sentence, or in several paragraphs, depending on your writing situation and audience. You may use the summary often for the following reasons:

• To condense the material. You may have to condense or reduce the source material to pull out the  points that relate to your paper.
• To omit extras from the material. You may have to leave out extra information from the source material so you can focus on the author’s main points.
• To simplify the material.  You may have to simply the most important complex arguments, sentences or vocabulary in the source material.

When you decide to  summarize or paraphrase, avoid the following:

• keeping the same structure of ideas and/or sentence structure
• just changing some of the words
• adding your ideas into the summary - be faithful to the meaning of the source material.
• forgetting to cite your sources and use signal words.

Peanut Butter and Jelly - APA Style

Whenever you have a reference at the end of your paper, you need at least one intext citation to go with it. Every intext citation should point to a reference at the end of your paper.

References and Intext Citations Go Together Like Peanut Butter and Jelly.

Your intext citation contains the first word(s) of your reference and the date so the reader can find it easily . For example:

You write this in your paper: For optimal decomposition, experts believe you should aim for a carbon to nitrogen ratio of 30:1 ( Johnson, 2001 ).

This is in your Reference List:

  Johnson , L. (2001, February). Compost Happens: The Secret to Making Quick Gardener's Gold Instead of a Slow, Stinking Mess Requires, Like Everything Else, Balance.  Canadian Gardening , 12 (1), 28-33.

APA Reminders and Tips

• Few Pointers
• APA Reminders
• How to Find Journal Title and Date
• Additional APA Information
• Video about formatting, hanging indent, double-space
• APA font should be Times New Roman, 12 point font.
• References should be double-spaced.
• The title of your article is in title case (only first word and proper nouns capitalized). 
• Make sure your URL leads to the correct document. Copy and paste it into a tab to double-check. 
• Don't forget your hanging indents.

Please also note that in PubMed they show the AMA Approved Abbreviations for journal names instead of the full journal name sometimes. Make sure you are using the full journal name. 

Check out the APA 7 Page to learn more about APA and see example citations. 

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